The fruit salad maker; it’s an interdisciplinary tale

Multidisciplinary = the fruit bowl (single disciplines brought together) Interdisciplinary = a fruit salad (combine disciplines together for one output) Transdisciplinary = the smoothie (disciplines transformed-new). EU EnRRICH project

EU EnRRICH project

A young fruit enthusiast wanted to make a fruit salad. Seeing that so many different fruit suppliers bring all sorts of fruit to her fruit bar, and many customers in return buy individual fruits, she thought she’d make something that each fruit supplier doesn’t produce by combining their supplies – a fruit salad. Besides, there seems to be a great deal of excitement over this new mixing of various fruits and everybody seems to want and encourage it.

Having sampled many different fruits over the years, the fruit salad maker decided it is a good use of her time and expertise to get into the fruit salad making business. She decided on mango, kiwi and pineapple as her fruits of choice that would make her signature fruit salad. They blend very well, they are grown locally, and they complement one another. When mixed, they not only produce an excellent taste, but they are also very appetizing to look at. Most mango, kiwi and pineapple lovers should be able to appreciate and enjoy them, the fruit salad maker thought and she started the process of combining her fruits.

“Not so fast”, came along the fruit gatekeepers. “We need to first see that your tastes for fruits, ability to make fruit salad, and knowledge of each fruit is sufficient before we allow you to open this fruit bar”. Well, it’s legally required that a fruit bar is certified after all. And on the positive side, this certificate would signify a much-needed validation and boost from the fruit community.

Not being able to open her fruit bar without the recognition required and the seal of approval, the fruit salad maker embarked on the process of fulfilling the necessary requirements to pass the necessary tests. She compiled a convincing argument for the need for fruit salads, her knowledge of three fruits, and most importantly for her personal skills and passion for mixing fruits. She demonstrated how her fruits of choice go well together, why they should be made into fruit salad and how much her customers would benefit from such combination.

She then produced the first plate of fruit salad and put it in front of the fruit gatekeepers. “I love the idea of fruit salads. We are all stuck in our special fruit echo chambers. We should all try fruit salads and appreciate those that actually make colourful fruit salads”, said the mango gatekeeper. He then tasted a big mouthful of the fruit salad before him. “It needs more mango”, he said. “I also recommend you study the history of mango production and the fine-grained detail of the biochemistry of mango to make your fruit salad better. I am afraid I can’t let you past my gate until then”, he added.

The kiwi gatekeeper, who also confessed how much he loves fruit salads, followed and had a mouthful of the fruit salad in front of him.  Like his previous colleague the mango gatekeeper, the kiwi gatekeeper seems to be solely concerned with the kiwi part of the fruit salad – not the whole combination. “Salt would really compliment the kiwis, add a pinch to bring out the flavour more. In order for me to recognize that you have used kiwi in your fruit salad, you need a lot more kiwi on your fruit salad,” he commented. “Plus, I don’t recognize the breed of kiwi that you’re using. I will give you a list of good kiwis you need to use. Until the kiwi is right, I am afraid it is my duty to not let you pass my gate. Better luck next time” he added.

Lastly, the pineapple gatekeeper scooped a spoonful of the fruit salad and tried it. “I also love the idea of fruit salads but I have to tell you that this is not how we slice pineapples over at the pineapple empire. We also marinate them in our special sauce. Your pineapples lack both. You really need to know your pineapple inside out if you are to call yourself a fruit salad maker at all. Plus, I see very little pineapple on this plate. So, get the special sauce from our empire and cut your pineapples our way. Only then can we give you our approval,” she exclaimed.

The fruit salad maker, unestablished and with much less power than the gatekeepers, felt disheartened. She tried to point out that each gatekeeper needs to look at the dish as a whole instead of focusing on each specific fruit. And, surely, the single fruit bars don’t go through as much scrutiny. Unfortunately, questioning the individual fruit experts didn’t do her any favours – they have been in their respective fruit business for much longer than she has and must surely know what they are doing. Who’s she to question their domain expertise?!

It felt as though, what they are demanding seemed too self-fulfilling and incommensurable at times. But then again, she suffered from too much self-doubt given that this is her first big attempt at making a fruit salad, to argue with their demands. Either way, if she is to get that business going, she needs each gatekeeper’s seal of approval. She went ahead and attempted to make the type of fruit salad that would satisfy each gatekeeper; with plenty of mango, huge helpings of ripe kiwi and custom sliced pineapples.

At the next round of testing, the fruit salad maker revised the plate in a manner that reflects the advice previously provided by the gatekeepers. Unfortunately, they unanimously agreed that the plate is overflooded with too much fruit, is unhealthy and is unattractive to look at. “All the excess fruit must be trimmed away,” they declared. “This is a health hazard and we cannot approve of such a dish. Think about how to make it neater, healthier and attractive and come back to us with your improved fruit salad. We will then discuss the matter and perhaps let you through our gate,” they said.

After many attempts to satisfy each of the gatekeepers version of a perfect fruit salad, the fruit salad maker is back to square one. She’s caught in a recursive loop. Each fruit connoisseur, expert on their own fruit, seems to underappreciate the taste and benefit of the fruit mix before them. Putting individual fruit experts together doesn’t necessarily make a fruit salad judge, after all.

Having gone through a number of time-consuming practices of making fruit salads and the bureaucratic paperwork associated with it, the fruit salad maker wonders if the fruit salad making business is worthwhile at all. Single fruit dealings, the dominant mode of doing business would have been simpler – not as rewarding for sure, but certainly simpler. But the thing is, once you develop the palate for the unique taste of fruit salads, nothing else will do.

 

 

For a more scholarly read

• Ananny, M., & Crawford, K. (2016). Seeing without knowing: Limitations of the transparency ideal and its application to algorithmic accountability. new media & society, 1461444816676645.
• Ajunwa, I., Crawford, K., & Schultz, J. (2017). Limitless worker surveillance. Cal. L. Rev., 105, 735.
• Barabas, C., Dinakar, K., Virza, J. I., & Zittrain, J. (2017). Interventions over Predictions: Reframing the Ethical Debate for Actuarial Risk Assessment.arXiv preprint arXiv:1712.08238.
• Barocas, S., & Selbst, A. D. (2016). Big data’s disparate impact.
• Bartlett, A., Lewis, J., Reyes-Galindo, L., & Stephens, N. (2018). The locus of legitimate interpretation in Big Data sciences: Lessons for computational social science from-omic biology and high-energy physics. Big Data & Society, 5(1), 2053951718768831.
• Bolukbasi, T., Chang, K. W., Zou, J. Y., Saligrama, V., & Kalai, A. T. (2016). Man is to computer programmer as woman is to homemaker? Debiasing word embeddings. In Advances in Neural Information Processing Systems (pp. 4349-4357).
• Caliskan-Islam, A., Bryson, J. J., & Narayanan, A. (2016). Semantics derived automatically from language corpora necessarily contain human biases. arXiv preprint arXiv:1608.07187.
• Chouldechova, A. (2017). Fair prediction with disparate impact: A study of bias in recidivism prediction instruments.arXiv preprint arXiv:1703.00056. (PDF)
• Creemers, R., (2018) China’s Social Credit System: An Evolving Practice of Control
• Datta, A., Sen, S., & Zick, Y. (2016, May). Algorithmic transparency via quantitative input influence: Theory and experiments with learning systems. In Security and Privacy (SP), 2016 IEEE Symposium on (pp. 598-617). IEEE. (PDF)
• Datta, A., Tschantz, M. C., & Datta, A. (2015). Automated experiments on ad privacy settings.Proceedings on Privacy Enhancing Technologies, 2015(1), 92-112
• Edwards, L., Martin, L., & Henderson, T. (2018). Employee Surveillance: The Road to Surveillance is Paved with Good Intentions.
• Friedman, B., & Nissenbaum, H. (1996). Bias in computer systems. ACM Transactions on Information Systems (TOIS), 14(3), 330-347
• Jawaheri, H. A., Sabah, M. A., Boshmaf, Y., & Erbad, A. (2018). When A Small Leak Sinks A Great Ship: Deanonymizing Tor Hidden Service Users Through Bitcoin Transactions Analysis. arXiv preprint arXiv:1801.07501.
• Keyes, O. (2018). The misgendering machines: Trans/HCI implications of automatic gender recognition. Proceedings of the ACM on Human-Computer Interaction, 2(CSCW), 88.
• Kleinberg, J., & Mullainathan, S. (2018). Simplicity Creates Inequity: Implications for Fairness, Stereotypes, and Interpretability. arXiv preprint arXiv:1809.04578.
• Kuehlkamp, A., Becker, B., & Bowyer, K. (2017, March). Gender-From-Iris or Gender-From-Mascara?. In Applications of Computer Vision (WACV), 2017 IEEE Winter Conference on (pp. 1151-1159). IEEE.
• Lau, J., Zimmerman, B., & Schaub, F. (2018). Alexa, Are You Listening?: Privacy Perceptions, Concerns and Privacy-seeking Behaviors with Smart Speakers. Proceedings of the ACM on Human-Computer Interaction, 2(CSCW), 102.
• Lipton, Z. C., & Steinhardt, J. (2018). Troubling Trends in Machine Learning Scholarship. arXiv preprint arXiv:1807.03341.
• Miller, T. (2017). Explanation in artificial intelligence: insights from the social sciences. arXiv preprint arXiv:1706.07269.
• Monahan, J., & Skeem, J. L. (2016). Risk assessment in criminal sentencing.Annual review of clinical psychology, 12, 489-513.
• Munoz, C., Smith, M., & Patil, D. (2016). Big data: A report on algorithmic systems, opportunity, and civil rights.Executive Office of the President. The White House.
• Narayanan, A., Huey, J., & Felten, E. W. (2016). A precautionary approach to big data privacy. In Data protection on the move (pp. 357-385). Springer, Dordrecht.
• Raji, I. D., & Buolamwini, J. (2019). Actionable auditing: Investigating the impact of publicly naming biased performance results of commercial ai products. In AAAI/ACM Conf. on AI Ethics and Society.
• Richardson, R., Schultz, J., & Crawford, K. (2019). Dirty Data, Bad Predictions: How Civil Rights Violations Impact Police Data, Predictive Policing Systems, and Justice. New York University Law Review Online, Forthcoming.
• Sadowski, J. (2019). When data is capital: Datafication, accumulation, and extraction. Big Data & Society, 6(1), 2053951718820549.
• Silver, E., & Miller, L. L. (2002). A cautionary note on the use of actuarial risk assessment tools for social control. Crime & Delinquency, 48(1), 138-161.
• Stark, L. (2019). Facial recognition is the plutonium of AI. XRDS: Crossroads, The ACM Magazine for Students, 25(3), 50-55.
• Stark, L., & Hoffmann, A. L. (2018). Data Is The New What?: Popular Metaphors & Professional Ethics in Emerging Data Cultures. Journal of Cultural Analytics.
• Suresh, H., & Guttag, J. V. (2019). A Framework for Understanding Unintended Consequences of Machine Learning. arXiv preprint arXiv:1901.10002.
• van Wynsberghe, A., & Robbins, S. (2018). Critiquing the reasons for making artificial moral agents. Science and engineering ethics, 1-17.
• Weinberg, J. (2018). “Know Everything that Can Be Known About Everybody”: The Birth of the Credit Report.
• Wilson, B., Hoffman, J., & Morgenstern, J. (2019). Predictive Inequity in Object Detection. arXiv preprint arXiv:1902.11097.
• Yeung, K. (2017). Algorithmic Regulation: A Critical Interrogation.
• Zafar, M. B., Valera, I., Gomez Rodriguez, M., & Gummadi, K. P. (2017, April). Fairness beyond disparate treatment & disparate impact: Learning classification without disparate mistreatment. In Proceedings of the 26th International Conference on World Wide Web (pp. 1171-1180). International World Wide Web Conferences Steering Committee.
• Zhang, B. H., Lemoine, B., & Mitchell, M. (2018). Mitigating Unwanted Biases with Adversarial Learning. arXiv preprint arXiv:1801.07593.

This list is not exhaustive by any means but work that is relevant to my work and a list I revise and revisit regularly

Link for the main resources page here

Books

Weapons of math destruction: how big data increases inequality and threatens democracy by Cathy O’Neil. A great number of the article on the list below are written by O’Neil. She is also active on Twitter regularly posting links and interesting critical insights on everything to do with mathematical models and bias. Here is my own review of O’Neil’s book with plenty of relevant links itself and here for another excellent review of O’Neil’s book.

We Are Data: Algorithms and the Making of Our Digital Selves (2018) by John Cheney-Lippold.

Below is the first few paragraph from a review by Daniel Zwi, a lawyer with an interest in human rights and technology. Here is also a link to my twitter thread where you can read excerpts from the book that I tweeted as I read the book.

In 2013, a 41-year-old man named Mark Hemmings dialled 999 from his home in Stoke-on-Trent. He pleaded with the operator for an ambulance, telling them that ‘my stomach is in agony’, that ‘I’ve got lumps in my stomach’, that he was vomiting and sweating and felt light-headed. The operator asked a series of questions — ‘have you any diarrhoea or vomiting?’; ‘have you passed a bowel motion that looks black or tarry or red or maroon?’ — before informing him that he did not require an ambulance. Two days later Mr Hemmings was found unconscious on the floor of his flat. He died of gallstones shortly after reaching hospital.

This episode serves as the affective fulcrum of We Are Data: Algorithms and the Making of Our Digital Selves, John Cheney-Lippold’s inquiry into the manner in which algorithms interpret and influence our behaviour. It represents the moment at which the gravity of algorithmic regulation is brought home to the reader. And while it may seem odd to anchor a book about online power dynamics in a home telephone call (that most quaint of communication technologies), the exchange betokens the algorithmic relation par excellence. Mr Hemmings’s answers were used as data inputs, fed into a sausage machine of opaque logical steps (namely, the triaging rules that the operator was bound to apply), on the basis of which he was categorised as undeserving of immediate assistance.

The dispassionate, automated classification of individuals into categories is ubiquitous online. We either divulge our information voluntarily — when we fill out our age and gender on Facebook, for example — or it is hoovered up surreptitiously via cookies (small text files which sit on our computer and transmit information about our browsing activity to advertising networks). Our media preferences, purchases and interlocutors are noted down and used as inputs according to which we are ‘profiled’ — sorted into what Cheney-Lippold calls ‘measureable types’ such as ‘gay conservative’ or ‘white hippy’ — and served with targeted advertisements accordingly.

The Age of Surveillance Capitalism: The Fight for a Human Future at the New Frontier of Power (2019) by Shoshana Zuboff

The challenges to humanity posed by the digital future, the first detailed examination of the unprecedented form of power called “surveillance capitalism,” and the quest by powerful corporations to predict and control our behavior. Shoshana Zuboff’s interdisciplinary breadth and depth enable her to come to grips with the social, political, business, and technological meaning of the changes taking place in our time. We are at a critical juncture in the confrontation between the vast power of giant high-tech companies and government, the hidden economic logic of surveillance capitalism, and the propaganda of machine supremacy that threaten to shape and control human life. Will the brazen new methods of social engineering and behavior modification threaten individual autonomy and democratic rights and introduce extreme new forms of social inequality? Or will the promise of the digital age be one of individual empowerment and democratization?

The Age of Surveillance Capitalism is neither a hand-wringing narrative of danger and decline nor a digital fairy tale. Rather, it offers a deeply reasoned and evocative examination of the contests over the next chapter of capitalism that will decide the meaning of information civilization in the twenty-first century. The stark issue at hand is whether we will be the masters of information and machines or its slaves.

Algorithms of oppression: How search engines reinforce – below is an excerpt from Nobel’s book: You can also find another review of Algorithms of Oppression here. Run a Google search for “black girls”—what will you find? “Big Booty” and other sexually explicit terms are likely to come up as top search terms. But, if you type in “white girls,” the results are radically different. The suggested porn sites and un-moderated discussions about “why black women are so sassy” or “why black women are so angry” presents a disturbing portrait of black womanhood in modern society. In Algorithms of Oppression, Safiya Umoja Noble challenges the idea that search engines like Google offer an equal playing field for all forms of ideas, identities, and activities. Data discrimination is a real social problem; Noble argues that the combination of private interests in promoting certain sites, along with the monopoly status of a relatively small number of Internet search engines, leads to a biased set of search algorithms that privilege whiteness and discriminate against people of color, specifically women of color.

Algorithms to Live By: The Computer Science of Human Decisions by Brian Christian and Tom Griffiths. This book is concerned with the workings of the human mind and how computer science can help human decision making.  Here is a post by Artem Kaznatcheev on Computational Kindness which might give you a glimpse of the some of the issues that book covers. Here is a long interview with Brian Christian and Tom Griffiths and a TED Talk with Tom Griffiths on The Computer Science of Human Decision Making.

The Black Box Society: The Secret Algorithms That Control Money and Information by Frank Pasquale. You can read the introduction and conclusion chapters of his book here.  And here is a good review of Pasquale’s book. You can follow his twitter stream here.

Technically Wrong: Sexist Apps, Biased Algorithms, and Other Threats of Toxic Tech by Sara Wachter-Boettcher

Here is a synopsis:  A revealing look at how tech industry bias and blind spots get baked into digital products—and harm us all.

Buying groceries, tracking our health, finding a date: whatever we want to do, odds are that we can now do it online. But few of us ask why all these digital products are designed the way they are. It’s time we change that. Many of the services we rely on are full of oversights, biases, and downright ethical nightmares: Chatbots that harass women. Signup forms that fail anyone who’s not straight. Social media sites that send peppy messages about dead relatives. Algorithms that put more black people behind bars.

Sara Wachter-Boettcher takes an unflinching look at the values, processes, and assumptions that lead to these and other problems. Technically Wrong demystifies the tech industry, leaving those of us on the other side of the screen better prepared to make informed choices about the services we use—and demand more from the companies behind them.

Paula Boddington, Oxford academic and author of Towards a Code of Ethics for Artificial Intelligence, recommends the five best books on Ethics for Artificial Intelligence. Here is the full interview with Nigel Warburton, published on December 1, 2017.

“Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor” by Virginia Eubanks is being published and will be released on January 23, 2018. Here is an excerpt from Danah Boyd’s blog:

“Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor” is a deeply researched accounting of how algorithmic tools are integrated into services for welfare, homelessness, and child protection. Eubanks goes deep with the people and families who are targets of these systems, telling their stories and experiences in rich detail. Further, drawing on interviews with social services clients and service providers alongside the information provided by technology vendors and government officials, Eubanks offers a clear portrait of just how algorithmic systems actually play out on the ground, despite all of the hope that goes into their implementation. Additionally, Berkman Klein discusses “Algorithms and their unintended consequences for the poor” with Eubanks here.

The Big Data Agenda: Data Ethics and Critical Data Studies by Annika Richterich PDF available through the link here.

“This book highlights that the capacity for gathering, analysing, and utilising vast amounts of digital (user) data raises significant ethical issues. Annika Richterich provides a systematic contemporary overview of the field of critical data studies that reflects on practices of digital data collection and analysis. The book assesses in detail one big data research area: biomedical studies, focused on epidemiological surveillance. Specific case studies explore how big data have been used in academic work.

The Big Data Agenda concludes that the use of big data in research urgently needs to be considered from the vantage point of ethics and social justice. Drawing upon discourse ethics and critical data studies, Richterich argues that entanglements between big data research and technology/ internet corporations have emerged. In consequence, more opportunities for discussing and negotiating emerging research practices and their implications for societal values are needed.”

Re-Engineering Humanity by professor Evan Selinger and Brett Frischmann

Every day, new warnings emerge about artificial intelligence rebelling against us. All the while, a more immediate dilemma flies under the radar. Have forces been unleashed that are thrusting humanity down an ill-advised path, one that’s increasingly making us behave like simple machines? In this wide-reaching, interdisciplinary book, Brett Frischmann and Evan Selinger examine what’s happening to our lives as society embraces big data, predictive analytics, and smart environments.

Outnumbered: From Facebook and Google to Fake News and Filter-bubbles – The Algorithms That Control Our Lives (featuring Cambridge Analytica) by David Sumpter. A review from Financial Times, here.

Artificial Unintelligence: How Computers Misunderstand the World By Meredith Broussard

A guide to understanding the inner workings and outer limits of technology and why we should never assume that computers always get it right.

“In Artificial Unintelligence, Meredith Broussard argues that our collective enthusiasm for applying computer technology to every aspect of life has resulted in a tremendous amount of poorly designed systems. We are so eager to do everything digitally—hiring, driving, paying bills, even choosing romantic partners—that we have stopped demanding that our technology actually work. Broussard, a software developer and journalist, reminds us that there are fundamental limits to what we can (and should) do with technology. With this book, she offers a guide to understanding the inner workings and outer limits of technology—and issues a warning that we should never assume that computers always get things right.”

 

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