Cognitive Science

Why model?

I came across this little paper on the Introduction to Dynamical Systems and Chaos online course from Santa Fe. It was provided as a supplementary reading in the ‘Modelling’ section. The paper lays out some of the most enduring misconceptions about building models.

“The modeling enterprise extends as far back as Archimedes; and so does its misunderstanding.” Epstein (2008)

So, why model? What are models? And who are modellers?

Prior to reading this paper, my short answers to these questions would have been in accordance with the widely held misconceptions that:

We model to explain and or predict. Models are formal representations (often mathematical) of phenomenon or processes. And a modeller is someone who builds these explicit formal mathematical models. However, Epstein explains:

“Anyone who ventures a projection, or imagines how a social dynamic—an epidemic, war, or migration—would unfold is running some model.”

I like the idea that we all run some implicit models all the time. In the social and political sphere, where it is extremely difficult to operationalize and specify variables, this perspective gives implicit modelling such as drawing dynamical analogies, its due importance.

The paper lays out 16 reasons other than prediction for building models. And the idea that prediction and explanation aren’t the only modelling goals was revelation to me given that I’ve had a love hate relationship with modelling in the past. I am attracted to models, specially those with dynamical systems inclination but the overall tendency towards prediction as a goal often frustrates me. Just to clarify, prediction is a fine goal but my objection arise when 1) we’re deluded into thinking that models give us the tools to predict specific individual behaviours and 2) we can model a phenomenon, especially human behaviour, without first understanding it.

ML

xkcd: Machine Learning

Let me elaborate further in the context of automated predictive system that are currently trending (at least, within my academic circle) and often preoccupy my thinking. Claims to predict “criminal” and “risky” behaviour are examples from last week’s headlines: UK police wants Artificial Intelligence (AI) to predict criminal behaviour before it happens and Predictim, a commercial data analytics firm, claims its AI can flag “risky” babysitters. Unfortunately, these are not the outrageous exceptions but the general direction where things in the digital surveillance sphere seem to be heading.

Behaviours such as “criminal” or “risky” are very complex adaptive behaviours which are a result of infinite ongoing factors, which we can never fully specify in the first place. This makes it impossible to predict criminal behaviour with certainty. Juarrero reminds us why it is impossible to predict human behaviour with precision:

“When we are dealing with complex adaptive systems, surprises are unavoidable. Because of their sensitivity to initial conditions – due, in turn, to their contextual and temporal embeddedness – complex adaptive systems are characterized by unusual twists and novel turns. Since we will never be able to specify any dynamical system’s initial conditions to the requisite (infinite) degree, a fortiori we will never be able to capture all the details and circumstances of anyone’s life and background. Given this limitation, we must always keep in mind that reconstructing specific instances of behavior will always be, at best, an interpretation and not a deduction – a much more fallible type of explanation than we had previously hoped was available. Interpretations of human action are always tentative. Absolute certainty about either what the agent just did, or what he or she will do – specifically – a year from now, is therefore impossible.” (Juarrero 1999, p. 225)

These claims to predict “criminal” or “risky” behaviour are more than a mere misunderstanding of human nature or simple illusions about what AI tools are capable of doing. As these tools are being implemented into the social world, they have grave consequences on people’s lives. When claiming to predict someone’s potential criminality, errors are inevitable. The stakes are high when we get things wrong. Unsurprisingly, it is often society’s most vulnerable, those who are disfranchised, that pay a high price. Indeed, such models are used to further punish and disfranchise those that fall prey to these models.

A slightly different but interrelated issue with modelling to predict is that the strive to predict and explain often ignores the value of describing and/or observing to gain deep understanding. Sure, describing to understand, and explaining and predicting aren’t mutually exclusive. However, in reality, we seem to have blindly adopted prediction and generalization as primary goals of science. Studying to describe and understand, as a result, are undervalued. What is the point of describing? you might ask. I think it is fundamental to understand any phenomena or process as deeply and comprehensibly as possible before we can attempt to explain or predict it, and description is key to gaining such understanding.

I’ll leave you with an insightful Geertz (1973) passage from The Interpretation of Cultures:

“… I have never been impressed with claims that structural linguistics, computer engineering or some other advanced form of thought is going to enable us to understand men without knowing them.”

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How to prepare a talk on AI

How would you give a talk on Artificial Intelligence (AI) to 120 students between the age of 16-18, not all of whom are necessarily interested or have a background in science? How would you define AI? What would you include (and exclude)? What is the best way to structure it? Well, surely, there are many valid answers to these questions. It was the first time that myself and Elayne Ruane, a colleague who is also a PhD researcher, attempted to give an 80 minute talk to a big crowd of such students. We didn’t find much in terms of guidance or advice on how to interact with the students or how to frame the AI discourse in a suitable manner for students who are about to embark on their college journey. We wanted to convey the excitement, hope and potential the field holds while also portraying a realistic image of its current state. Hopefully sharing our general approach might be helpful to anybody who finds themselves in a similar situation.

Mind, what worked for us might not work in different contexts, mindsets, situations, or for a different topic. AI is one of the most over-hyped and misunderstood areas of research in the minds of the general public. Furthermore, AI has been somewhat associated with a certain stereotypical archetype in the media – a white male genius computer geek. How one introduces the field and the kind of work and influential figures one includes plays a subtle but important role towards challenging these misconceptions and stereotypes. Specifically, when addressing a crowd of young people in the midst of deciding what areas of study they will pursue at university, how you present the field of AI can send implicit signals about who is welcome. For us, this is everyone.

Initial Discussion

We began our talk with a brief discussion of what a computer science degree, as one of the routes to AI research, entails (within the context of our own department at University College Dublin) and the kinds of careers that it can lead to while raising the point that there isn’t one path to follow. We then briefly talked about exemplar AI projects that are taking place within our own School. We kept this part of the talk very interactive by frequently polling the group by way of raising their hands. This was important in keeping the students engaged.

What is AI?

We discussed the general definition of AI – the common view that artificial intelligence refers to a machine that simulates human intelligence. What it means to ‘simulate’ or ‘human intelligence’ are contested and of course far from settled. However, we felt it was important to keep it simple for the purpose of this talk. ‘Machines that simulate human intelligence and exhibit human behaviour’, often comes down to abilities such as learning, problem solving, reasoning, language processing and the like.

Unlike other disciplines such as physics or biology, Artificial Intelligence is not a clearly defined and well contained discipline but rather a very broad and cross disciplinary endeavour. It draws from mathematics, engineering, biology, neuroscience, linguistics, philosophy, and many more. Although the most direct route to studying AI is through computer science (certainly within the context of UCD), one can also get to AI through other routes. Besides, AI can be synthesized with any field of enquiry, including, neuroscience, music and art. Christie’s recent AI generated art is a good example.

AI is a wide umbrella term with sub-fields including robotics, natural language processing, computer vision, machine learning and deep learning, speech recognition, machine translation and more. We tried to use examples of these relevant to the students including Google Translate, Amazon’s Alexa, PS4 games, Minecraft, facial recognition tools, and robots. We showed them the famous video of Boston Dynamic’s robot, Spot, dancing to Uptown Funk which was a huge hit.

The History of AI

Ada_Lovelace_portraitAI is often thought of as a recent development, or worse, as futuristic, something that will happen in the far future. We tend to forget that dreams, aspirations and fascinations with AI go back in history back to antiquity. In this regard, Rene Descartes’s simulacrum and the Mechanical Turk are good examples. Descartes was fond of automata and had a walking and talking clockwork named after his daughter Francine. The machine apparently simulated his daughter, who died of scarlet fever at the age of 5. Similarly, the 18c Hungarian author and inventor Wolfgang von Kempelen created the Mechanical Turk, (a fake) chess-playing and speaking machine to impress the Empress Maria Theresa of Austria.

We can list an endless number of scholars who contributed to the development of AI as it is conceived today. The main towering figures we included were:

  • Al KhawarizmiThe ninth century Persian mathematician Muḥammad ibn Mūsā al-Khwārizmī who gave us one of the earliest mathematical algorithms. The word “algorithm” comes from mispronunciation of his name.
  • The English mathematician, Ada Lovelace who is often regarded as the first computer programmer.
  • Alan Turing who is regarded as the father of theoretical computer science and whom most students seemed to be already aware of.
  • And more recently, and perhaps scholars most influential in shaping the way we currently understand AI, are Marvin Minsky, John McCarthy, and Margaret Masterman.

Fun Game

We tried to make our talk as interactive as possible. We had questions and discussion points throughout. Towards the end, we had a game where students had to guess whether the AI being described on each slide was ‘sci-fi’ or ‘real’. Here are the main examples. Have a go yourself. 🙂

Sci-fi or real

  • Self-aware robots

Self aware robot

  • Face recognition AI which rates people’s ‘trustworthiness’

Trustworthiness AI

  • A bedside light that notifies you of your retweets

Light notifying RT

  • Automated confession systems

eConfession

Common Misconceptions

If there is anything that the AI narrative is not short of, it’s hype and misconception. Clarifications, in a subtle way, both help illustrate what the actual current state of the field is as well as highlighting the challenges that arise with it. As such, the final concluding remakes were highlighting the misconceptions surrounding AI and the ethical concerns that necessarily arise with any technological advancement. The major misconceptions we mentioned are:

  1. AI is a distant reality. The fact is far from it. AI is deeply embedded in the infrastructure of everyday life. It is invisible and ubiquitous.
  2. AI equals robots or ‘self-driving’ cars. As it would have been obvious by now, robotics is simply one dimension.
  3. AI is neutral and can’t be biased. This again is far from reality. As AI integrates deeper into the educational, medical, legal, and other social spheres, ethical questions inevitably arise. Questions of ethics, fairness, and responsibility are inherently questions of AI.

That concludes the content of the talk.

General advice:

  1. Keep it open and flexible. Create opportunities to hear from them. This allows you to get an idea of their awareness and knowledge (which can then help you calibrate on the fly in terms of technical detail) while also keeping them engaged.
  2. Pictures, more picture, and videos, are a great way to open up discussion. We showed a video of Google Assistant making a phone call which really captured their attention and got them talking. This also brought forth some ethical discussion.
  3. Prepare for plenty of questions around “Is AI going to take over?” and “How scared and worried should we be?”. It’s important to highlight how AI advancements can be misused but the trick is to highlight how much of what is reported on AI is overly blown hype which contributes to these unnecessary and unrealistic fears of AI when in fact much of the development in AI remains still premature. On the other hand, remember, we were talking to young science students about to embark to college. We still want to encourage them and want them to feel the dreams, excitements and hopes that have been the driving force of AI, at least in the 50s and 60s and the promising potentials that AI presents in medicine, robotics and more.

 

Further reading

 

 

The AI side of cognitive science is concerned with first world problems

I recently had the opportunity to attend a multidisciplinary conference where cognitive scientists, philosophers, psychologists, artificial intelligence (AI) researchers, neuroscientists and physicists came together to discuss the self. The conference was, generally speaking, well organized and most of the talks were interesting. The theme of the conference was on the openness of the self which means that contrary to the traditional essentialist view of self as fixed, fully autonomous and self-contained, the consensus, among the attendees, was that the self is not a static, discrete entity that exists independent of others but dynamic, changing, co-dependent, and intertwined with others. This intertwinement would furthermore extend to social and political forces that play crucial roles into constituting who we are. In this vein, any discussion of self and technology needs to acknowledge the entanglement of social and political factors and the necessity for diverse input and perspectives.

AI is a very broad field of enquiry which includes, to mention but a few, facial recognition technologies, search engines (such as Google), online assistants (such as Siri), and algorithms which are used in almost every sphere (medical, financial, judicial, and so on) of society. Unfortunately, the view of AI that seems to dominate public as well as academic discourses is a narrow and one-dimensional one where the concern revolves around the question of artificially intelligent “autonomous” entities. This view is unsurprisingly often promoted by a one-dimensional group of people; white, middle-class and male. Questions outside “the creation of artificial AI” rarely enter the equation. The social, political, and economical factors rarely feature in the cognitive science and interdisciplinary formulations of selfhood and technology — as if any technological development emerges in a social, political and economical vacuum. And the conference I attended was no different.

This was apparent during theme-based group discussions at this conference where one group discussed issues regarding self and technology. The discussion was led by researchers in embodied AI and robotics. The questions revolved around the possibility of creating an artificial self, robots, whether AI can be sentient and if so how might we know it. As usual, the preoccupation with abstract concerns and theoretical construction took centre stage, to  the detriment of the political and social issues. Attempts to direct some attention towards the social and political issues were dismissed as irrelevant.

It is easy to see the appeal of getting preoccupied in these abstract philosophical questions. After all, we immediately think of “I, Robot” type of robots when we think of AI and we think of “self-driving” cars when we think of ethical questions in AI.

game and gambling, gaming machines, chess playing Turk, design by Wolfgang von Kempelen (1734 - 1804), built by Christoph Mechel

A 1980s Turk reconstruction

The fascination and preoccupation for autonomous and discrete machines is not new to current pop-culture. The French philosopher René Descartes had a walking and talking clockwork named after his daughter Francine. The machine apparently simulated his daughter Francine, who died of scarlet fever at the age of five. The 18c Hungarian author and inventor Wolfgang von Kempelen created the Mechanical Turk, (a fake) chess-playing and speaking machine to impress the Empress Maria Theresa of Austria.

It is not surprising that our perception of AI is dominated by such issues given that our Sci-Fi pop culture plays an influential role towards our perception of AI. The same culture feeds on overhype and exaggeration of the state of AI. The researchers themselves are also often as responsible for miscommunication and misunderstanding about the state of the art of the filed. And the more hyped a piece of work is, the more attention it is given – look no further than the narrative surrounding Sophia – an excessively anthropomorphized and overhyped machine.

Having said that, the problem goes further than misleading coverage and overhype. The overhype, the narrow one-dimension view of AI as concerned with question of artificial self and “self-driving” cars, detracts from nuanced and most important and more pressing issues in AI that impact the very poor, disfranchised, socially, economically disadvantaged. For example, in the current data economy, insurance systems reward and offer discounts for those that are willing to be tracked and provide as much information about their activities and behaviours. Consumers who want to withhold all but the essential information from their insurers will pay a premium. Privacy, increasingly, will come at a premium cost only the privileged can afford.

An implicit assumption that AI is some sort of autonomous, discrete entity separate from humans, and not a disruptive force for society or the economy, underlies this narrow one-dimensional view of AI and the preoccupation with the creation of artificial self. Sure, if your idea of AI revolves around sentient robots, that might bear some truth. This implicit assumption seems, to me, a hangover from Cartesian dichotomous thinking that remains persistent even among scholars within the embodied and enactive tradition who think that their perspectives account for complex reality. This AI vs humans thinking is misleading and unhelpful, to say the least.

AI systems are ubiquitous and this fact is apparent if you abandon the narrow and one-dimensional view of AI. AI algorithms are inextricably intertwined with our social, legal, health and educational system and not some separate independent entities as we like to envision when we think of AI. The apps that power your smart phone, the automated systems, including those that contribute to the decision towards whether you get a loan or not, whether you are hired or not, or how much your car insurance premium will cost you all are AI. AI that have real impact, especially on society’s most vulnerable.

Yet, most people working on AI (both in academia and Silicon Valley) are unwilling to get their hands dirty with any aspect of the social, economic or political aspect and impact of AI. The field seems, to a great extent, to be constituted of those who are socially, economically and racially privileged where these issues bear no personal consequences. The AI side of cognitive science is no different with its concerns of first world problems.  Any discussion of a person or even society is devoid of gender, class, race, ability and so on. When scholars in these fields speak of “we”, they are barely inclusive of those that are outside the status quo which is mostly a white, male, Western, middle-class educated person. If your model of self is such, how could you and why would you be concerned about the class, economic, race and gender issues that emerge due to unethical application of AI, right? After all, you are unlikely to be affected.  Not only is the model of self unrepresentative of society, there barely is awareness of the issue as a problem in the first place. The problem is invisible due to privilege which renders diversity and inclusivity of perspectives as irrelevant.

This is not by any means a generalization of everyone within the AI scholarship. There are, of course, plenty of people who acknowledge the political and social forces as part of issues to be concerned about within the discussion of AI. Unsurprisingly, such important work in this regard is done by people of colour and women who unfortunately, remain a minority. And the field as a whole would do well to make sure that it is inclusive of such voices, and to value their input instead of dismissing them.

The in(human) gaze and robotic carers

Google search “robot carers” and you’ll find extremely hyped up articles and think pieces on either how robot carers are just another way of dying even more miserably or how robot carers are saving the elderly from the lives of loneliness and nothing much in between. Not much nuance. Neither is right, of course. Robot carers shouldn’t be dismissed at first hand as the end of human connections and neither should they be overhyped as the flawless substitutes for human care.

I think they can be useful and practical and even preferable to a human carer in some cases while they cannot (and most likely never will) substitute human care and connection in other aspects. The human gaze is my reason for thinking that.

But first let me say a little about the Inhuman Gaze conference, which provoked me to think about robot care givers. The conference took place last week (6th – 9th June) in Paris. It was a diverse and multidisciplinary conference that brought together philosophers, neuroscientists, psychiatrists (scholars and practitioners alike) with the common theme of the inhuman gaze. Over the four days, speakers presented their philosophical arguments, empirical studies and clinical case studies, each from their own perspective, what the human/inhuman gaze is and its implication for the sense of self. I, myself, presented my argument for why other’s gaze (human or otherwise) is a crucial constituent to “self”. I looked at solitary confinement as an example. In solitary confinement (complete isolation or significantly reduced intersubjective contact), prisoners suffer from negative physical and psychological effects including confusion, hallucination and gradual loss of sense of self. The longer (and more intense) the solitary confinement goes, the more the pronounced the negative effects, leading to gradual loss of sense of self.

The reason for gradual loss of self in the absence of contact with others, Bakhtin would insist, is that the self is dependent on others for its existence. The self is never a self-contained and self-sustaining entity. It simply cannot exist outside the web of relations with others. Self-narrative requires not only having something to narrate but also having someone to narrate it to. To be able to conceptualize my self as a meaningful whole, which is fundamental to self-individuation and self-understanding, I need an additional, external perspective – an other.  The coherent self is put under threat in solitary confinement as it is deprived of the “other”, which is imperative for its existence. The gaze of another, even when uncaring, is an affirmation of my existence.

So, what is an inhuman gaze? A gaze from non-human objects: like the gaze of a wall in a solitary confinement? The gaze of a CCTV camera (although there often is a human at the other end of a CCTV camera)? or a gaze from a human but one that is objectifying and dehumanizing? For example, the gaze from a physician who’s performing an illegal organ harvesting where the physician treats the body that she’s operating on like an inanimate object? Let’s assume an inhuman gaze is the gaze of non-human objects for now. Because the distinctiveness of the human gaze (sympathizing, caring, objectifying or humanizing) is important to the point that I am trying to make. The human gaze, unlike the inhuman gaze, is crucial to self-affirmation.

channel-4s-new-sci-fi-robot-series-humans

From Channel 4’s sci-fi robot series Humans

Robot caregivers and the human gaze…

Neither the extreme alarmist nor the uncritical enthusiast help elucidate the pitfalls and potential benefits of robot caregiving. Whether robotic caregiving is a revelation or a doom depends on the type of care one needs. Roughly speaking, we can categorize care that robots can provide into two general categories. First one is physical or mechanical care – for example., fetching medicine or changing elderly patients into incontinence wear. The second one, on the other hand is companionship (to elderly people or children) where the aim might be to provide emotional support.

Now, robotic care might be well suited for the physical or mechanical type of care. In fact, some people might prefer a robot dealing with such physical task as incontinence care or any similar task that they are no longer able to perform themselves. Such care, when provided by a human, might be embarrassing and humiliating for some people. Not only is the human gaze capable of deep understanding and sympathy but also the potential to humiliate and intimidate. The robotic gaze, on the other hand, having no intrinsic values, is not judgemental. So, in the case of physical and mechanical care, the absence of the human gaze does not necessarily result in a significant negative effect. In fact, it might be desirable when we are in a vulnerable position where we feel we might be humiliated.

On the contrary, if companionship and emotional support are the types of care that we are looking for, the value and judgement free robotic gaze will simply not do. We are profoundly social, dynamic and embodied beings who continually strive to attribute meaning and value to the world. If we are to ascribe an ‘essence of the human condition’, it is that that our being in the world is thoroughly interdependent with the existence of others and context where we continually move and negotiate between different positions. True companionship and emotional connection requires intrinsic recognition of emotions, suffering, happiness, and the like. A proper emotional and ethical relation to the other (and the acceptance of genuine responsibility) requires the presence of a loving and value-positing consciousness, and not a value-free, objectifying gaze.

True human companionship and emotional support cannot be programmed into a robot no matter how advanced our technologies can become, for companionship and emotional connection require sense-making and a value-positing consciousness. Sense-making is an active, dynamic and partly open undertaking – and therefore a never-ending process – not a matter of producing and perceiving mappings of reality which can then be codified into a software.  The human gaze affords mutual understanding of what being a human is like. Recognition of emotions, suffering, etc., requires recognition of otherness based on mutual understanding. The human gaze recognizes an ‘other’ human gaze. As Hans Jonas has put it succinctly in ‘The Phenomenon of Life’, “only life can know life … only humans can know happiness and unhappiness.” 

 

 

Afrofeminist epistemology and dialogism: a synthesis (work in progress)

Embodied, enactive and dialogical approaches to cognitive science radically depart from traditional Western thought in the manner with which they deal with life, mind and the person. The former can be characterised as emphasising interdependence, relationships, and connectedness with attempts to understanding organisms in their milieu. Acknowledgements of complexities and ambiguities of reality form the starting points for epistemological claims.  The latter, on the other hand, tends to strive for certainty and logical coherence in an attempt to establish stable and relatively fixed epistemological generalisations. Individuals, which often are perceived as independent discreet entities, are taken as the primary subjects of knowledge and the units of analysis.

Collins’s proposed black feminist epistemology, hereafter “Afrofeminist epistemology”, opposes the traditional Western approach to epistemology as well as the largely Positivist scientific view inherited from it.  As such, it is worth drawing attention to the similarities between Black feminist thought and dialogical approaches to the cognitive sciences. In what follows I seek to reveal a striking convergence of themes between these two schools of thought. In so doing, I intend to illustrate that the two traditions – cognitive sciences, especially the dialogical approach to epistemology, and Afrofeminist epistemology, particularly, the type proposed by Patricia Hill Collins (2002) – can inform one another through dialogue.

General characterization of classic Western approach to epistemology and the Cartesian inheritance

The classic Western approach to epistemology tends to be monological; meaning it tends to focus on individuals and their cognition and behaviour. When relationships and interactions enter the equation, individuals and their relations are often portrayed as distinct entities that can be neatly separated. Dichotomous thinking — subject versus object, emotion versus reason – persists within this tradition. Ethical and moral values and questions are often treated as clearly separable from “objective scientific work” and as something that the scientist need not contaminate her “objective” work with. In its desire for absolute rationality, Western thought wishes to cleave thought from emotion, cultural influence and ethical dimensions. Cognition, evaluation and emotions are treated as if they are entities that shouldn’t be contaminated. Abstract and intellectual thinking are regarded as the most trustworthy forms of understanding and rationality is fetishized.

In the classic Western epistemological tradition, abstract reasoning is taken to be the highest cognitive goal, and certainty as a necessary component for knowledge.  Since the ultimate goal is to arrive at timeless, universally applicable laws, establishing certainty is pivotal for laying the foundations. Although there are historical antecedents leading up to and contributing towards what is generally regarded as Western tradition – in particular, Plato in his dialogues Meno and Phaedo – Descartes represents the pinnacle of Western thought (Gardiner 1998, Toulmin 1992). The subject as autonomous and self-sustaining entity or a Cartesian cogito, which we have inherited from Cartesian thinking, remains prevalent in most current Western philosophy as well as in the background assumptions of the human sciences. The way the individual self is taken as the unquestioned origin of knowledge of the world and others is a legacy of this tradition (Linell 2009).

Black feminist criticism of dominant approach and the proposed alternative

Contrary to the classic Western epistemological tradition, in Afrofeminist epistemology ethical and moral values and questions are inseparable from our enquires into knowledge. Similarly, knowledge claims and knowledge validation processes are not independent of the interests and values of those who define what knowledge is, what is important and worthy of study, and what the criteria for epistemological justification are (Collins 2002). Such definitions and criteria are guarded fiercely by the institutions and individuals who act as the ‘gatekeepers’ of the classic Western epistemological tradition. This traditional Western epistemology, Collins points out, predominantly represents Western, elite, and white, male interests and values. In fact, a brief review of the history of Western philosophical canon reveals that knowledge production processes and the criteria for knowledge claims have predominantly been set by elite, white, Western men.

Scholars like Karen Warren (2009) have cogently argued that the history of classical Western philosophy has, for centuries, almost exclusively consisted of elite, white, Western European men giving the illusion that Western white men are the epitome of intellectual achievement. Women’s voices and perspectives were diminished, ignored, and systematically excluded from the canon. In her ‘recovery project’, Warren finds that women philosophers nonetheless have made important contributions throughout the history of philosophy and that you find them when you go looking for them. This, to a great extent, remains the case not only in philosophy, but also in much of the rest of the academic tradition. A brief look at any philosophy curricula would reveal that white European male philosophers and their views remain dominant and definitive.

Traditional approaches taken as the “normal” and “acceptable” ways to theorise and generalise about people’s lived experiences means that any other approaches to theorising about groups of people that are not aligned with canonical intellectual currents (often white European male) are dismissed as “anomalies”. For Collins, it is indisputable that different people experience reality differently and that all social thought somewhat reflects the realities and interests of its creators. Political criteria influence knowledge production and validation processes in one way or another.  Collins asserts, in studying Black women’s realities, the typical perspectives on offer have either identified black women with the oppressor, in which case Black women lack an independent interpretation of their own realities, or have characterised Black women as less human than the oppressor, in which case Black women lack the capacity to articulate their own standpoint. While in the first perspective independent Black women’s realities are seen as not their own, in the latter, it is seen as inferior. For that reason, the traditional epistemology is inadequate to capture and account for the lived experiences of black women – hence Collins’ proposal for an Afrocentric feminist epistemology which is grounded in black women’s values and lived experiences.

Black women’s lived experiences are different in important ways. The kind of relationships Black women have, and the kind of work they engage in are notable examples that demonstrate the differing realities and lived experiences. Intuitive knowledge, what Collins calls wisdom, is crucial to the everyday lives and survival of black women. While wisdom and intuitions, as opposed to abstract intellectualizing, might be excluded as irrelevant, and at best, less credible as far as the traditional epistemologies are concerned, they are highly valued within black communities:

“The distinction between knowledge and wisdom, and the use of experience as the cutting edge dividing them, has been key to Black women’s survival. … knowledge without wisdom is adequate for the powerful, but wisdom is essential to the survival of the subordinate.” (Collins 1989, p. 759)

The desire for complete objectivity and universally generalisable theories in the dominant Western tradition has led to a focus on abstract analysis of the nature of concepts like ‘knowledge’ and ‘justification’, with little to no grounding of complex lived experience. Its portrayal of reason and rationality in direct contrast with emotions – the former to arrive at pure, objective knowledge –  has led to dichotomous thinking, thus blinding us to continuities and complementarities. Consequently, “reason” has been privileged over emotions. This in turn has impeded emotional and bodily knowledge, what Foucault (1980) calls ‘subjugated knowledge’ often expressed through music, drama, etc., as less important. However, ‘subjugated knowledge’ is crucial and is part of a way of life and survival for black communities. Such knowledge, grounded in concrete experiences and recognised through connectedness, dialogues and relationships, is what is of real value for Black women.

That knowledge claims should be grounded in concrete, lived experience rather than abstract intellectualising is crucial to Collins’s Afrocentric feminist epistemology. Collins’s Afrocentric epistemology prioritizes wisdom over knowledge and has, at its core, black women’s experiences of race and gender oppression. Black women have shared experience of oppression, imperialism, colonialism, slavery, and apartheid as well as roots in the core African value system prior to colonization. The roots of Afrocentric epistemology can be traced back to African-based oral traditions. As such, dialogues occupy an important place. Dialogues, so far as the Afrocentric epistemology is concerned, are an essential method for assessing knowledge claims.

This Afrocentric epistemology, grounded in the lived experience of black women, that employs dialogues as a way of validating knowledge claims, stands in a stark contrast with that of the Eurocentric epistemology. Connectedness rather than separation is an essential component of the knowledge validation process. Individuals are not detached observers of stories or folktales, but rather active participants, listeners and speakers and part of the story. Dialogues explore and capture the fundamentally interactive connected nature of people and relationships.

Ethical claims lie at the heart of an Afrocentric feminist epistemology, in contrast to the classical Western epistemology that considers ethical issues as separate from and independent of ‘objective scientific investigations’. Afrocentric feminist epistemology is about employing emotions, wisdom, ethics and reason as interconnected and equally essential components in assessing knowledge claims with reference to a particular set of historical conditions.

Dialogical criticism to dominant approach and its alternative

The dialogical approach to cognitive science – inspired by Mikhail Mikhailovich Bakhtin’s (1895 – 1975) thinking and further developed by dialogists such as Per Linell (2009) – objects to the dominant Western epistemological approach. Dialogical theories which have roots in the Bakhtin Circle, a 20th century school of Russian thought, have had a massive influence on social theory, philosophy and psychology. At the centre of dialogical theories lies the view that linguistic production, the notion of self-hood, and knowledge are essentially dialogic. Dialogical approaches are concerned with conceptualizing and theorizing human-sense making and they do so based on a set of assumptions some of which stand in stark opposition to traditional Western philosophy and science. These assumptions include: individual selves cannot be assumed to exist as agents and thinkers before they begin to interact with others and the world; our sense-makings are not separable from our historical antecedents and current cultural and societal norms and value systems. The interrelation between self, others and the environment are there from the start in the infant’s life and the awareness of self and others co-develop over time; they are two sides of the same process. Classical Western philosophy and science has tried to reduce the world to rational individual subjects in attempt to establish stable universals. The origin of knowledge of the world and of others is the discreet individual person. So far as dialogical approaches go, most traditional Western epistemological approaches are rooted in Cartesian individualism and are monological – meaning, that they only encompass individuals and their cognition and environments. Groups and societies are nothing but ensembles of individuals:

“Individuals alone think, speak, carry responsibilities, and other individuals at most have a casual impact on their activities and stances.” (Linell 2009, p. 44)

Dialogism[1], in contrast, insists that interdependencies, co-dependencies, and relationships between the individual and the world are most fundamental components in understanding the nature of selves and furthermore, of knowledge. The term intersubjectivity captures this concept well:

“The term “intersubjectivity”—or what Hannah Arendt calls “the subjective in-between”—shifts our emphasis away from notions of the person, the self, or the subject as having a stable character and abiding essence, and invites us to explore the subtle negotiations and alterations of subjective experience as we interact with one another, intervocally or dialogically (in conversation or confrontation), intercorporeally (in dancing, moving, fighting, or competing), and introceptively (in getting what we call a sense of the other’s intentions, frame of mind, or worldview).”  (Jackson 2002, p. 5)

Cultures and societies are typically conceived as objective, stable structures so far as Western epistemologies go. Dialogism by contrast conceives cultures and societies as dynamic, living and partly open, with tensions, internal struggles and conflicts between majorities and minorities and different value systems. “Knowledge is necessarily constructed and continually negotiated (a) in situ and in sociocultural traditions, and (b) in dialogue with others; individuals are never completely autonomous1 as sense-makers.” (Linell 2009, p. 46) The individual is not a separate, discrete, fixed and stable entity that stands independent from others, but rather one that is always in dynamical interactions with and interdependent with others. Knowledge claims and knowledge validation processes need therefore to reflect these continual tensions and dynamic interactions.

Concluding remarks: drawing similarities between dialogical approaches and Afrofeminist epistemology

So, what are the implications, if any, of drawing these commonalities between Afrofeminist epistemology and dialogical approaches to epistemology, and their common refutation of traditional Western epistemology? Collins has described Afrofeminist and Western epistemological grounds as competing and at times irreconcilable:

“Those Black feminists who develop knowledge claims that both epistemologies can accommodate may have found a route to the elusive goal of generating so called objective generalizations that can stand as universal truths.”  (Collins 1989, p.773)  

The synthesis and incorporation of dialogism with Afrofeminist epistemology is, in a sense, not the discovery of that elusive finding into “objective generalization” or “universal truths” that satisfy both epistemologies. Rather such synthesis, I argue, is a means towards epistemological approaches that aspire to embed Afrofeminist values and dialogical epistemological underpinnings to our understandings of personhood and knowledge. Such epistemological approaches acknowledge that knowledge claims, knowledge validation processes and any scientific endeavours in general are value-laden and cannot be considered independent of underlying values and interests. A move towards epistemological approaches that acknowledge the role of the scientist/theorist which Barad (2007) captures concisely:

“A performative understanding of scientific practices, for example, takes account of the fact that knowing does not come from standing at a distance and representing but rather from a direct material engagement with the world.”   (Barad 2007, p. 49)

Connectedness and relationships rather than disinterested, disembodied, and detached Cartesian individuals form a central component of analysis. Great emphasis is placed on extensive dialogues and not to become a detached observer of stories. In so doing, individual expressiveness, emotions, the capacity for empathy and the fact that ideas cannot be divorced from those who create and share them need to be key factor for this epistemology. Such is an epistemological approach that aspires to embed Afrofeminist values and dialogical underpinnings.

Knowledge is specific to time and place and is not rooted in the individual person but in relationships between people. Individuals exist in a web of relations and co-dependently of one other, negotiating meanings and values through dialogues. As Bakhtin, pioneer of dialogism has emphasized, we are essentially dialogical beings, and it is only through dialogues with others that we come to realise and sustain a coherent – albeit continually changing –  sense of self. Reality is messy, ambiguous, and complex. Any epistemological approach that takes the person as fully autonomous, fixed, and a self-sufficient agent whose actions are guided by pure rationality fail to recognise the complexities and ambiguities of reality, time and context-bound nature of knowledge. At the core of this proposed Afrofeminist/dialogical approach to epistemology is an attempt to bring values as important constituent factor to the dialogical, intersubjective embodied, in a constant flux person and the epistemologies that drive from it.

[1] It is important to note that individuals do not disappear in dialogism, rather, the individual is a social being who is interdependent with others, “not an autonomous subject or a Cartesian cogito.” (Linell 2009)

Bibliography

Barad, K. (2007). Meeting the universe halfway: Quantum physics and the entanglement of matter and meaning. duke university Press.

Collins, P. H. (1989). The social construction of black feminist thought. Signs: Journal of Women in Culture and Society14(4), 745-773.

Collins, P. H. (2002). Black feminist thought: Knowledge, consciousness, and the politics of empowerment. Routledge.

Foucault, M. (1980). Language, counter-memory, practice: Selected essays and interviews. Cornell University Press.

Gardiner, M. (1998). The incomparable monster of solipsism: Bakhtin and Merleau-Ponty. Bakhtin and the human sciences. Sage, London, 128-144.

Jackson, M. (2012). Lifeworlds: Essays in existential anthropology. University of Chicago Press.

Linell, P. (2009). Rethinking language, mind, and world dialogically. IAP.

Toulmin, S. E., & Toulmin, S. (1992). Cosmopolis: The hidden agenda of modernity. University of Chicago Press.

Warren, K. (Ed.). (2009). An unconventional history of Western philosophy: conversations between men and women philosophers. Rowman & Littlefield.

 

The dark side of Big Data – how mathematical models increase inequality. My review of O’Neil’s book ‘WMD’

We live in the age of algorithms. Where the internet is, algorithms are. The Apps on our phones are results of algorithms. The GPS system can bring us from point A to point B thanks to algorithms. More and more decisions affecting our daily lives are handed over to automation. Whether we are applying for college, seeking jobs, or taking loans, mathematical models are increasingly involved with the decision makings. They pervade schools, the courts, the workplace, and even the voting process. We are continually ranked, categorized, and scored in hundreds of models, on the basis of our revealed preferences and patterns; as shoppers and couch potatoes, as patients and loan applicants, and very little of this do we see – even in applications that we happily sign up for.

More and more decisions being handed over to algorithms should in theory mean less human biases and prejudices. Algorithms are, after all, “neutral” and “objective”. They apply the same rules to everybody regardless of race, gender, ethnicity or ability. However, this couldn’t be far from the truth. In fact, mathematical models can be, and in some cases have been, tools that further inequality and unfairness. O’Neil calls these kinds of models Weapons of Math Destruction (WMD). WMDs are biased, unfair and ubiquitous. They encode poisonous prejudices from past records and work against society’s most vulnerable such as racial and ethnic minorities, low-wage workers, and women. It is as if these models were designed expressly to punish and to keep them down. As the world of data continues to expand, each of us producing ever-growing streams of updates about our lives, so do prejudice and unfairness.

Mathematical models have revolutionized the world and efficiency is their hallmark and sure, they aren’t just tools that create and distribute bias, unfairness and inequality. In fact, models, by their nature are neither good nor bad, neither fair nor unfair, neither moral nor immoral – they simply are tools. The sports domain is a good example where mathematical models are a force for good. For some of the world’s most competitive baseball teams today, competitive advantages and wins depend on mathematical models. Managers make decisions that sometimes involve moving players across the field based on analysis of historical data and current situation and calculate the positioning that is associated with the highest probability of success.

There are crucial differences, however, between models such as those used by baseball managers and WMDs.  While the former is transparent, and constantly updates its model with feedbacks, the latter by contrast are opaque and inscrutable black-boxes. Furthermore, while the baseball analytics engines manage individuals, each one potentially worth millions of dollars, companies hiring minimum wage workers, by contrast, are managing herds. Their objectives are optimizing profits so they slash their expenses by replacing human resources professionals with automated systems that filter large populations into manageable groups. Unlike the baseball models, these companies have little reason – say plummeting productivity – to tweak their filtering model.  O’Neil’s primary focus in the book are models that are opaque and inscrutable, those used within powerful institutions and industries, which create and perpetuate inequalities – WMDs – “The dark side of Big Data”! 

Weapons-of-math-destructionThe book contains crucial insights (or haunting warnings, depending on how you choose to approach it) to the catastrophic directions mathematical models used in the social sphere are heading. And it couldn’t come from a more credible and experienced expert than a Harvard mathematician who then went to work as quant for D. E. Shaw, a leading hedge fund, and an experienced data scientist, among other things.

One of the most persistent themes of O’Neil’s book is that the central objectives of a given model are crucial. In fact, objectives determine whether a model becomes a tool that helps the vulnerable or one that is used to punish them. WMDs objectives are often to optimize efficiency and profit, not justice. This, of course, is the nature of capitalism. And WMDs efficiency comes at the cost of fairness – they become biased, unfair, and dangerous. The destructive loop goes around and around and in the process, models become more and more unfair.

Legal traditions lean strongly towards fairness … WMDs, by contrast, tend to favour efficiency. By their very nature, they feed on data that can be measured and counted. But fairness is squishy and hard to quantify. It is a concept. And computers, for all their advances in language and logic, still struggle mightily with concepts. They “understand” beauty only as a word associated with the Grand Canyon, ocean sunsets, and grooming tips in Vogue magazine. They try in vain to measure “friendship” by counting likes and connections on Facebook. And the concept of fairness utterly escapes them. Programmers don’t know how to code for it, and few of their bosses ask them too. So fairness isn’t calculated into WMDs and the result is massive, industrial production of unfairness. If you think of a WMD as a factory, unfairness is the black stuff belching out of the smoke stacks. It’s an emission, a toxic one. [94-5]

The prison system is a startling example where WMDs are increasingly used to further reinforce structural inequalities and prejudicesIn the US, for example, those imprisoned are disproportionately poor and of colour. Being a black male in the US makes you nearly seven times more likely to be imprisoned than if you were a white male. Are such convictions fair? Many different lines of evidence suggest otherwise. Black people are arrested more often, judged guilty more often, treated more harshly by correctional officers, and serve longer sentences than white people who have committed the same crime. Black imprisonment rate for drug offenses, for example, is 5.8 times higher than it is for whites, despite a roughly comparable prevalence of drug use.

Prison systems which are awash in data hardly carry out important research such as why non-white prisoners from poor neighbourhoods are more likely to commit crimes or what the alternative ways of looking at the same data are. Instead, they use data to justify the workings of the system and further punish those that are already at a disadvantage. Questioning the workings of the system or enquiries on how the prison system could be improved are almost never considered. If, for example, building trust were the objective, an arrest may well become the last resort, not the first. Trust, like fairness, O’Neil explains, is hard to quantify and presents a great challenge to modellers even when the intentions are there to consider such concept as part of the objective.

Sadly, it’s far simpler to keep counting arrests, to build models that assume we’re birds of a feather and treat us such… Innocent people surrounded by criminals get treated badly. And criminals surrounded by law-abiding public get a pass. And because of the strong correlation between poverty and reported crime, the poor continue to get caught up in these digital dragnets. The rest of us barely have to think about them. [104]

Insofar as these models rely on barely tested insights, they are in a sense not that different to phrenology – digital phrenology. Phrenology, the practice of using outer appearance to infer inner character, which in the past justified slavery and genocide has been outlawed and is considered pseudoscience today. However, phrenology and scientific racism are entering a new era with the appearance of justified “objectivity” with machine-learned models. “Scientific” criminological approaches now claim to “produce evidence for the validity of automated face-induced inference on criminality. However, what these machine-learned “criminal judgements” pick up on, more than anything, is systematic unfairness and human bias embedded in historical data.  

model that profiles us by our circumstances helps create the environment that justifies its assumptions. The stream of data we produce serve as insights into our lives and behaviours. Instead of testing whether these insights stand up to scientific scrutiny, the data we produce are used to justify the modellers’ assumptions and to reinforce per-existing prejudice. And the feedback loop goes on.

When I consider the sloppy and self-serving ways that companies use data, I am often reminded of phrenology… Phrenology was a model that relied on pseudo-scientific nonsense to make authoritative pronouncements, and for decades it went untested. Big Data can fall into the same trap. [121-2]

Hoffman in 1896 published a 330-page report where he used exhaustive statistics to support a claim as pseudo-scientific and dangerous as phrenology. He made the case that the lives of black Americans were so precarious that the entire race was uninsurable. However, not only were Hoffman’s statistics erroneously flawed, like many of WMDs O’Neil discusses throughout the book, he also confused causation for correlation. The voluminous data he gathered served only to confirm his thesis: race is a powerful predictor of life expectancy. Furthermore, Hoffman failed to separate the “Black” population into different geographical, social or economic cohorts blindly assuming that the whole “Black” population is a homogeneous group. 

This cruel industry has now been outlawed. Nonetheless, the unfair and discriminatory practices remain and are still practiced but in a far subtler form –  they are now coded into the latest generations of WMDs and obfuscated under complex mathematics. Like Hoffman, the creators of these new models confuse correlation with causation and they punish the struggling classes and racial and ethnic minorities. And they back up their analysis with realms of statistics, which give them the studied air of “objective science”. 

What is even more frightening is that as oceans of behavioural data continue to feed straight into artificial intelligence systems, this, to the most part will, unfortunately, remain a black box to the human eye. We will rarely learn about the classes that we have been categorized into or why we were put there, and, unfortunately, these opaque models are as much a black-box to those who design them. In any case, many companies would go out of their way to hide the results of their models, and even their existence.

In the era of machine intelligence, most of the variables will remain a mystery... automatic programs will increasingly determine how we are treated by other machines, the ones that choose the ads we see, set prices for us, line us up for a dermatologist appointment, or map our routes. They will be highly efficient, seemingly arbitrary, and utterly unaccountable. No one will understand their logic or be able to explain it. If we don’t wrest back a measure of control, these future WMDs will feel mysterious and powerful. They’ll have their way with us, and we’ll barely know it is happening. [173]

In the current insurance system, (at least as far as the US is concerned) the auto insurers’ tracking systems which provide insurers with more information enabling them to create more powerful predictions, are opt-in. Only those willing to be tracked have to turn on their black boxes. Those that do turn them on get rewarded with discounts where the rest subsidize those discounts with higher rates. Insurers who squeeze out the most intelligence from this information, turning it into profits, will come out on top. This, unfortunately, undermines the whole idea of collectivization of risk on which insurance systems are based. The more insurers benefit from such data, the more of it they demand, gradually making trackers the norm. Consumers who want to withhold all but the essential information from their insurers will pay a premium. Privacy, increasingly, will come at a premium cost. A recently approved US bill illustrates just that. This bill would expand the reach of “Wellness Programs” to include genetic screening of employees and their dependents and increase the financial penalties for those who choose not to participate.

Being poor in a world of WMDs is getting more and more dangerous and expensive. Even privacy is increasingly becoming a luxury that only the wealthy can afford. In a world which O’Neil calls a ‘data economy’, where artificial intelligence systems are hungry for our data, we are left with very few options but to produce and share as much data about our lives as possible. In the process, we are (implicitly or explicitly) coerced into self-monitoring and self-discipline. We are pressured into conforming to ideal bodies and “normal” health statuses as dictated by organizations and institutions that handle and manage our social relations, such as, our health insurances. Raley (2013) refers to this as dataveillance: a form of continuous surveillance through the use of (meta)data. Ever growing flow of data, including data pouring in from the Internet of Things – the Fitbits, Apple Watches, and other sensors that relay updates on how our bodies are functioning, continue to contribute towards this “dataveillance”.  

One might argue that helping people deal with their weight and health issues isn’t such a bad thing and that would be a reasonable argument. However, the key question here, as O’Neil points out, is whether this is an offer or a command. Using flawed statistics like the BMI, which O’Neil calls “mathematical snake oil”, corporates dictate what the ideal health and body looks like. They infringe on our freedom as they mould our health and body ideals. They punish those that they don’t like to look at and reward those that fit their ideals. Such exploitation are disguised as scientific and are legitimized through the use of seemingly scientific numerical scores such as the BMI. The BMI, kg/m2 (a person’s weight (kg) over height (m) squared), is only a crude numerical approximation for physical fitness. And since the “average” man underpins its statistical scores, it is more likely to conclude that women are “overweight” – after all, we are not “average” men. Even worse, black women, who often have higher BMIs, pay the heaviest penalties.  

The control of great amounts of data and the race to build powerful algorithms is a fight for political power. O’Neil’s breathtakingly critical look at corporations like Facebook, Apple, Google, and Amazon illustrates this. Although these powerful corporations are usually focused on making money, their profits are tightly linked to government policies which makes the issue essentially a political one.

These corporations have significant amounts of power and a great amount of information on humanity, and with that, the means to steer us in any way they choose. The activity of a single Facebook algorithm on Election Day could not only change the balance of Congress, but also potentially decide the presidency. When you scroll through your Facebook updates, what appears on your screen is anything but neutral – your newsfeed is censored. Facebook’s algorithms decided whether you see bombed Palestines or mourning Israelis, a policeman rescuing a baby or battling a protester. One might argue that television news has always done the same and this is nothing new. CNN, for example, chooses to cover a certain story from a certain perspective, in a certain way. However, the crucial difference is, with CNN, the editorial decision is clear on the record. We can pinpoint to individual people as responsible and accountable for any given decision and the public can debate whether that decision is the right one. Facebook on the other hand, O’Neil puts it, is more like the “Wizard of Oz” — we do not see the human beings involved. With its enormous power, Facebook can affect what we learn, how we feel, and whether we vote – and we are barely aware of any of it. What we know about Facebook, like other internet giants, comes mostly from the tiny proportion of their research that they choose to publish.

In a society where money buys influence, these WMD victims are nearly voiceless. Most are disenfranchised politically. The poor are hit the hardest and all too often blamed for their poverty, their bad schools, and the crime that afflicts their neighbourhoods. They, for the most part, lack economic power, access to lawyers, or well-funded political organizations to fight their battles. From bringing down minorities’ credit scores to sexism in the workplace, WMDs serve as tools. The result is widespread damage that all too often passes for inevitability.

Again, it is easy to point out that injustice, whether based on bias or greed, has been with us forever and WMDs are no worse than the human nastiness of the recent past. As with the above examples, the difference is transparency and accountability. Human decision making has one chief virtue. It can evolve. As we learn and adapt, we change. Automated systems, especially those O’Neil classifies as WMD, by contrast, stay stuck in the time until engineers dive in to change them.

If Big Data college application model had established itself in the early 1960s, we still wouldn’t have many women going to college, because it would have been trained largely on successful men. [204]

Rest assured, the book is not all doom and gloom or that all mathematical models are biased and unfair. In fact, O’Neil provides plenty of examples where models are used for good and models that have the potential to be great.

Whether a model becomes a tool to help the vulnerable or a weapon to inflict injustice, as O’Neil, time and time again emphasizes, comes down to its central objectives. Mathematical models can sift through data to locate people who are likely to face challenges, whether from crime, poverty, or education. The kinds of objectives adopted dictate whether such intelligence is used to reject or punish those that are already vulnerable or to reach out to them with the resources they need. So long as the objectives remain on maximizing profit, or excluding as many applicants as possible, or to locking up as many offenders as possible, these models serve as weapons that further inequalities and unfairness. Change that objective from leeching off people to reaching out to them, and a WMD is disarmed — and can even become a force of good. The process begins with the modellers themselves. Like doctors, data scientists should pledge a Hippocratic Oath, one that focuses on the possible misuse and misinterpretation of their models. Additionally, initiatives such as the Algorithmic Justice League, which aim to increase awareness of algorithmic bias, provide space for individuals to report such biases. 

Opaqueness is a common feature of WMDs. People have been dismissed from work, sent to prison, or denied loans due to their algorithmic credit scores with no explanation as to how or why. The more we are aware of their opaqueness, the better chance we have in demanding transparency and accountability and this begins by making ourselves aware of the works of experts like O’Neil. This is not a book only for those working in data science, machine learning or other related fields, but one that everyone needs to read. If you are a modeller, this book should encourage you to zoom out, think whether there are individuals behind the data points that your algorithms manipulate, and think about the big questions such as the objectives behind your code. Almost everyone, to a greater or lesser extent, is part of the growing world of ‘data economy’. The more awareness there is of the dark side of these machines, the better equipped we are to ask questions, to demand answers from those behind the machines that decide our fate.

Solitary confinement deprives dialogicity and therefore deprives a coherent sense of self

Solitary confinement NYTI recently came across this extremely powerful and disturbing 3 minutes video of solitary confinement. Given my dialogically informed perspective, it made me reflect (as well as initiate conversations with others ;-)) on the concepts of self, other, and world. Solitary confinement, which is the absence of dialogical relationship with others, seems to have a devastating effect on one’s sense of self and this video is an unerring illustration.

Solitary confinement is the complete isolation of prisoners from others or significantly reduced intersubjective contact with others or technically speaking, physical isolation for 22 to 24 hours per day. Solitary confinement is often referred as “Administrative segregation” in prisons. It is a common practice across the globe. In the US alone there are 80,000 to 100,000 inmates held in solitary confinements everyday. Similarly in Canada, “On any given day, there are 850 offenders (about 5.6% of the prison population) in solitary confinement. Some of these inmates have been isolated for more than four months. Many are young. Many have serious mental health problems.”

I don’t think there can be much disagreement regarding the disturbed state of most of the prisoners in the video. Solitary confinement deprives us of intersubjective contact with others which is imperative for constructing and sustaining our sense of self. Alexis de Tocqueville and Charles Dickens have described the prisoner in isolated cells as “buried alive” and subjected to “immense amount of torture and agony” through a “slow and daily tampering with the mysteries of the brain”. Looking at solitary confinement from a phenomenological perspective, Gallagher (2014), has identified a long list of substantial negative health effects associated with solitary confinement:

“anxiety, fatigue, confusion, paranoia, depression, hallucinations, headaches, insomnia, trembling, apathy, stomach and muscle pains, oversensitivity to stimuli, feelings of inadequacy, inferiority, withdrawal, isolation, rage, anger, and aggression, difficulty in concentrating, dizziness, distortion of the sense of time, severe boredom, and impaired memory.”

Solitary confinement can alter or eradicate sense of self. “The person subjected to solitary confinement risks losing her self and disappearing into a non-existence”. Nonetheless, such effects of solitary confinement are often understated perhaps due to our individualistic perception of self and our tendency to underestimate the importance of others as constituents of self. In fact, as Foucault in Discipline and Punish reminds us, the original purposes of solitary confinement, was as a positive instrument for reform. Solitary confinements were thought to rehabilitate the prisoner as a social and moral individual through reflection in isolation – a way for the prisoner to reflect on their crimes and return into his inner ‘true’ self. Given time to introspect in a solitary confinement, the prisoner was expected to turn their thoughts inward, repent their crimes, and eventually return to society as a morally cleansed citizen.

“Thrown into solitude, the convict reflects. Placed alone in the presence of his crime, he learns to hate it, and, if his soul is not yet blunted by evil, it is in isolation that remorse will come to assail him”

(Tocqueville in Foucault 1979: 237)

At the centre of this viewpoint is an underlying assumption which views the individual as something that exists and is capable of reasoning and functioning in isolation from others – a notion of the individual that is self-sufficient and self-contained where the necessarily interrelatedness of self, other, and world is overlooked. For philosophers such as Gardiner, this individualistic notion of the self is something we have adopted from the Western Christian notion of the soul through Cartesian-inspired philosophies. Contrary to this notion of solitary (which is built upon individualistic assumptions) as a means to come back to the inner self, deprived from contact and interaction with others, the very core of our existence is threatened.

‘‘Just as the body is formed initially in the mother’s womb (body), a person’s consciousness awakens wrapped in another consciousness … Individuality is created by and through others and the Other is part of the self.”

(Bakhtin, 1990)

Coming back to my brief musing, the fact that our sense of self seems to erode when we are deprived of interaction with others reinforces the Bakhtinian dialogical viewpoint that self and others co-develop and are two sides of the same coin. It is through our dialogical and embodied interactions with others that we are able to form and sustain a sense of coherent self. Others are essentially involved at all social and individual lived experiences. Through our encounters with others, we are able to evaluate and assess our own existence. Depriving the person of ‘others’ by subjecting them to solitary confinement, denies that essential additioal external perspective, the means by which a coherent self-image is maintained and the person risks losing the ‘self’ and disappearing into a non-existence.

Bakhtin, Merleau-Ponty, and the Cartesian subject

 

MC-Escher

M. C. Escher. Bond of Union, 1956

To what extent are the modernist conceptions of the subject Cartesian? What of our sciences, especially the human sciences and knowledge that emerges from them? And how can we overcome these lingering Cartesian residues? Gardiner in ”The incomparable monster of solipsism’: Bakhtin and Merleau-Ponty’ explores these questions (and more). This post is an attempt to provide a brief review of this paper. 

The subject, at least as far as Western metaphysics goes, is narcissist for it is shadowed by the Cartesian view which yields to a total self-determinism and total-self-grounding, Gardiner argues. Our capacities for abstract thinking are privileged at the expense of embodied dialogism. The production of knowledge according to Western metaphysics is rooted in the solitary subject contemplating an external world in a purely cognitive manner as a disembodied observer. The locus of classical modernity, is captured by the overwhelming desire for epistemological certitude and logical coherence in the desire to establish absolute certainty. In attempting to establish this lucidity and certainty; complex, multivalent and ambiguous reality is substituted with crystalline logic and conceptual rigour. Our obsession to transcribe the world into pure algorithmic language, as if the external world presents itself as a collection of inert facts, according to Gardiner, is the epitome of Cartesianism. Merleau-Ponty describes this as “A nightmare to which there is no awakening“.

It is however, important to note that the status of the human sciences has evolved considerably over the course of the enlightenment and since Popper, falsifiablity and not certitude and coherence is the hallmark of science. Descartes, with emphasis on doubt stands at the beginning of this tradition and as Ian Shapiro would argue, the locus classicus of modernity is in fact doubt, skepticism, and falsifiability. 

Nonetheless, Gardiner reminds us that the Cartesian perspective, not only remains central, but also poses a threat to dialogical values and what they espouse. By seeing the world as a projection of cognitive capacities, we leave no room for recognizing otherness. Not only is the body alien to this physical subject, other selves are equally mysterious that can have no authentically dialogical relationship. It is by adopting a dialogical world view that we are able to capture the interactive nature of bodies and selves as they co-exist within a shared life world. Gardiner asserts that Bakhtin and Merleau-Ponty are in agreement that modern Western thought, Platonism being the archetypal example, is dominated by perspectives that have rejected the validity of the body and it’s lived experience in favour of theoretical constructions. The utilitarian character of modern science and technology and abstract idealist philosophy reflects this. The tradition in which arguments are framed and debated in the philosophy of mind where philosophical zombies and Martian c-fibres often take centre stage, illustrates this further. 

This privileging of purely cognitive abilities results in tendencies in equating the self to subjective mental processes. This comes at a price of the viewing the subject as something abstract that dispassionately contemplates the world from afar. The Bakhtinian dialogical perspectives strongly oppose such formulations of the subject. Bakhtin insists that relation to the other requires presence of value positing consciousness and not a disinterested, objectifying gaze. Without the interactive context connecting self, other, and world, the subject slips into solipsism and loses ground for its Being and become empty.

Similarly, for Merleau-Ponty, the world is always in a Heraclitian flux, constantly transforming and becoming — not static and self-contained. Nor is our relation with others a purely cognitive affair. World and body exist in a relation of overlapping.  My senses reach out to the world and respond to it, actively engaging with it. They shape and configure it just as the world at the same time reaches deep into my sensory Being. The perceptual system is not a mere mechanical apparatus that only serves representational thinking to produce refined concepts and ideas but is radically intertwined with the world itself. Self perception, according to Merleau-Ponty, is not merely cognitive but it is also corporal. As I experience the world around me, I am simultaneously an entity in the world. I can hear myself speaking.

The world is presented to me in a deformed manner. My perspective are skewed by the precise situation I occupy at a particular point in time/space, by the idiosyncrasies of my psychosocial and historical context of my existence. Since I am thrown into the world lacking intrinsic significance and I have to make the world meaningful, I am condemned to make continual value judgments and generate meanings. I can never possess the totality of the world through intellectual grasps of my environment, thus my knowledge of the experiential world is always constrained and one sided. As meaning of the world for each of us is constructed from a vantage point of our uniquely embodied viewpoint, no two individuals experience the world precisely the same way.  Encounter with other selves is necessary to gain a more complete perspective on the world. I am never my own light to myself. It is through encounter with another self that I gain access to an external viewpoint through which I am able to visualize myself as a meaningful whole, a gestalt.

Gardiner argues this is how we can escape solipsism – through an apprehension of oneself in the mirror of the other, a vantage point that enables one to evaluate and assess his/her own existence and construct a coherent self-image. To be able to conceptualize myself as a meaningful whole, which is fundamental to self-individuation and self-understanding, I need additional, external perspective. By looking through the other’s soul, I vivify my exterior and make it part of the plastic pictorial world.

We need a philosophy that understands nature as a dynamic, living organism that is ‘pregnant with potentials’. As embodied subjects, we are intertwined with the world, bound up with the dynamic cycles and processes of growth and change.  Insofar as our minds are incarnate and our bodies necessarily partake the physical and biological natural processes, there is an overlap of spirit and matter, subject and object, nature and culture. No break in the circuit; impossible to say where nature ends and subject begins. The self is dynamic, embodied, and creative entity that strives to attribute meaning and value to the world. We are forced to make certain choices and value judgments by Being-in-the-World to transform the world as it is given into a-world-for-me. In making the world a meaningful place, the subject actively engages with and alters its lived environment. I and other co-mingle in the ongoing event of Being. The self, as Bakhtin points out, is ‘unfinalizable’ –  continually re-authored as circumstances change.

Gardiner concludes that both Merleau-Ponty and Bakhtin object to the ‘Primacy of intellectual objectivism’ taken as the model of intelligibility which forms Western philosophy from which our sciences emerge. Such objectification of the world in modernist paradigms represents a retreat from lived experience. Genuinely participative thinking and active engaging requires an engaged, embodied relation to the other and to the world at large. Our capacity for abstract cognition and representational thinking is incapable of grasping the linkage between myself and the other within the fabric of everyday social life. Hence the solipsistic consequences of subjectivistic idealism. As Bakhtin’s ‘carnal hermeneutics’ – the dialogical character of human embodiment – emphasizes, the incarnated self can only be affirmed through its relation with the other. The body is not something self-sufficient: it needs the others’ recognition and form giving activity.

Science! It works, bitches!

Science 2

Science is constantly pushing the boundaries as to what can be known and it’s the best available tool we have to produce the most reliable knowledge. Scientifically produced knowledge is often taken as legitimate, objective, unbiased and value-free. A scroll through some scientist’s Twitter posts can show just how much a great deal of scientists make it clear that knowledge that science produces is the ultimate fact. Arguably, knowledge grounded in science is perceived as the ultimate and the most authoritative that others need to aspire to – one that is qualified to legitimately dictate correct from incorrect or right from wrong and considered as the standard against all other forms of knowledge should be measured.

This form of knowledge is often presented in sharp contrast with knowledge that is dogmatic and ideological as if they are neatly separable. Those who are reasonable and educated are seen as free from ideologies and dogmas. Those that attempt to dispute this so called fact are often portrayed as anti-science. Typically snarled at “Don’t take it personal it’s science, can’t argue with the facts”.

Don’t get me wrong! I love science. Science is wonderful and yes, as far as the most consensual way of producing knowledge goes, science may be the best tool we have. However, it’s the idea of scientific knowledge as completely objective, free from any values, ideologies and biases that I object to. There is no such thing as ‘a view from nowhere’ and science and scientists are not immune to this. Science as completely free and separable from ideologies, biases and currently available discourses and a tool by which we objectively discover what is out there is simply naive. Nor is science free from theoretical commitments, or epistemological and ontological assumptions on which experiments are founded.

The methods we choose to investigate (and by implication those we choose to ignore) are central to the kind of knowledge we produce. Such methods are essentially tied to certain underlying theoretical commitments, which are embedded in certain ontological and epistemological assumptions. How something is defined has great influence on what conclusion one arrives at. How scientists analyse and interpret data can greatly be influenced by their preconceived notions. These two studies on sex differences on the brain, arriving at almost opposite conclusions despite having comparable data, shows just that. 

Science as a way to establishing facts gets fussier and messier as we move away from the natural sciences and towards studies of human cognition and behaviour. The more socially constructed the concept seems, the more problematic it becomes to make any claims of knowledge as the truth or an established fact. This is evident by the fact that there can be multiple equally plausible theories and research findings explaining certain concepts such as emotions or happinessNot to mention the difficulties defining these concepts in a manner that scientists agree upon. The very idea of defining the concept or phenomena that scientists are trying to get hold on brings with it associated cultural, historical, and ideological baggage.

We operate within a certain cultural context and are situated in a certain geographical location at a certain time in history where certain ways of practicing science are more acceptable than others. The way we frame how we think about certain things as well as the methods we develop to explore these questions are inseparably tangled with these factors. As well as our historical and cultural past, our own perspective is coloured by our immediate interaction with others around us. The very language we use to formulate our hypothesis predetermines, to some extent, the direction that our research follows. For example, despite the underlying similarities these questions are framed “are you pro-choice?” or “Do you object to the idea of murdering unborn children?” will elicit different responses.

This messy picture of science where the objective and subjective are not neatly separable, makes attempts to develop so called objective approaches with regard to socially constructed behaviours such as criminality questionable. What kind of behaviour is criminal? In which society? At what time? There is no simple and universally defined definition of crime. A brief look at the concept of homosexuality that has developed from being a criminal act to now (for most of the Western society, anyway) as a right, shows how slippery and context dependent the very idea of what counts as a criminal behaviour. Any attempt to understand drug-related crimes, for example, shows how unclear the idea of crime can be – both snorting cocaine and smoking cannabis (in certain parts of the world) being defined as criminal acts, legally speaking .

I am not arguing that all science is biased and that the work scientists have been doing is no use. The point I want to make is that how we come to conceptualise certain phenomena in a certain way but not other does not spring out of nowhere but is inextricably linked to our language, the current dominant theories, current discourses available to us, and our history among other things. Therefore we need to be aware that our science is (implicitly or explicitly) influenced and to some extent determined by these – some fields more than others. And as the scientist is not a robot that is devoid of passion, interests, errors, and biases (which is not necessarily beneficial either as I think some passion and interest in our research is important), the least we can do is acknowledge this and actively question and review whether our views have been clouded by such as well as being mindful of any generalizable claims that we make as objective facts.

What makes me, me?

What makes me like coffee over tea? Why do some people engage in criminal activities? What is it that makes some people a rapist? What are the sources of bullying behaviour?

Wouldn’t it be wonderful if we could find simple explanations for such complex questions? Psychology is constantly trying to explain complex behaviour. It’s not uncommon to hear explanations by psychologists, neuroscientists, social scientists, criminologists, and the like, usually each from their own perspective, asserting why we prefer one thing over the other, why we are repulsed by certain things, or why we behave the way we do. These explanations often invoke factors such as parenting style, genes, environment, history, culture and so on, depending on the perspective the subject has been approached from.

Arguably, explanations that closely focus on certain factors and not others serve a purpose when it comes to narrowly defined investigations. The problem is, in attempting to explain complex behaviour, we often fail prey (knowingly or unknowingly) to false dichotomies. Despite the constant warning against false dichotomies, it is common to read scientific papers making attempts, for example,  to ascribe the influence of genes as opposed to environment in seeking to understand the effect of parenting on the kind of person we grow up to be.

The “person” is an extremely slippery and difficult concept to pin down. What makes me ‘me’ is extremely fuzzy (and constantly changing) to the extent that it cannot be separated from those around me, my historical background, the culture and time I am situated in, and the dynamical interactions at play. We are constantly dynamically interacting and influencing others around us, and the physical environment, as well as being influenced by these factors. My view of what constitutes a criminal behaviour for example, does not spring into being from nowhere. Rather it is an interplay of many factors, such as the currently available discourse, my political, social, economic, and geographical position in a certain society, the kind of shared of language that is available for use, as well as my family, culture and historical background.

Given that we are constantly in the process of becoming mediated by the dynamical interplay of inextricably linked factors such as culture, genes, physical environment, history, currently available discourses, local societal norms, diet and so on, attempting to separate these factors and claiming to have determined  the contribution genes and/or environment makes towards complex behaviour such as criminality would be similar to having successfully separated the inside and outside of a Mobius strip.