Social algorithms and the Weapons of Math Destruction

Cathy O'Neil holding her new book: Weapons of Math Destruction at a Barnes & Noble in NYC.

Cathy O’Neil holding her new book: Weapons of Math Destruction at a Barnes & Noble in New York city.

In reference to intelligent robots taking over the world, Andrew Ng once said: “I don’t work on preventing AI from turning evil for the same reason that I don’t work on combating overpopulation on the planet Mars.” Sure, it will be an important issue to think about when the time comes. But for now, there is no productive way to think seriously about it. Today there are more concrete problems to worry about and more basic questions that need to be answered. More importantly, there are already problems to deal with. Problems that don’t involve super intelligent tin-men, killer robots, nor sentient machine overlords. Focusing on distant speculation obscures the fact that algorithms — and not necessarily very intelligent ones — already reign over our lives. And for many this reign is far from benevolent.

I owe much of my knowledge about the (negative) effects of algorithms on society to the writings of Cathy O’Neil. I highly recommend her blog mathbabe.org. A couple of months ago, she shared the proofs of her book Weapons of Math Destruction with me, and given that the book came out last week, I wanted to share some of my impressions. In this post, I want to summarize what makes a social algorithm into a weapon of math destruction, and share the example of predictive policing.

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Computational kindness and the revelation principle

In EWD1300, Edsger W. Dijkstra wrote:

even if you have only 60 readers, it pays to spend an hour if by doing so you can save your average reader a minute.

He wrote this as the justification for the mathematical notations that he introduced and as an ode to the art of definition. But any writer should heed this aphorism.[1] Recently, I finished reading Algorithms to Live By by Brian Christian and Tom Griffiths.[2] In the conclusion of their book, they gave a unifying name to the sentiment that Dijkstra expresses above: computational kindness.

As computer scientists, we recognise that computation is costly. Processing time is a limited resource. Whenever we interact with others, we are sharing in a joint computational process, and we need to be mindful of when we are not carrying our part of the processing burden. Or worse yet, when we are needlessly increasing that burden and imposing it on our interlocutor. If you are computationally kind then you will be respectful of the cognitive problems that you force others to solve.

I think this is a great observation by Christian and Griffiths. In this post, I want to share with you some examples of how certain systems — at the level of the individual, small group, and society — are computationally kind. And how some are cruel. I will draw on examples from their book, and some of my own. They will include, language, bus stops, and the revelation principle in algorithmic game theory.
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Systemic change, effective altruism and philanthropy

Keep your coins. I want change.The topics of effective altruism and social (in)justice have weighed heavy on my mind for several years. I’ve even touched on the latter occasionally on TheEGG, but usually in specific domains closer to my expertise, such as in my post on the ethics of big data. Recently, I started reading more thoroughly about effective altruism. I had known about the movement[1] for some time, but had conflicting feelings towards it. My mind is still in disarray on the topic, but I thought I would share an analytic linkdex of some texts that have caught my attention. This is motivated by a hope to get some guidance from you, dear reader. Below are three videos, two articles, two book reviews and one paper alongside my summaries and comments. The methods range from philosophy to comedy and from critical theory to social psychology. I reach no conclusions.

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Diversity and persistence of group tags under replicator dynamics

Everyday I walk to the Stabile Research Building to drink espresso and sit in my cozy — although oversaturated with screens — office. Oh, and to chat about research with great people like Arturo Araujo, David Basanta, Jill Gallaher, Jacob Scott, Robert Vander Velde and other Moffitters. This walk to the office takes about 30 minutes each way, so I spend it listening to podcasts. For the past few weeks, upon recommendation from a friend, I’ve started listing to the archive of the Very Bad Wizards. This is a casual — although oversaturated with rude jokes — conversation between David Pizarro and Tamler Sommers on various aspects of the psychology and philosophy of morality. They aim at an atmosphere of two researchers chatting at the bar; although their conversation is over Skype and drinks. It is similar to the atmosphere that I want to promote here at TheEGG. Except they are funny.

While walking this Wednesday, I listed to episode 39 of Very Bad Wizards. Here the duo opens with a Wilson & Haidt’s TIME quiz meant to quantify to what extent you are liberal or conservative.[1] They are 63% liberal.[2]

To do the quiz, you are asked to rate 12 statements (well, 11 and one question about browsers) on a six point Likert scale from strongly disagree to strongly agree. Here are the three that caught my attention:

  1. If I heard that a new restaurant in my neighborhood blended the cuisines of two very different cultures, that would make me want to try it.
  2. My government should treat lives of its citizens as being much more valuable than lives in other countries.[3]
  3. I wish the world did not have nations or borders and we were all part of one big group.[4]

Do you strongly agree? Strongly disagree? What was your overall place on the liberal-conservative scale?

ArtemScaleTIMES

Regardless of your answers, the statements probably remind you of an important aspect of your daily experience. The world is divided into a diversity of groups, and they coexist in a tension between their arbitrary, often artificial, nature and the important meaning that they hold to both their own members and others. Often this division is accompanied by ethnocentrism — a favoring of the in-group at the expensive of, or sometimes with direct hostility toward, the out-group — that seems difficult to circumvent through simply expanding our moral in-group. These statements also confront you with the image of what a world without group lines might look like; would it be more cooperative or would it succumb to the egalitarian dilemma?[5]

As you know, dear reader, here at TheEGG we’ve grappled with some of these questions. Mostly by playing with the Hammond & Axelrod model of ethnocentrism (2006; also see: Hartshorn, Kaznatcheev & Shultz, 2012). Recently, Jansson’s (2015) extension of my early work on the robustness of ethnocentrism (Kaznatcheev, 2010) has motivated me to continue this thread. A couple of weeks ago I sketched how to reduce the dimensionality of the replicator equations governing tag-based games. Today, I will use this representation to look at how properties of the game affect the persistence and diversity of tags.
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Radicalization, expertise, and skepticism among doctors & engineers: the value of philosophy in education

This past Friday was a busy day for a lot of the folks in Integrated Mathematical Oncology here at the Moffitt Cancer Center. Everybody was rushing around to put the final touches on a multi-million dollar research center grant application to submit to the National Cancer Institute. Although the time was not busy for me, I still stopped by Jacob Scott’s office towards the end of the day to celebrate. Let me set the scene for you: it is a corner office down the hall from me; its many windows are scribbled over with graphs, equations, and biological interaction networks; two giant screens crowd a standing desk, and another screen is hidden in the corner; the only non-glass wall has scribbles in pencil for the carpenters: paint blackboard here. There are too many chairs — Jake is a connector, so his office is always open to guests.

A different celerbation in Jake's office. The view is from his desk towards the wall that needs to be replaced by a blackboard.

A different celerbation in Jake’s office. The view is from his desk towards the wall that needs to be replaced by a blackboard.

In addition to the scientific and administrative stress of grant-writing, Jake was also covering for his friend as the doc-of-the-day for radiation oncology. So as I rambled on: “If we consider nodes of degree three or higher in this model, we would break up contingent blocks of mutants and result in the domain of our probability distribution going from n^2 to 2^n“, scribbling more math on his wall, we would get interrupted by phone calls. His resident calling to tell him that the neurosurgeons have scheduled a consultation for an acute myeloid leukemia patient who is recovering from surgery earlier that day.

“Only on a Friday afternoon do you get this kind of consult!” Jake fires off, “He’s still in surgery! We can’t do anything for at least a few days – schedule him for Monday.”

The call was on speakerphone, but I could not keep up with the conversation. After years of training and experience, this was an effortless context-shift for Jake. He went from the heavy skepticism of a scientist staring at a blackboard to the certainty of a doctor that needed to get shit done, and back, in moments. I couldn’t imagine having this sort of confidence in my judgements, mostly because I have no training in medicine, but also because I am not expected to be certain. That is why I lean towards using abductive models versus insilications for clinial research; I have more confidence in machine learning than in my own physical and biological intuitions about cancer. Even if that approach might produce less understanding.

In recent weeks, I’ve noticed a theme in some of the (news and blog) articles I’ve been reading. In this post, I wanted to provide an annotated collection of some of these links, along with my reflections on what they say about the tension between expertise and skepticism and how that can radicalize us, both in mundane ways and in drastic ones. And what role philosophy can play in helping us cope. I will end up touching on recent events and politics as a source context, but hopefully we can keep the overall conversation more or less detached from current events.
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Emotional contagion and rational argument in philosophical texts

Last week I returned to blogging with some reflections on reading and the written word more generally. Originally, I was aiming to write a response to Roger Schank’s stance that “reading is no way to learn”, but I wandered off on too many tangents for an a single post or for a coherent argument. The tangent that I left for this post is the role of emotion and personality in philosophical texts.

In my last entry, I focused on the medium independent aspects of Schank’s argument, and identified two dimensions along which a piece of media and our engagement with it can vary: (1) passive consumption versus active participation, and (2) the level of personalization. The first continuum has a clearly better end on the side of more active engagement. If we are comparing mediums then we should prefer ones that foster more active engagement from the participants. The second dimension is more ambiguous: sometimes a more general piece of media is better than a bespoke piece. What is better becomes particularly ambiguous when being forced to adapt a general approach to your special circumstances encourages more active engagement.

In this post, I will shift focus from comparing mediums to a particular aspect of text and arguments: emotional engagement. Of course, this also shows up in other mediums, but my goal this time is not to argue across mediums.

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False memories and journalism

We like to think of ourselves as a collection of our memories, and of each memory as a snapshot of an event in our lives. Sure, we all know that our minds aren’t as sturdy as our computer’s hard-drive, so these snapshots decay over time, especially the boring ones — that’s why most of us can’t remember what we had for breakfast 12 years ago. We are even familiar with old snapshots rearranging their order and losing context, but we don’t expect to generate vivid and certain memories of events that didn’t occur. How could we have a snapshot of something that didn’t happen?

This view of memory is what makes Brian Williams’ recent fib about being on board a helicopter that was hit by two rockets and small arms fire in Iraq 12 years ago, so hard to believe. There was indeed a helicopter that was forced to land on that day, but the downed aircraft’s crew reports that Williams was actually on a helicopter about an hour behind the three that came under fire. Williams has apologized for his story, saying he conflated his helicopter with the downed one. To this, Erik Wemple voices the popular skepticism that “‘conflating’ the experience of taking incoming fire with the experience of not taking incoming fire seems verily impossible.”

But research into false memories suggests that such constructed memories as Williams’ do occur. In this post, I want to discuss these sort of false memories, share a particularly interesting example, and then discuss what this might mean for journalism.

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An approach towards ethics: neuroscience and development

For me personally it has always been a struggle, reading through all the philosophical and religious literature I have a long standing interest in, to verbalize my intuitive concept of morals in any satisfactory way. Luckily for me, once I’ve started reading up on modern psychology and neuroscience, I found out that there are empirical models based on clustering of the abundant concepts that correlate well with both our cultured intuitions and our knowledge of brain functioning. Models that are for the studies of Ethics what the Big Five traits are for personality theories or what the Cattell-Horn-Carroll theory is for cognitive abilities.  In this post I’m going to provide an account of research of what is the most elucidating level of explanation of human morals – that of neuroscience and psychology. The following is not meant as a comprehensive review, but a sample of what I consider the most useful explanatory tools. The last section touches briefly upon genetic and endocrinological component of human morals, but it is nothing more than a mention. Also, I’ve decided to omit citations in quotes, because I don’t want to include into the list of reference the research I am personally unfamiliar with.

A good place to start is Jonathan Haidt’s TED talk:

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An approach towards ethics: primate sociality

Moral decision making is one of the major torrents in human behavior. It often overrides other ways of making judgments, it generates conflicting sets of cultural values and is reinforced by them. Such conflicts might even occur in the head of some unfortunate individual, which makes the process really creative. On the other hand ethical behavior is the necessary social glue and the way people prioritize prosocial practices.

In the comments to his G+ post about Michael Sandel’s Justice course, Artem Kaznatcheev invited me to have a take on moral judgment and social emotions based on what I gathered through my readings in the recent couple of years. I’m by no means an expert in any of the fields that I touch upon in the following considerations, but I’ve been purposefully struggling with the topic due to my interest in behavioral sciences trying to come up with a lucid framework to think about the subject. Not everything I write here is backed up very well by research, mainly because I step up a little and try to see what might come next, but I’ll definitely do my best to leave my general understanding distinct from concepts prevailing in the studies I have encountered. It is not an essay on ethics per se, but rather where I am now in understanding how moral sentiments work. A remark to make is that for the purposes of that text I understand behavior broadly, e.g. thinking is a behavior.

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Truthiness of irrelevant detail in explanations from neuroscience to mathematical models

TruthinessTruthiness is the truth that comes from the gut, not books. Truthiness is preferring propositions that one wishes to be true over those known to be true. Truthiness is a wonderful commentary on the state of politics and media by a fictional character determined to be the best at feeling the news at us. Truthiness is a one word summary of emotivism.

Truthiness is a lot of things, but all of them feel far from the hard objective truths of science.

Right?

Maybe an ideal non-existent non-human Platonic capital-S Science, but at least science as practiced — if not all conceivable versions of it — is very much intertwined with politics and media. Both internal to the scientific community: how will I secure the next grant? who should I cite to please my reviewers? how will I sell this to get others reading? And external: how can we secure more funding for science? how can we better incorporate science into schools? how can we influence policy decisions? I do not want to suggest that this tangle is (all) bad, but just that it exists and is prevalent. Thus, critiques of politics and media are relevant to a scientific symposium in much the same way as they are relevant to a late-night comedy show.

I want to discuss an aspect of truthiness in science: making an explanation feel more scientific or more convincing through irrelevant detail. The two domains I will touch on is neuroscience and mathematical modeling. The first because in neuroscience I’ve been acquainted with the literature on irrelevant detail in explanations and because neuroscientific explanations have a profound effect on how we perceive mental health. The second because it is the sort of misrepresentation I fear of committing the most in my own work. I also think the second domain should matter more to the working scientist; while irrelevant neurological detail is mostly misleading to the neuroscience-naive general public, irrelevant mathematical detail can be misleading, I feel, to the mathematically-naive scientists — a non-negligible demographic.

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