Humility is a luxury the privileged can afford

I had the privilege of participating in a science communication conference last week (12, December 2018). Some of the speakers beautifully and convincingly articulated the argument for the importance of academics communicating their work with non-academics as well as other academics from different disciplines and how to do it. Alan Alda’s talk, in particular was deep,insightful and thought-provoking.

Alda’s “Communication is not something you add to science; it is the essence of science” captures his key message that communication is an essential part of doing science and not something separate and extra. There is very little dispute regarding the importance of sharing one’s work with the general public as well as scientists, and with academics outside one’s field. However, there is very little guidance as to how one ought to go about it. Alda’s talk during the SCI:COM conference in Dublin provided some of the most insightful advice by far that I have come across.

Alda suggests, talk TO and not AT people. This seemingly obvious but powerful statement is a way of shifting the mindset from “giving a talk” or “delivering a lecture” which treats knowledge as something that can be simply dispersed to communication as two-way shared activity.

Science commination is a reciprocal process that involves both the speaker and the audience. It is vital that the communicator pays attention to the person that they are communicating with. “It is up to you,the communicator, to ensure that the person is following and to bring them onboard.” And this requires understanding your audience. As Alda puts it: “the speaker needs to listen harder than the listener”.

Communication, Alda argues, is not about me figuring out the best message and spraying it at you, it is building a reciprocal dynamic relationship that changes both the speaker and the audience. Effective communication is understanding your audience and knowing how to connect with them. In order to do so, we don’t start with crafting the best message; we start with awareness of the audience.

Good science communication, Alda emphasises, requires reputation, which is intrinsically connected to trust. Speaking from a position of authority is different from speaking as an equal fellow human being. Your audience is more likely to trust you when you speak as a fellow human and this requires humility, which brings me to central point of my blog.

I wholeheartedly agree with Alda’s approach to communication and also think that humility is a virtue that needs to be highly valued.However, whether humility is viewed as a virtue is dependent on societal stereotypes, hence my conflict with it. Humility doesn’t yield trust and reputation for everyone and I speak from a perspective of a black woman in academia. 

In academia, we often have an ideal representation or an image of what an ‘intellectual’ looks like. This is typically a white, middle-class, cis, male. Society’s stereotypes make this group of people automatically perceived as authoritative. Academia’s structure means that people who fit the stereotypically ‘intellectual’ are seen as as unquestionable experts. And for the privileged who fit society’s ‘intellectual’, where coming across as authoritative is the default, humility and speaking to their audience as a fellow human, gains them trust. On the other hand, academics that don’t fit society’s stereotypical ‘intellectual’ often have to work hard to simply prove that they are as capable of their white male counterparts. In an academic environment where looks, gender and race are part of ‘fitting in’ and getting acknowledgements as an intellectual, humility, which is an admirable character for the white male, can be a way of proving that you are not capable, for a black woman. When the default assumption is often you might lack the capacities due to your race or gender, humility might seem like conforming people’s assumptions. Humility, downplaying one’s skills and achievements, for the black woman who already struggles to establish herself as an intellectual, can be a self-imposed punishment which underestimates her intellectual capacity. Humility, then seems, a luxury that the privileged can afford.

Having said that, I must emphasize that the problem is not humility itself but societal stereotypes and rigid academic structures. I still think humility is a character we need to treasure, both in academia and outside. I just hope that we gradually challenge these stereotypes of what an expert intellectual looks like, which will then afford minority’s the luxury for humility and not punish them for it.

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.

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.”