I was going through a shelf of books I haven’t visited in a couple of decades and found a book I used in 1986 when I taught Introduction to Computer Science in my last year as a philosophy professor. (It’s a long story.) Ethical Issues in the Use of Computers was a handy anthology, edited by Deborah G. Johnson and John W. Snapper (Wadsworth, 1985).

So what were the ethical issues posed by digital tech back then?

The first obvious point is that back then ethics were ethics: codes of conduct promulgated by professional societies. So, Part I consists of eight essays on “Codes of Conduct for the Computer Professions.” All but two of the articles present the codes for various computing associations. The two stray sheep are “The Quest for a Code of Professional Ethics: An Intellectual and Moral Confusion” (John Ladd) and “What Should Professional Societies do About Ethics?” (Fay H. Sawyier).

Part 2 covers “Issues of Responsibility”, with most of the articles concerning themselves with liability issues. The last article, by James Moor, ventures wider, asking “Are There Decisions Computers Should Not Make?” About midway through, he writes:

“Therefore, the issue is not whether there are some limitations to computer decision-making but how well computer decision making compares with human decision making.” (p. 123)

While saluting artificial intelligence researchers for their enthusiasm, Moor says “…at this time the results of their labors do not establish that computers will one day match or exceed human levels of ability for most kinds of intellectual activities.” Was Moor right? It depends. First define basically everything.

Moor concedes that Hubert Dreyfus’ argument (What Computers Still Can’t Do) that understanding requires a contextual whole has some power, but points to effective expert systems. Overall, he leaves open the question whether computers will ever match or exceed human cognitive abilities.

After talking about how to judge computer decisions, and forcefully raising Joseph Weizenbaum’s objection that computers are alien to human life and thus should not be allowed to make decisions about that life, Moor lays out some guidelines, concluding that we need to be pragmatic about when and how we will let computers make decisions:

“First, what is the nature of the computer’s competency and how has it been demonstrated? Secondly given our basic goals and values why is it better to use a computer decision maker in a particular situation than a human decision maker?”

We are still asking these questions.

Part 3 is on “Privacy and Security.” Four of the seven articles can be considered to be general introductions fo the concept of privacy. Apparently privacy was not as commonly discusssed back then.

Part 4, “Computers and Power,” suddenly becomes more socially aware. It includes an excerpt from Weizenbaum’s Computer Power and Human Reason, as well as articles on “Computers and Social Power” and “Peering into the Poverty Gap.”

Part 5 is about the burning issue of the day: “Software as Property.” One entry is the Third Circuit Court of Appeals finding in Apple vs. Franklin Computer. Franklin’s Ace computer contained operating system code that had been copied from Apple. The Court knew this because in addition to the programs being line-by-line copies, Franklin failed to remove the name of one of the Apple engineers that the engineer had embedded in the program. Franklin acknowledged the copying but argued that operating system code could not be copyrighted.

That seems so long ago, doesn’t it?

Because this post mentions Joseph Weizenbaum, here’s the beginning of a blog post from 2010:

I just came across a 1985 printout of notes I took when I interviewed Prof. Joseph Weizenbaum in his MIT office for an article that I think never got published. (At least Google and I have no memory of it.) I’ve scanned it in; it’s a horrible dot-matrix printout of an unproofed semi-transcript, with some chicken scratches of my own added. I probably tape recorded the thing and then typed it up, for my own use, on my KayPro.

In it, he talks about AI and ethics in terms much more like those we hear today. He was concerned about its use by the military especially for autonomous weapons, and raised issues about the possible misuse of visual recognition systems. Weizenbaum was both of his time and way ahead of it.

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Daily Nous has run a guest editorial by C. Thi Nguyen defending “public philosophy.” Yes! In fact, it’s telling that public philosophy even needs defense. And defense from whom?

Here’s a pull quote from the last paragraph:

To speak bluntly: the world is in crisis. It’s war, the soul of humanity is at stake, and the discipline that has been in isolation training for 2000 years for this very moment is too busy pointing out tiny errors in each other’s technique to actually join the fight.

And this is from near the beginning:

We need to fill the airwaves with the Good Stuff, in every form: op-eds, blog posts, YouTube videos, podcasts, long-form articles, lectures, forums, Tweets, and more. Good philosophy needs to be everywhere, accessible to every level, to anybody who might be interested. We need to flood the world with gateways of every shape and size.

So, yes, of course!

Who then is Dr. Nguyen arguing against? Who does not support increasing the presence of public philosophy?

Answer: The bulk of the article in fact outlines what we have to do in order to get the profession of philosophy to accept public philosopher as an activity worth recognizing, rewarding, and promoting.

If that op-ed is a manifesto (it is), sign me up!

[Disclosure: I am an ex-academic philosophy professor whose writings sometimes impinge on actual philosophy.]

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I’ve written before about the need to teach The Kids (also: all of us) not only how to think critically so we can see what we should not believe, but also how to come to belief. That piece, which I now cannot locate, was prompted by danah boyd’s excellent post on the problem with media literacy. Robert Berkman, Outreach, Business Librarian at the University of Rochester and Editor of The Information Advisor’s Guide to Internet Research, asked me how one can go about teaching people how to come to belief. Here’s an edited version of my reply:

I’m afraid I don’t have a good answer. I actually haven’t thought much about how to teach people how to come to belief, beyond arguing for doing this as a social process (the ol’ “knowledge is a network” argument :) I have a pretty good sense of how *not* to do it: the way philosophy teachers relentlessly show how every proposed position can be torn down.

I wonder what we’d learn by taking a literature course as a model — not one that is concerned primarily with critical method, but one that is trying to teach students how to appreciate literature. Or art. The teacher tries to get the students to engage with one another to find what’s worthwhile in a work. Formally, you implicitly teach the value of consistency, elegance of explanation, internal coherence, how well a work clarifies one’s own experience, etc. Those are useful touchstones for coming to belief.

I wouldn’t want to leave students feeling that it’s up to them to come up with an understanding on their own. I’d want them to value the history of interpretation, bringing their critical skills to it. The last thing we need is to make people feel yet more unmoored.

I’m also fond of the orthodox Jewish way of coming to belief, as I, as a non-observant Jew, understand it. You have an unchanging and inerrant text that means nothing until humans interpret it. To interpret it means to be conversant with the scholarly opinions of the great Rabbis, who disagree with one another, often diametrically. Formulating a belief in this context means bringing contemporary intelligence to a question while finding support in the old Rabbis…and always always talking respectfully about those other old Rabbis who disagree with your interpretation. No interpretations are final. Learned contradiction is embraced.

That process has the elements I personally like (being moored to a tradition, respecting those with whom one disagrees, acceptance of the finitude of beliefs, acceptance that they result from a social process), but it’s not going to be very practical outside of Jewish communities if only because it rests on the acceptance of a sacred document, even though it’s one that literally cannot be taken literally; it always requires interpretation.

My point: We do have traditions that aim at enabling us to come to belief. Science is one of them. But there are others. We should learn from them.

TL;DR: I dunno.

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“If a lion could talk, we could not understand him.”
— Ludwig Wittgenstein, Philosophical Investigations, 1953.

“If an algorithm could talk, we could not understand it.”
— Deep learning, Now.

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An earlier draft of Descartes’ Meditations has been discovered, which will inevitably lead to a new round of unfunny jokes under the rubric of “Descartes’ First Draft.” I can’t wait :(

The draft is a big discovery. Camilla Shumaker at Research Frontiers reports that Jeremy Hyman, a philosophy instructor at the University of Arkansas, came across a reference to the manuscript and hied off to a municipal library in Toulouse … a gamble, but he apparently felt he had nothing left Toulouse.

And so it begins…

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I’m at a two-day inter-disciplinary workshop on “Trustworthy Algorithmic Decision-Making” put on by the National Science Foundation and Michigan State University. The 2-page whitepapers
from the participants are online. (Here’s mine.) I may do some live-blogging of the workshops.

Goals:

– Key problems and critical qustionos?

– What to tell pol;icy-makers and others about the impact of these systems?

– Product approaches?

– What ideas, people, training, infrastructure are needed for these approaches?

Excellent diversity of backgrounds: CS, policy, law, library science, a philosopher, more. Good diversity in gender and race. As the least qualified person here, I’m greatly looking forward to the conversations.

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Inspired by fabulously wrong paint colors that Janelle Shane’s generated by running existing paint names through a machine learning system, and then by an hilarious experiment in dog breed names by my friend Matthew Battles, I decided to run some data through a beginner’s machine learning algorithm by karpathy.

I fed a list of cocktail names in as data to an unaltered copy of karpathy’s code. After several hundred thousand iterations, here’s a highly curated list of results:

Then I added to the original list of cocktails a list of Western philosophers. After about 1.4 million iterations, here’s a curated list:

Yes, we need not worry about human bartenders, cocktail designers, or philosophers being replaced by this particular algorithm. On the other hand, this is algorithm consists of a handful of lines of code and was applied blindly by a person dumber than it. Presumably SkyNet — or the next version of Microsoft Clippy — will be significantly more sophisticated than that.

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John McCumber — a grad school colleague with whom I have alas not kept up — has posted at Aeon an insightful historical argument that America’s default philosophy came about because of a need to justify censoring American communist professorss (resulting in a naive scientism) and a need to have a positive alternative to Marxism (resulting in the adoption of rational choice theory).

That compressed summary does not do justice to the article’s grounding in the political events of the 1950s nor to how well-written and readable it is.

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“Any sufficiently advanced technology is indistinguishable from magic,” said Arthur C. Clarke famously.

It is also the case that any sufficiently advanced technology is indistinguishable from prejudice.

Especially if that technology is machine learning. ML creates algorithms to categorize stuff based upon data sets that we feed it. Say “These million messages are spam, and these million are not,” and ML will take a stab at figuring out what are the distinguishing characteristics of spam and not spam, perhaps assigning particular words particular weights as indicators, or finding relationships between particular IP addresses, times of day, lenghts of messages, etc.

Now complicate the data and the request, run this through an artificial neural network, and you have Deep Learning that will come up with models that may be beyond human understanding. Ask DL why it made a particular move in a game of Go or why it recommended increasing police patrols on the corner of Elm and Maple, and it may not be able to give an answer that human brains can comprehend.

We know from experience that machine learning can re-express human biases built into the data we feed it. Cathy O’Neill’s Weapons of Math Destruction contains plenty of evidence of this. We know it can happen not only inadvertently but subtly. With Deep Learning, we can be left entirely uncertain about whether and how this is happening. We can certainly adjust DL so that it gives fairer results when we can tell that it’s going astray, as when it only recommends white men for jobs or produces a freshman class with 1% African Americans. But when the results aren’t that measurable, we can be using results based on bias and not know it. For example, is anyone running the metrics on how many books by people of color Amazon recommends? And if we use DL to evaluate complex tax law changes, can we tell if it’s based on data that reflects racial prejudices?[1]

So this is not to say that we shouldn’t use machine learning or deep learning. That would remove hugely powerful tools. And of course we should and will do everything we can to keep our own prejudices from seeping into our machines’ algorithms. But it does mean that when we are dealing with literally inexplicable results, we may well not be able to tell if those results are based on biases.

In short: Any sufficiently advanced technology is indistinguishable from prejudice.[2]

[1] We may not care, if the result is a law that achieves the social goals we want, including equal and fair treatment of tax players regardless of race.

[2] Please note that that does not mean that advanced technology is prejudiced. We just may not be able to tell.

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I’m at a day-long conference/meet-up put on by the Berkman Klein Center‘s and MIT Media Lab‘s “AI for the Common Good” project.

Jonathan Zittrain gives an opening talk. Since we’re meeting at Harvard Law, JZ begins by recalling the origins of what has been called “cyber law,” which has roots here. Back then, the lawyers got to the topic first, and thought that they could just think their way to policy. We are now at another signal moment as we are in a frenzy of building new tech. This time we want instead to involve more groups and think this through. [I am wildly paraphrasing.]

JZ asks: What is it that we intuitively love about human judgment, and are we willing to insist on human judgments that are worse than what a machine would come up with? Suppose for utilitarian reasons we can cede autonomy to our machines — e.g., autonomous cars — shouldn’t we? And what do we do about maintaining local norms? E.g., “You are now entering Texas where your autonomous car will not brake for pedestrians.”

“Should I insist on being misjudged by a human judge because that’s somehow artesinal?” when, ex hypothesis, an AI system might be fairer.

Autonomous systems are not entirely new. They’re bringing to the fore questions that have always been with us. E.g., we grant a sense of discrete intelligence to corporations. E.g., “McDonald’s is upset and may want to sue someone.”

[This is a particularly bad representation of JZ’s talk. Not only is it wildly incomplete, but it misses the through-line and JZ’s wit. Sorry.]

Lightning Talks

Finale Doshi-Velez is particularly interested in interpretable machine learning (ML) models. E.g., suppose you have ten different classifiers that give equally predictive results. Should you provide the most understandable, all of them…?

Why is interpretability so “in vogue”? Suppose non-interpretable AI can do something better? In most cases we don’t know what “better” means. E.g., someone might want to control her glucose level, but perhaps also to control her weight, or other outcomes? Human physicians can still see things that are not coded into the model, and that will be the case for a long time. Also, we want systems that are fair. This means we want interpretable AI systems.

How do we formalize these notions of interpretability? How do we do so for science and beyond? E.g., what is a legal “right to explanation
” mean? She is working with Sam Greshman on how to more formally ground AI interpretability in the cognitive science of explanation.

Vikash Mansinghka leads the eight-person Probabilistic Computing project at MIT. They want to build computing systems that can be our partners, not our replacements. We have assumed that the measure of success of AI is that it beats us at our own game, e.g., AlphaGo, Deep Blue, Watson playing Jeopardy! But games have clearly measurable winners.

His lab is working on augmented intelligence that gives partial solutions, guidelines and hints that help us solve problems that neither system could solve on their own. The need for these systems are most obvious in large-scale human interest projects, e.g., epidemiology, economics, etc. E.g., should a successful nutrition program in SE Asia be tested in Africa too? There are many variables (including cost). BayesDB, developed by his lab, is “augmented intelligence for public interest data science.”

Traditional computer science, computing systems are built up from circuits to algorithms. Engineers can trade off performance for interpretability. Probabilisitic systems have some of the same considerations. [Sorry, I didn’t get that last point. My fault!]

John Palfrey is a former Exec. Dir. of BKC, chair of the Knight Foundation (a funder of this project) and many other things. Where can we, BKC and the Media Lab, be most effective as a research organization? First, we’ve had the most success when we merge theory and practice. And building things. And communicating. Second, we have not yet defined the research question sufficiently. “We’re close to something that clearly relates to AI, ethics and government” but we don’t yet have the well-defined research questions.

The Knight Foundation thinks this area is a big deal. AI could be a tool for the public good, but it also might not be. “We’re queasy” about it, as well as excited.

Nadya Peek is at the Media Lab and has been researching “macines that make machines.” She points to the first computer-controlled machine (“Teaching Power Tools to Run Themselves“) where the aim was precision. People controlled these CCMs: programmers, CAD/CAM folks, etc. That’s still the case but it looks different. Now the old jobs are being done by far fewer people. But the spaces between doesn’t always work so well. E.g., Apple can define an automatiable workflow for milling components, but if you’re student doing a one-off project, it can be very difficult to get all the integrations right. The student doesn’t much care about a repeatable workflow.

Who has access to an Apple-like infrastructure? How can we make precision-based one-offs easier to create? (She teaches a course at MIT called “How to create a machine that can create almost anything.”)

Nathan Mathias, MIT grad student with a newly-minted Ph.D. (congrats, Nathan!), and BKC community member, is facilitating the discussion. He asks how we conceptualize the range of questions that these talks have raised. And, what are the tools we need to create? What are the social processes behind that? How can we communicate what we want to machines and understand what they “think” they’re doing? Who can do what, where that raises questions about literacy, policy, and legal issues? Finally, how can we get to the questions we need to ask, how to answer them, and how to organize people, institutions, and automated systems? Scholarly inquiry, organizing people socially and politically, creating policies, etc.? How do we get there? How can we build AI systems that are “generative” in JZ’s sense: systems that we can all contribute to on relatively equal terms and share them with others.

Nathan: Vikash, what do you do when people disagree?

Vikash: When you include the sources, you can provide probabilistic responses.

Finale: When a system can’t provide a single answer, it ought to provide multiple answers. We need humans to give systems clear values. AI things are not moral, ethical things. That’s us.

Vikash: We’ve made great strides in systems that can deal with what may or may not be true, but not in terms of preference.

Nathan: An audience member wants to know what we have to do to prevent AI from repeating human bias.

Nadya: We need to include the people affected in the conversations about these systems. There are assumptions about the independence of values that just aren’t true.

Nathan: How can people not close to these systems be heard?

JP: Ethan Zuckerman, can you respond?

Ethan: One of my colleagues, Joy Buolamwini, is working on what she calls the Algorithmic Justice League, looking at computer vision algorithms that don’t work on people of color. In part this is because the tests use to train cv systems are 70% white male faces. So she’s generating new sets of facial data that we can retest on. Overall, it’d be good to use test data that represents the real world, and to make sure a representation of humanity is working on these systems. So here’s my question: We find co-design works well: bringing in the affected populations to talk with the system designers?

[Damn, I missed Yochai Benkler‘s comment.]

Finale: We should also enable people to interrogate AI when the results seem questionable or unfair. We need to be thinking about the proccesses for resolving such questions.

Nadya: It’s never “people” in general who are affected. It’s always particular people with agendas, from places and institutions, etc.

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