The MIT Media Lab controversy and getting back to ‘radical courage’, with Media Lab student Arwa Mboya – gpgmail


People win prestigious prizes in tech all the time, but there is something different about The Bold Prize. Unless you’ve been living under a literal or proverbial rock, you’ve probably heard something about the late Jeffrey Epstein, a notorious child molester and human trafficker who also happened to be a billionaire philanthropist and managed to become a ubiquitous figure in certain elite science and tech circles.

And if you’re involved in tech, the rock you’ve been living under would have had to be fully insulated from the internet to avoid reading about Epstein’s connections with MIT’s Media Lab, a leading destination for the world’s most brilliant technological minds, also known as “the future factory.” 

This past week, conversations around the Media Lab were hotter than the fuel rods at Fukushima, as The New Yorker’s Ronan Farrow, perhaps the most feared and famous investigative journalist in America today, blasted out what for some were new revelations that Bill Gates, among others, had given millions of dollars to the Media Lab at Jeffrey (no fucking relation, thank you very much!) Epstein’s behest. Hours after Farrow’s piece was published, Joi Ito, the legendary but now embattled Media Lab director, resigned.

But well before before Farrow weighed in or Ito stepped away, students, faculty, and other leaders at MIT and far beyond were already on full alert about this story, thanks in large part to Arwa Michelle Mboya, a graduate student at the Media Lab, from Kenya by way of college at Yale, where she studied economics and filmmaking and learned to create virtual reality. Mboya, 25, was among the first public voices (arguably the very first) to forcefully and thoughtfully call on Ito to step down from his position.

Imagine: you’re heading into the second year of your first graduate degree, and you find yourself taking on a man who, when Barack Obama took over Wired magazine for an issue as guest editor, was one of just a couple of people the then sitting President of the United States asked to personally interview. And imagine that man was the director of your graduate program, and the reason you decided to study in it in the first place.

Imagine the pressure involved, the courage required. And imagine, soon thereafter, being completely vindicated and celebrated for your actions. 

Arwa Mboya. Image via MIT Media Lab

That is precisely the journey that Arwa Mboya has been on these past few weeks, including when human rights technologist Sabrina Hersi Issa decided to crowd-fund the Bold Prize to honor Mboya’s courage, which has now brought in over $10,000 to support her ongoing work (full disclosure: I am among the over 120 contributors to the prize).

Mboya’s advocacy was never about Joi Ito personally. If you get to know her through the interview below, in fact, you’ll see she doesn’t wish him ill.

As she wrote in MIT’s The Tech nine days before Farrow’s essay and ten before Ito’s resignation, “This is not an MIT issue, and this is not a Joi Ito issue. This is an international issue where a global network of powerful individuals have used their influence to secure their privilege at the expense of women’s bodies and lives. The MIT Media Lab was nicknamed “The Future Factory” on CBS’s 60 Minutes. We are supposed to reflect the future, not just of technology but of society. When I call for Ito’s resignation, I’m fighting for the future of women.”

From the moment I read it, I thought this was a beautiful and truly bold statement by a student leader who is an inspiring example of the extraordinary caliber of student that the Media Lab draws.

But in getting to know her a bit since reading it, I’ve learned that her message is also about even more. It’s about the fact that the women and men who called for a new direction in light of Jeffrey Epstein’s abuses and other leaders’ complicity did so in pursuit of their own inspiring dreams for a better world.

Arwa, as you’ll see below, spoke out at MIT because of her passion to use tech to inspire radical imagination among potentially millions of African youth. As she discusses both the Media Lab and her broader vision, I believe she’s already beginning to provide that inspiration. 

Greg Epstein: You have had a few of the most dramatic weeks of any student I’ve met in 15 years as a chaplain at two universities. How are you doing right now?

Arwa Mboya: I’m actually pretty good. I’m not saying that for the sake of saying. I have a great support network. I’m in a lab where everyone is amazing. I’m very tired, I’ll say that. I’ve been traveling a lot and dealing with this while still trying to focus on writing a thesis. If anything, it’s more like overwhelmed and exhausted as opposed to not doing well in and of itself.

Epstein: Looking at your writing — you’ve got a great Medium blog that you started long before MIT and maintained while you’ve been here — it struck me that in speaking your mind and heart about this Media Lab issue, you’ve done exactly what you set out to do when you came here. You set out to be brave, to live life, as the Helen Keller quote on your website says, as either a great adventure or nothing. 

Also, when you came to the Media Lab, you were the best-case scenario for anyone who works on publicizing this place. You spoke and wrote about the Lab as your absolute dream. When you were in Africa, or Australia, or at Yale, how did you come to see this as the best place in the world for you to express the creative and civic dreams that you had?

Mboya: That’s a good question — what drew me here? The Media Lab is amazing. I read Whiplash, which is Joi Ito’s book about the nine principles of the Media Lab, and it really resonated with me. It was a place for misfits. It was a place for people who are curious and who just want to explore and experiment and mix different fields, which is exactly what I’ve been doing before.

From high school, I was very narrow in my focus; at Yale I did Econ and film, so that had a little more edge. After I graduated I insisted on not taking a more conventional path many students from Yale take, so [I] moved back to Kenya and worked on many different projects, got into adventure sports, got into travel more.

Epstein: Your website is full of pictures of you flipping over, skydiving, gymnastics — things that require both strength and courage. 

Mboya: I’d always been an athlete, loved the outdoors.

I remember being in Vietnam; I’d never done a backflip. I was like, “Okay, I’m going to learn how to do this.” But it’s really scary jumping backwards; the fear. Is, you can’t see where you’re going. I remember telling myself, ” Okay, just jump over the fear. Just shut it off and do it. Your body will follow.” I did and I was like, “Oh, that was easy.” It’s not complicated. Most people could do it if they just said, “Okay, I’ll jump.”

It really stuck with me. A lot of decisions I’ve [since] made, that I’m scared of, I think, “Okay, just jump, and your body will follow.” The Media Lab was like that as well.

I really wanted to go there, I just didn’t think there was a place for me. It was like, I’m not techie enough, I’m not anything enough. Applying was, ’just jump,’ you never know what will happen.

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Image from Arwa Mboya

Epstein: Back when you were applying, you wrote about experiencing what applicants to elite schools often call “imposter syndrome.” This is where I want to be, but will they want me?

Mboya: Exactly.




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The risks of amoral A.I. – gpgmail


Artificial intelligence is now being used to make decisions about lives, livelihoods, and interactions in the real world in ways that pose real risks to people.

We were all skeptics once. Not that long ago, conventional wisdom held that machine intelligence showed great promise, but it was always just a few years away. Today there is absolute faith that the future has arrived.

It’s not that surprising with cars that (sometimes and under certain conditions) drive themselves and software that beats humans at games like chess and Go. You can’t blame people for being impressed.

But board games, even complicated ones, are a far cry from the messiness and uncertainty of real-life, and autonomous cars still aren’t actually sharing the road with us (at least not without some catastrophic failures).

AI is being used in a surprising number of applications, making judgments about job performance, hiring, loans, and criminal justice among many others. Most people are not aware of the potential risks in these judgments. They should be. There is a general feeling that technology is inherently neutral — even among many of those developing AI solutions. But AI developers make decisions and choose tradeoffs that affect outcomes. Developers are embedding ethical choices within the technology but without thinking about their decisions in those terms.

These tradeoffs are usually technical and subtle, and the downstream implications are not always obvious at the point the decisions are made.

The fatal Uber accident in Tempe, Arizona, is a (not-subtle) but good illustrative example that makes it easy to see how it happens.

The autonomous vehicle system actually detected the pedestrian in time to stop but the developers had tweaked the emergency braking system in favor of not braking too much, balancing a tradeoff between jerky driving and safety. The Uber developers opted for the more commercially viable choice. Eventually autonomous driving technology will improve to a point that allows for both safety and smooth driving, but will we put autonomous cars on the road before that happens? Profit interests are pushing hard to get them on the road immediately.

Physical risks pose an obvious danger, but there has been real harm from automated decision-making systems as well. AI does, in fact, have the potential to benefit the world. Ideally, we mitigate for the downsides in order to get the benefits with minimal harm.

A significant risk is that we advance the use of AI technology at the cost of reducing individual human rights. We’re already seeing that happen. One important example is that the right to appeal judicial decisions is weakened when AI tools are involved. In many other cases, individuals don’t even know that a choice not to hire, promote, or extend a loan to them was informed by a statistical algorithm. 

Buyer Beware

Buyers of the technology are at a disadvantage when they know so much less about it than the sellers do. For the most part decision makers are not equipped to evaluate intelligent systems. In economic terms, there is an information asymmetry that puts AI developers in a more powerful position over those who might use it. (Side note: the subjects of AI decisions generally have no power at all.) The nature of AI is that you simply trust (or not) the decisions it makes. You can’t ask technology why it decided something or if it considered other alternatives or suggest hypotheticals to explore variations on the question you asked. Given the current trust in technology, vendors’ promises about a cheaper and faster way to get the job done can be very enticing.

So far, we as a society have not had a way to assess the value of algorithms against the costs they impose on society. There has been very little public discussion even when government entities decide to adopt new AI solutions. Worse than that, information about the data used for training the system plus its weighting schemes, model selection, and other choices vendors make while developing the software are deemed trade secrets and therefore not available for discussion.

Image via Getty Images / sorbetto

The Yale Journal of Law and Technology published a paper by Robert Brauneis and Ellen P. Goodman where they describe their efforts to test the transparency around government adoption of data analytics tools for predictive algorithms. They filed forty-two open records requests to various public agencies about their use of decision-making support tools.

Their “specific goal was to assess whether open records processes would enable citizens to discover what policy judgments these algorithms embody and to evaluate their utility and fairness”. Nearly all of the agencies involved were either unwilling or unable to provide information that could lead to an understanding of how the algorithms worked to decide citizens’ fates. Government record-keeping was one of the biggest problems, but companies’ aggressive trade secret and confidentiality claims were also a significant factor.

Using data-driven risk assessment tools can be useful especially in cases identifying low-risk individuals who can benefit from reduced prison sentences. Reduced or waived sentences alleviate stresses on the prison system and benefit the individuals, their families, and communities as well. Despite the possible upsides, if these tools interfere with Constitutional rights to due process, they are not worth the risk.

All of us have the right to question the accuracy and relevance of information used in judicial proceedings and in many other situations as well. Unfortunately for the citizens of Wisconsin, the argument that a company’s profit interest outweighs a defendant’s right to due process was affirmed by that state’s supreme court in 2016.

Fairness is in the Eye of the Beholder

Of course, human judgment is biased too. Indeed, professional cultures have had to evolve to address it. Judges for example, strive to separate their prejudices from their judgments, and there are processes to challenge the fairness of judicial decisions.

In the United States, the 1968 Fair Housing Act was passed to ensure that real-estate professionals conduct their business without discriminating against clients. Technology companies do not have such a culture. Recent news has shown just the opposite. For individual AI developers, the focus is on getting the algorithms correct with high accuracy for whatever definition of accuracy they assume in their modeling.

I recently listened to a podcast where the conversation wondered whether talk about bias in AI wasn’t holding machines to a different standard than humans—seeming to suggest that machines were being put at a disadvantage in some imagined competition with humans.

As true technology believers, the host and guest eventually concluded that once AI researchers have solved the machine bias problem, we’ll have a new, even better standard for humans to live up to, and at that point the machines can teach humans how to avoid bias. The implication is that there is an objective answer out there, and while we humans have struggled to find it, the machines can show us the way. The truth is that in many cases there are contradictory notions about what it means to be fair.

A handful of research papers have come out in the past couple of years that tackle the question of fairness from a statistical and mathematical point-of-view. One of the papers, for example, formalizes some basic criteria to determine if a decision is fair.

In their formalization, in most situations, differing ideas about what it means to be fair are not just different but actually incompatible. A single objective solution that can be called fair simply doesn’t exist, making it impossible for statistically trained machines to answer these questions. Considered in this light, a conversation about machines giving human beings lessons in fairness sounds more like theater of the absurd than a purported thoughtful conversation about the issues involved.

Image courtesy of gpgmail/Bryce Durbin

When there are questions of bias, a discussion is necessary. What it means to be fair in contexts like criminal sentencing, granting loans, job and college opportunities, for example, have not been settled and unfortunately contain political elements. We’re being asked to join in an illusion that artificial intelligence can somehow de-politicize these issues. The fact is, the technology embodies a particular stance, but we don’t know what it is.

Technologists with their heads down focused on algorithms are determining important structural issues and making policy choices. This removes the collective conversation and cuts off input from other points-of-view. Sociologists, historians, political scientists, and above all stakeholders within the community would have a lot to contribute to the debate. Applying AI for these tricky problems paints a veneer of science that tries to dole out apolitical solutions to difficult questions. 

Who Will Watch the (AI) Watchers?

One major driver of the current trend to adopt AI solutions is that the negative externalities from the use of AI are not borne by the companies developing it. Typically, we address this situation with government regulation. Industrial pollution, for example, is restricted because it creates a future cost to society. We also use regulation to protect individuals in situations where they may come to harm.

Both of these potential negative consequences exist in our current uses of AI. For self-driving cars, there are already regulatory bodies involved, so we can expect a public dialog about when and in what ways AI driven vehicles can be used. What about the other uses of AI? Currently, except for some action by New York City, there is exactly zero regulation around the use of AI. The most basic assurances of algorithmic accountability are not guaranteed for either users of technology or the subjects of automated decision making.

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Image via Getty Images / nadia_bormotova

Unfortunately, we can’t leave it to companies to police themselves. Facebook’s slogan, “Move fast and break things” has been retired, but the mindset and the culture persist throughout Silicon Valley. An attitude of doing what you think is best and apologizing later continues to dominate.

This has apparently been effective when building systems to upsell consumers or connect riders with drivers. It becomes completely unacceptable when you make decisions affecting people’s lives. Even if well-intentioned, the researchers and developers writing the code don’t have the training or, at the risk of offending some wonderful colleagues, the inclination to think about these issues.

I’ve seen firsthand too many researchers who demonstrate a surprising nonchalance about the human impact. I recently attended an innovation conference just outside of Silicon Valley. One of the presentations included a doctored video of a very famous person delivering a speech that never actually took place. The manipulation of the video was completely imperceptible.

When the researcher was asked about the implications of deceptive technology, she was dismissive of the question. Her answer was essentially, “I make the technology and then leave those questions to the social scientists to work out.” This is just one of the worst examples I’ve seen from many researchers who don’t have these issues on their radars. I suppose that requiring computer scientists to double major in moral philosophy isn’t practical, but the lack of concern is striking.

Recently we learned that Amazon abandoned an in-house technology that they had been testing to select the best resumes from among their applicants. Amazon discovered that the system they created developed a preference for male candidates, in effect, penalizing women who applied. In this case, Amazon was sufficiently motivated to ensure their own technology was working as effectively as possible, but will other companies be as vigilant?

As a matter of fact, Reuters reports that other companies are blithely moving ahead with AI for hiring. A third-party vendor selling such technology actually has no incentive to test that it’s not biased unless customers demand it, and as I mentioned, decision makers are mostly not in a position to have that conversation. Again, human bias plays a part in hiring too. But companies can and should deal with that.

With machine learning, they can’t be sure what discriminatory features the system might learn. Absent the market forces, unless companies are compelled to be transparent about the development and their use of opaque technology in domains where fairness matters, it’s not going to happen.

Accountability and transparency are paramount to safely using AI in real-world applications. Regulations could require access to basic information about the technology. Since no solution is completely accurate, the regulation should allow adopters to understand the effects of errors. Are errors relatively minor or major? Uber’s use of AI killed a pedestrian. How bad is the worst-case scenario in other applications? How are algorithms trained? What data was used for training and how was it assessed to determine its fitness for the intended purpose? Does it truly represent the people under consideration? Does it contain biases? Only by having access to this kind of information can stakeholders make informed decisions about appropriate risks and tradeoffs.

At this point, we might have to face the fact that our current uses of AI are getting ahead of its capabilities and that using it safely requires a lot more thought than it’s getting now.


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