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View the page of Orly Lobel

Legal policy and scholarship are increasingly focused on regulating technology to safeguard against risks and harms, neglecting the ways in which the law should direct the use of new technology, and in particular artificial intelligence (AI), for positive purposes. Orly Lobel argues that the focus on AI wrongs is descriptively inaccurate, undermining a balanced analysis of the benefits, potential, and risks involved in digital technology.

Image courtesy of interviewee. July 25, 2024

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This video discusses the themes and arguments presented in the author's new book, "The Quality Machine," which aims to shift the narrative surrounding artificial intelligence (AI) and its integration into society. The author critiques the prevalent alarmist discourse that focuses on the risks and harms posed by AI, arguing that this type of pessimism detracts from the potential benefits of harnessing technology for positive societal impact.

The author emphasizes the need for a rational, comparative analysis of AI, urging that discussions should not only highlight its flaws but also consider how AI can improve upon existing systems. They argue that AI's capacity for learning and improvement, especially when compared to human cognitive biases, presents an opportunity for progress rather than fear.

Additionally, the author calls for more balanced regulations and policy interventions that facilitate the use of AI for socioeconomic benefit, rather than simply limiting its deployment due to potential risks. They stress the importance of public discourse and the role of policymakers in guiding responsible AI development, ensuring that technology serves the public good and promotes equity and safety.

Key themes include the promotion of AI's positive potential, the risks of technology being viewed through an overly critical lens, the importance of empirical evidence in policy-making, and the call for collaborative efforts to engage all stakeholders in shaping the future of AI. The overall goal is to encourage a future that leverages AI to enhance various aspects of human life while addressing its challenges effectively.

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X-TIMESTAMP-MAP:LOCAL=00:00:00.000,MPEGTS=900000

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I wrote The Quality Machine, my new book,

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harnessing Digital Technology for a better, brighter Future.

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As a response to this tunnel vision

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discussion, or what I call a very skewed conversation

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and debate that we've been having about,

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human machine future, about the future of ai

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and how it's affecting every aspect of our lives.

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Uh, what I argue is that we are indeed at the cusp

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of a, a huge revolution.

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Uh, every, human activity work,

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family, our, health, our

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intimate relations, they're all being affected by the

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rapid integration of algorithms

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and machine learning into our decision making processes,

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into, just our, our sorting

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and what we know about ourselves, about the world.

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Um, but what we've been seeing is a lot

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of alarmist discussions, a lot of alarmist reporting

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by, journalists.

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Um, and also that kind of trickles into the policies

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that we're seeing on both sides

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of the pond all over the world, in the EU

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and, here in the United States, where there is a,

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a skewed focus on AI as wrongs, as harms, as risks,

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as fails.

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And what I argue in both, my new article, the Law of AI

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for Good, and in my new book, the Equality Machine, is that

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absolutely undeniably there are, wrongs,

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there are risks, but the way that we need

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to analyze what's happening has to be much more rational,

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much more, comparative, not kind

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of in absolute terms of, you know, how AI doesn't do well.

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We need to compare it to the status quo and,

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and what, you know, what, needs to be fixed,

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what is imperfect right now.

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And, also celebrate and really discover and,

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and, develop a vision of

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how do we harness this technology for good, for, more

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equality for all the values

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that we want in a democratic society.

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And there is vast potential,

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but if we're kind of just focusing on the dark side,

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it's almost like a self-fulfilling prophecy that we get,

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very, fearful, alarmed,

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and disengaged with the potential.

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There is kind of this pendulum in the way

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that we think about technology in general, where,

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there is a tendency to,

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either be overly optimistic

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and have this kind of very rosy, you know, kind

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of blind trust, and maybe that was, true

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for a few decades ago.

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But then what I call the tech lash, I, I show that

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it starts from, specific disenchantment

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and specific, uncovering of various

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activities and,

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and wrongs that were promulgated

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by certain big tech corporations,

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which are, you know, absolutely

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something that we need to think about.

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Uh, you know, has Facebook done, you know, things

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that it should not have done.

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Um, you know, how do we think about

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what the algorithms prioritize,

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or how do we design, robots embodied

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AI in, in, a gendered way or,

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or not, you know, using stereotypes or not.

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And I, I, I go through so much of that in, in the book,

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but what I, show is that

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at some point we kind of delink that those specific,

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concerns about specific fails of, of,

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of certain corporations.

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And it becomes this mindset where we are,

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overly distrusting of anything that has to do

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with technology and specifically

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with artificial intelligence.

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And we kind of adopt a mindset of simply like,

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you know, stopping everything disengaging

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and, preferring, just, you know, the,

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the status quo over, what is inevitably becoming,

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you know, very important in our lives.

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There is, something that I've found very frustrating,

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particularly with, reporters, with the media,

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is this rehashing of the same exact stories

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of an AI fail that are no longer relevant

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or representative of what the technology actually is.

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So I'll give you two examples.

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Um, one,

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and I think these examples should be familiar

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to our audience because they are so rehashed

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and, you know, so kind

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of coming up all the time when you know every other,

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media story about, you know, how we should fear ai,

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brings this up.

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Um, so the first is how,

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probably a decade ago at this point, there was a finding

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that, facial recognition technology,

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was doing better when it comes to white male faces

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and doing worse when it had to screen,

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for, people of color or women.

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Um, that's absolutely,

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A big problem has to be corrected, has to be addressed.

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But in fact, it has been addressed in the newest

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technologies, and it's actually on the spectrum of problems.

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It's not a hard problem.

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It, it, it has to do with how much data

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and how representative the data, is

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that the algorithm is trained upon.

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And so you just simply have to, you know, feed it more,

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rather than less and, and make sure that it, you know,

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and audit it and, and make sure that it's not in,

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having this biased performance

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and across different demographics.

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But then what, what the problem is that, you know,

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that we're not updating, this is kind of the, the,

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the thinking about machines a static.

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We're just kind of, we being, different audiences,

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including policymakers, including regulators

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that have interacted with, but,

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but certainly, you know, the public, you know,

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just readers, businesses, they kind

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of hear these stories and they say,

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we can't use facial recreation.

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'cause it is, per se, you know, inherently biased,

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and it just doesn't do well, that, you know,

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that's, that's simply not true.

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So we have to keep, you know, insisting on,

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updating our critique or our concerns to

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what is actually the technology that's available right now.

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And I know it's hard because there, it's, you know,

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so rapidly developing, but, we need to remember,

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and this is kind of a message in the equality machine

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and the law of AI for good, that, machine learning,

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you know, as, as the term suggests, it has the potential

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to learn and to improve.

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And in fact, if you think about it comparatively, we know

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that it has more potential to improve than our cognition.

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So again, I kind of take that, adopt that comparative lens

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where, in my research, I do a lot

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of behavioral research.

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I collaborate with social psychologists, with

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business school professors, and we're very, you know,

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well aware of the, our cognitive biases.

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So we document irrationalities

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and the limits, of our human cognition.

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Um, I teach discrimination law and policy.

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I, I serve and as an expert witness in discrimination cases,

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and with discrimination, you know, and,

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and human bias, it's very hard for us as humans to de-bias,

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even when we're kind of shown that we

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tend to have certain biases.

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A lot of them are unconscious.

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It's very hard to learn, to improve, compare that

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to the potential of kind of really auditing

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and detecting problems in the, in an algorithm

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and then improving it.

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We really need to insist on, you know,

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that comparative advantage not only in its kind

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of static form of, you know, the potential

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or the, the, the current state of the technology,

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but also its potential to, grow and,

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and become better and more accurate.

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We have, this, expectation

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that we won't find any problems in AI

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or algorithmic decision making,

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or automation that we don't have from human behavior.

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And so, another example, I mentioned

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that there's several examples, of like, stories

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that are rehashed,

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and are the, don't kind of match up to where we are

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right now in 2024, or where we will be in 2026.

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Um, another example is,

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self-driving cars.

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Um, we a have a tendency to do

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what you were just mentioning of kind of require perfection

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for, from an automated system

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that we don't require from humans.

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So in the example of driving, I think is very,

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easy to explain.

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And once we kind of, you know, insist on this, people,

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can grasp this, you know, much, much more clearly.

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Uh, it's not enough to just be aware, made aware that

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in one instance,

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that a autonomous vehicle was deployed on the roads,

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it got into an accident.

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What we need to ask is not whether it's perfect

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and, you know, a hundred percent safe,

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but whether it's safer than the human driver.

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And we have the data, this is an empirical question,

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and the data will change from year to year,

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depending on the state of the technology.

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But we need to look at that data and,

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and insist on the comparative advantage,

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not insist on perfection.

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So this is, specifically a, an FTC report that came out,

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a couple of years ago where, Congress,

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the United States Congress asked the Federal Trade

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Commission to investigate

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and report on how can we use AI

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to tackle online harms.

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And the report for me is really paradigmatic of the kind of,

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irrational

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and kind of skewed aversion of AI

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that I see in a lot of other, policy fields and,

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and debates, where the FTC

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doesn't adopt a comparative lens.

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Um, it doesn't think, you know, kind of take the data or,

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or look empirically at,

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how our human moderators, outperforming

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or underperforming automated content moderators.

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And, you know, what, what is, what is available?

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What are the tools that are out there in the industry?

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What are companies doing? What are the best practices?

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What are bad practices? Doesn't do any of that.

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What it does is, go to kind of the same usual,

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a lot of them outdated suspects of,

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here are instances where AI didn't get it right,

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or AI was limited in its, ability,

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or presented some, you know, wrongs or risks.

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Um, it cites those,

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it says the technology is too rudimentary.

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There are all these, hypothetical, you know,

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sometimes panned out risks.

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Um, so Congress do nothing and that do nothing.

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Status quo approach is something that concerns me

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because I, I never want to, kind of

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call for, you know, just adoption for the sake of adoption.

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I'm not, I don't, have I particular,

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you know, stake

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or stance on, whether we should shift in any per,

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you know, one specific circumstance

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or context from, human decision making

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to automated decision making.

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But what I insist on is that we run through what we know

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what the empirics are

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and analyze this rationally rather than, just kind of

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having, you know, what we might call the precautionary,

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default of if we don't know enough, we do nothing.

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Uh, I think it's just not helpful,

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and it's also just not, not correct

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in this particular context.

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'cause we do know enough.

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The reality is that every Fortune 500 company

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is automating, certainly with online harms.

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Um, the, you know, every major platform,

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social media platform, e-commerce

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platform is using automated systems

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to do content moderation to, detect

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illegal, illegal activities and all that.

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I actually happen to be very familiar with some of these

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because I, consult a lot of these, you know, major,

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companies.

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And what I argue about the policy is that

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perversely and counter productively when, the FTC

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as a major regulator, you know, that's impacting the world,

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says, we don't know.

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So, you know, we won't say anything.

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Um, it's too risky when, when they do that perversely,

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we get a lot of the snake oil, the bad instances of ai,

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out there competing equally with the better, systems,

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the, the better services

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and products that, are out there in the for-profit,

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you know, in the, in the private industry.

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Whereas I, I argue that the role of

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Policymakers of regulators is to sort out

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and give us, you know, tools to differentiate

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and to say like, this is the gold standard right now for

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adopting a tool that's automated content moderation.

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And, you know, this is how we should, perform audits

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and, and regulate the market.

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Uh, and, and so, so I, I never wanna be heard

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or read as, somebody who's averse to regulation.

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Quite the contrary, the law of AI for good

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is actually an argument,

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for more government government intervention,

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but government intervention that's not, kind

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of all about banning certain technologies,

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but actually sorting out

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and, propelling the more effective technologies,

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the role of, public entities,

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which has always been the role of public entities, to,

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aid, consumers

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and, you know, private corporations in,

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choosing, selecting

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and, deploying, you know, the best practices,

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that, that are available.

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Uh, and this is especially important for,

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the smaller companies

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and, the, you know, lay consumers,

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because I think that the, the larger

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companies like Google will already, you know, know how to

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sort the different, you know, products available or,

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or they will de develop things

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that internally and audit them.

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But, if we want to have competition, which contributes

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to consumer welfare, which consumer contributes

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to innovation, then it is the role

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of regulators to say,

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just like they're doing an environmental, regulation

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or occupational safety regulation

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or transportation regulation, here's the gold standard.

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Like, you know, you need a a, a safety,

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system for, for this kind of, vehicle.

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Uh, you need to have a seatbelt in every car.

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Um, tell us

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after, you,

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the regulator has undertaken some empirical study or,

17:20.225 --> 17:21.765
or looked at the empirical studies

17:21.865 --> 17:24.045
or commission, you know,

17:24.045 --> 17:28.005
and outsourced some of that work, tell us what works,

17:28.465 --> 17:31.165
invest, procure,

17:31.715 --> 17:35.845
incentivize the private sector to develop,

17:36.105 --> 17:39.045
to improve what works,

17:39.425 --> 17:42.885
and what best aligns with our public interests.

17:43.065 --> 17:46.645
So increasing health, increasing access, increasing safety,

17:46.645 --> 17:50.805
increasing equity and equality across sectors.

17:51.265 --> 17:54.205
Um, really kind of having a public looking,

17:54.385 --> 17:58.365
not just a for-profit looking view of how to deploy

17:58.365 --> 18:01.365
that technology and, and develop these standards.

18:01.425 --> 18:03.765
And that, that has always been the role of regulation,

18:03.865 --> 18:07.805
and we're not doing enough of that now that, you know,

18:08.065 --> 18:11.205
AI is, is accelerating and, and shaping our lives.

18:11.705 --> 18:14.125
And, you know, some of this, i I wanna say kind

18:14.125 --> 18:16.445
of cautiously that I understand that it's,

18:16.505 --> 18:19.285
that's a tough thing when, you know things are moving

18:19.385 --> 18:21.445
so fast and they require knowledge

18:21.445 --> 18:25.405
and skill that are not always available to, regulators

18:25.405 --> 18:28.045
that have not been trained in machine learning

18:28.305 --> 18:31.525
and, you know, don't maybe see themselves as expert.

18:32.305 --> 18:35.045
Um, and there is, I think that lag

18:35.715 --> 18:40.325
that always exists in some, shape and

18:40.325 --> 18:43.205
or form, with, you know, the public

18:44.435 --> 18:47.245
regulator always kind of catching up to the private market,

18:47.585 --> 18:49.725
but I think now it's even more pronounced.

18:50.385 --> 18:53.525
We need to accelerate the investment in our,

18:53.815 --> 18:58.525
skill creation in hiring more, professionals in,

18:58.945 --> 19:03.165
the public sector that understand the technology so

19:03.165 --> 19:06.685
that we can do more of that work of harnessing AI for good.

19:07.465 --> 19:10.805
Um, one huge risk is,

19:11.195 --> 19:13.085
increased market concentration.

19:13.465 --> 19:18.325
And so we're seeing that already with, market dominance.

19:18.585 --> 19:20.205
Uh, what I mentioned

19:20.205 --> 19:21.565
before is that the,

19:21.785 --> 19:24.045
the bigger companies will do thi these kind

19:24.045 --> 19:25.805
of things anyway.

19:26.105 --> 19:30.565
Um, so we have, you know, open AI and we have, Google

19:30.865 --> 19:33.805
and Microsoft, all kind of racing ahead.

19:34.505 --> 19:39.005
Um, but the smaller kind of new entry, fresh blood,

19:39.505 --> 19:42.685
more competition over, you know, pricing

19:43.225 --> 19:46.285
and consumer welfare, we'll see less of that

19:46.355 --> 19:50.765
because we don't have the, the public arm to offset

19:51.115 --> 19:55.485
that disparity in abilities to, to sort to, to develop.

19:56.225 --> 19:57.885
Um, so that's one risk.

19:58.145 --> 20:02.405
Um, and so it's kind of another, way

20:02.405 --> 20:04.765
of thinking about competition law

20:04.765 --> 20:05.965
and antitrust policy

20:06.185 --> 20:10.685
to think about investment in technology as a public good.

20:11.135 --> 20:15.005
Technology is not just a private tool for, you know, the

20:15.965 --> 20:18.325
strongest companies to profit from.

20:18.665 --> 20:23.365
It really is a public good that we need to leverage for,

20:23.625 --> 20:24.965
the welfare of everybody.

20:24.985 --> 20:28.245
And that, again, that's the role of, the regulator.

20:29.265 --> 20:33.805
Um, the other thing is that we will see you just more fails,

20:34.025 --> 20:38.485
because, you know, if we don't,

20:38.985 --> 20:43.125
insist on, the fact

20:43.125 --> 20:46.605
that technology can actually correct for stereotypes for,

20:47.065 --> 20:49.925
for example, in, in the book,

20:49.925 --> 20:54.175
in the equality machine, i, I go through, a lot

20:54.175 --> 20:58.455
of the kind of stereotypes that have been injected into,

20:58.995 --> 21:03.295
personal digital assistance, to,

21:04.195 --> 21:07.295
robots that are designed with like our very like,

21:07.415 --> 21:09.375
gendered way of thinking.

21:09.525 --> 21:13.695
Like, so, so the private sector that's not, you know,

21:13.795 --> 21:18.215
too interested in public looking values will likely have

21:18.285 --> 21:22.255
like a, robot

21:22.255 --> 21:25.935
that is a doctor designed as a male and,

21:26.115 --> 21:27.775
and a nurse doctor,

21:28.035 --> 21:32.255
or sorry, nurse robot, you know, as, as a, as a female,

21:32.555 --> 21:35.335
you know, you, you're see, starting to see all those kind

21:35.335 --> 21:37.935
of like, stereotypes that are

21:38.675 --> 21:42.255
age old just being replicated into our,

21:43.175 --> 21:44.535
embodied ai.

21:45.235 --> 21:50.055
Um, but a public, looking, you know,

21:50.605 --> 21:51.815
role is, or,

21:52.075 --> 21:56.055
or, action by, by agencies can be

21:56.395 --> 22:00.895
to develop these kind of unexpected surprising,

22:01.635 --> 22:03.775
if stereotype flipping

22:03.955 --> 22:07.755
or resisting, access or,

22:07.775 --> 22:09.675
or availability of, of these tools.

22:10.415 --> 22:13.755
Um, other examples, and,

22:13.775 --> 22:16.395
and, you know, you might wanna ask me about this

22:16.395 --> 22:20.675
or you, you may have wanted to ask me about this, in,

22:20.735 --> 22:23.275
in next questions, but in the law of AI for good,

22:24.035 --> 22:28.595
I talk about how, another fallacy is,

22:28.935 --> 22:32.115
our over privileging of the value of privacy

22:32.855 --> 22:35.115
versus other, values.

22:36.095 --> 22:40.325
And, all along, you know, in, in all of my research,

22:40.485 --> 22:42.845
I I very much care about privacy.

22:43.365 --> 22:45.885
I think it's, it, it is an important democratic value.

22:46.045 --> 22:50.645
I think we should be concerned about, you know, over,

22:51.005 --> 22:53.365
surveillance and misuse of information.

22:54.025 --> 22:58.165
Um, but I also show that

22:59.075 --> 23:03.045
it's one of many other democratic values that we have

23:03.345 --> 23:08.245
and goals and norms that we, have always,

23:08.515 --> 23:09.925
strived for in a democracy.

23:10.905 --> 23:14.575
And that, if we

23:15.285 --> 23:19.415
just have kind of that lead of, okay, now that we have ai,

23:19.415 --> 23:22.135
there's even a greater risk that our privacy will be,

23:22.675 --> 23:24.135
infringe upon.

23:24.675 --> 23:29.335
So do not collect, do not misuse, do not, there's a kind

23:29.335 --> 23:34.215
of data minimization at, at the eu, GDPR

23:34.315 --> 23:36.415
and the eu, AI act.

23:36.415 --> 23:40.055
This kind of a very like controlling lens of,

23:40.615 --> 23:43.535
minimize the data that's collecting, if that's the kind

23:43.535 --> 23:48.365
of single dominant, rule, then there,

23:48.785 --> 23:50.365
and, and it's not contextualized

23:50.365 --> 23:53.965
and not offset by other, significant

23:54.585 --> 23:55.965
values and interests.

23:56.025 --> 24:00.525
Um, for example, having, representative data sets,

24:01.985 --> 24:06.935
completing missing, information and missing data.

24:07.355 --> 24:09.815
Uh, for example, in, in our health data.

24:10.075 --> 24:12.135
Uh, I have a whole chapter in the equality machine

24:12.135 --> 24:14.855
that starts from this understanding

24:14.955 --> 24:19.175
that's very well documented in the empirical research on

24:19.235 --> 24:24.095
how, clinical trials and, medical device

24:24.195 --> 24:27.615
and pharma, development has have,

24:27.925 --> 24:31.055
have long had this problem of basing a lot

24:31.055 --> 24:35.375
of the development on just kind of the skewed partial data

24:35.555 --> 24:38.095
of what, the white, you know,

24:38.765 --> 24:41.415
male physique biology, is.

24:41.955 --> 24:46.455
Um, now we have this opportunity to race ahead with,

24:47.155 --> 24:50.575
AI finding more patterns across different,

24:51.895 --> 24:56.455
databases that exist in, in hospitals and clinics and,

24:56.955 --> 25:01.295
and finding, you know, what works, what,

25:01.295 --> 25:04.375
what heals what, what is better patient care for all.

25:04.955 --> 25:08.935
Um, so, so again, what we need to kind of insist on is

25:09.325 --> 25:10.535
what are our public goals

25:10.675 --> 25:14.135
and how do we get there with the help of private, public,

25:14.515 --> 25:16.055
regulation.

25:16.785 --> 25:21.095
First and foremost, I want us to all be part

25:21.095 --> 25:22.975
of the conversation in the equality machine.

25:23.055 --> 25:24.975
I use the term skin in the game.

25:25.255 --> 25:27.775
I think everybody, all of us

25:27.775 --> 25:29.975
around the world should have skin in the game,

25:30.235 --> 25:34.775
in envisioning, in designing, in deploying, in,

25:35.025 --> 25:38.335
benefiting from our newfound capabilities

25:38.635 --> 25:40.215
of artificial intelligence.

25:40.715 --> 25:42.655
And so we can only do that.

25:43.435 --> 25:45.975
We actually have rational conversations,

25:46.415 --> 25:50.295
informed conversations about what it is that we want

25:50.315 --> 25:53.255
and what it is that we have now and,

25:53.395 --> 25:55.415
and can have, in the future.

25:56.275 --> 26:00.015
Um, and, I think that, you know,

26:00.885 --> 26:05.215
when we have, these much more balanced conversations

26:05.715 --> 26:09.215
and examples of all the wonderful ways

26:09.285 --> 26:13.815
that AI can actually assist our lives, then that's when,

26:14.235 --> 26:18.295
things start moving along to, you know, the better

26:18.315 --> 26:20.135
and brighter future that I envision.

00:00 - I wrote The Quality Machine, my new book, harnessing Digital Technology for a better, brighter Future. As a response to this tunnel vision discussion, or what I call a very skewed conversation and debate that we've been having about, human machine future, about the future of ai

00:23 - and how it's affecting every aspect of our lives. what I argue is that we are indeed at the cusp of a, a huge revolution. every, human activity work, family, our, health, our intimate relations, they're all being affected by the rapid integration of algorithms and machine learning into our decision making processes, into, just our, our sorting and what we know about ourselves, about the world.

01:02 - but what we've been seeing is a lot of alarmist discussions, a lot of alarmist reporting by, journalists. and also that kind of trickles into the policies that we're seeing on both sides of the pond all over the world, in the EU and, here in the United States, where there is a, a skewed focus on AI as wrongs, as harms, as risks, as fails. And what I argue in both, my new article, the Law of AI

01:37 - for Good, and in my new book, the Equality Machine, is that absolutely undeniably there are, wrongs, there are risks, but the way that we need to analyze what's happening has to be much more rational, much more, comparative, not kind of in absolute terms of, you know, how AI doesn't do well. We need to compare it to the status quo and, and what, you know, what, needs to be fixed, what is imperfect right now. And, also celebrate and really discover and,

02:14 - and, develop a vision of how do we harness this technology for good, for, more equality for all the values that we want in a democratic society. And there is vast potential, but if we're kind of just focusing on the dark side, it's almost like a self-fulfilling prophecy that we get, very, fearful, alarmed, and disengaged with the potential. There is kind of this pendulum in the way

02:46 - that we think about technology in general, where, there is a tendency to, either be overly optimistic and have this kind of very rosy, you know, kind of blind trust, and maybe that was, true for a few decades ago. But then what I call the tech lash, I, I show that it starts from, specific disenchantment and specific, uncovering of various activities and,

03:21 - and wrongs that were promulgated by certain big tech corporations, which are, you know, absolutely something that we need to think about. you know, has Facebook done, you know, things that it should not have done. you know, how do we think about what the algorithms prioritize, or how do we design, robots embodied AI in, in, a gendered way or,

03:50 - or not, you know, using stereotypes or not. And I, I, I go through so much of that in, in the book, but what I, show is that at some point we kind of delink that those specific, concerns about specific fails of, of, of certain corporations. And it becomes this mindset where we are, overly distrusting of anything that has to do with technology and specifically with artificial intelligence.

04:27 - And we kind of adopt a mindset of simply like, you know, stopping everything disengaging and, preferring, just, you know, the, the status quo over, what is inevitably becoming, you know, very important in our lives. There is, something that I've found very frustrating, particularly with, reporters, with the media, is this rehashing of the same exact stories of an AI fail that are no longer relevant or representative of what the technology actually is.

05:06 - So I'll give you two examples. one, and I think these examples should be familiar to our audience because they are so rehashed and, you know, so kind of coming up all the time when you know every other, media story about, you know, how we should fear ai, brings this up. so the first is how, probably a decade ago at this point, there was a finding

05:36 - that, facial recognition technology, was doing better when it comes to white male faces and doing worse when it had to screen, for, people of color or women. that's absolutely, A big problem has to be corrected, has to be addressed. But in fact, it has been addressed in the newest technologies, and it's actually on the spectrum of problems. It's not a hard problem. It, it, it has to do with how much data

06:12 - and how representative the data, is that the algorithm is trained upon. And so you just simply have to, you know, feed it more, rather than less and, and make sure that it, you know, and audit it and, and make sure that it's not in, having this biased performance and across different demographics. But then what, what the problem is that, you know, that we're not updating, this is kind of the, the, the thinking about machines a static.

06:43 - We're just kind of, we being, different audiences, including policymakers, including regulators that have interacted with, but, but certainly, you know, the public, you know, just readers, businesses, they kind of hear these stories and they say, we can't use facial recreation. 'cause it is, per se, you know, inherently biased, and it just doesn't do well, that, you know, that's, that's simply not true.

07:13 - So we have to keep, you know, insisting on, updating our critique or our concerns to what is actually the technology that's available right now. And I know it's hard because there, it's, you know, so rapidly developing, but, we need to remember, and this is kind of a message in the equality machine and the law of AI for good, that, machine learning, you know, as, as the term suggests, it has the potential to learn and to improve. And in fact, if you think about it comparatively, we know

07:44 - that it has more potential to improve than our cognition. So again, I kind of take that, adopt that comparative lens where, in my research, I do a lot of behavioral research. I collaborate with social psychologists, with business school professors, and we're very, you know, well aware of the, our cognitive biases. So we document irrationalities and the limits, of our human cognition. I teach discrimination law and policy.

08:17 - I, I serve and as an expert witness in discrimination cases, and with discrimination, you know, and, and human bias, it's very hard for us as humans to de-bias, even when we're kind of shown that we tend to have certain biases. A lot of them are unconscious. It's very hard to learn, to improve, compare that to the potential of kind of really auditing and detecting problems in the, in an algorithm and then improving it.

08:47 - We really need to insist on, you know, that comparative advantage not only in its kind of static form of, you know, the potential or the, the, the current state of the technology, but also its potential to, grow and, and become better and more accurate. We have, this, expectation that we won't find any problems in AI or algorithmic decision making, or automation that we don't have from human behavior.

09:22 - And so, another example, I mentioned that there's several examples, of like, stories that are rehashed, and are the, don't kind of match up to where we are right now in 2024, or where we will be in 2026. another example is, self-driving cars. we a have a tendency to do what you were just mentioning of kind of require perfection for, from an automated system

09:54 - that we don't require from humans. So in the example of driving, I think is very, easy to explain. And once we kind of, you know, insist on this, people, can grasp this, you know, much, much more clearly. it's not enough to just be aware, made aware that in one instance, that a autonomous vehicle was deployed on the roads, it got into an accident. What we need to ask is not whether it's perfect

10:24 - and, you know, a hundred percent safe, but whether it's safer than the human driver. And we have the data, this is an empirical question, and the data will change from year to year, depending on the state of the technology. But we need to look at that data and, and insist on the comparative advantage, not insist on perfection. So this is, specifically a, an FTC report that came out, a couple of years ago where, Congress,

10:53 - the United States Congress asked the Federal Trade Commission to investigate and report on how can we use AI to tackle online harms. And the report for me is really paradigmatic of the kind of, irrational and kind of skewed aversion of AI that I see in a lot of other, policy fields and, and debates, where the FTC doesn't adopt a comparative lens.

11:26 - it doesn't think, you know, kind of take the data or, or look empirically at, how our human moderators, outperforming or underperforming automated content moderators. And, you know, what, what is, what is available? What are the tools that are out there in the industry? What are companies doing? What are the best practices? What are bad practices? Doesn't do any of that. What it does is, go to kind of the same usual, a lot of them outdated suspects of,

12:03 - here are instances where AI didn't get it right, or AI was limited in its, ability, or presented some, you know, wrongs or risks. it cites those, it says the technology is too rudimentary. There are all these, hypothetical, you know, sometimes panned out risks. so Congress do nothing and that do nothing. Status quo approach is something that concerns me because I, I never want to, kind of

12:38 - call for, you know, just adoption for the sake of adoption. I'm not, I don't, have I particular, you know, stake or stance on, whether we should shift in any per, you know, one specific circumstance or context from, human decision making to automated decision making. But what I insist on is that we run through what we know what the empirics are and analyze this rationally rather than, just kind of

13:12 - having, you know, what we might call the precautionary, default of if we don't know enough, we do nothing. I think it's just not helpful, and it's also just not, not correct in this particular context. 'cause we do know enough. The reality is that every Fortune 500 company is automating, certainly with online harms. the, you know, every major platform, social media platform, e-commerce

13:47 - platform is using automated systems to do content moderation to, detect illegal, illegal activities and all that. I actually happen to be very familiar with some of these because I, consult a lot of these, you know, major, companies. And what I argue about the policy is that perversely and counter productively when, the FTC as a major regulator, you know, that's impacting the world, says, we don't know.

14:22 - So, you know, we won't say anything. it's too risky when, when they do that perversely, we get a lot of the snake oil, the bad instances of ai, out there competing equally with the better, systems, the, the better services and products that, are out there in the for-profit, you know, in the, in the private industry. Whereas I, I argue that the role of Policymakers of regulators is to sort out and give us, you know, tools to differentiate

14:56 - and to say like, this is the gold standard right now for adopting a tool that's automated content moderation. And, you know, this is how we should, perform audits and, and regulate the market. and, and so, so I, I never wanna be heard or read as, somebody who's averse to regulation. Quite the contrary, the law of AI for good is actually an argument, for more government government intervention, but government intervention that's not, kind

15:31 - of all about banning certain technologies, but actually sorting out and, propelling the more effective technologies, the role of, public entities, which has always been the role of public entities, to, aid, consumers and, you know, private corporations in, choosing, selecting and, deploying, you know, the best practices, that, that are available.

16:06 - and this is especially important for, the smaller companies and, the, you know, lay consumers, because I think that the, the larger companies like Google will already, you know, know how to sort the different, you know, products available or, or they will de develop things that internally and audit them. But, if we want to have competition, which contributes to consumer welfare, which consumer contributes

16:39 - to innovation, then it is the role of regulators to say, just like they're doing an environmental, regulation or occupational safety regulation or transportation regulation, here's the gold standard. Like, you know, you need a a, a safety, system for, for this kind of, vehicle. you need to have a seatbelt in every car. tell us after, you,

17:16 - the regulator has undertaken some empirical study or, or looked at the empirical studies or commission, you know, and outsourced some of that work, tell us what works, invest, procure, incentivize the private sector to develop, to improve what works, and what best aligns with our public interests. So increasing health, increasing access, increasing safety, increasing equity and equality across sectors.

17:51 - really kind of having a public looking, not just a for-profit looking view of how to deploy that technology and, and develop these standards. And that, that has always been the role of regulation, and we're not doing enough of that now that, you know, AI is, is accelerating and, and shaping our lives. And, you know, some of this, i I wanna say kind of cautiously that I understand that it's, that's a tough thing when, you know things are moving so fast and they require knowledge

18:21 - and skill that are not always available to, regulators that have not been trained in machine learning and, you know, don't maybe see themselves as expert. and there is, I think that lag that always exists in some, shape and or form, with, you know, the public regulator always kind of catching up to the private market, but I think now it's even more pronounced. We need to accelerate the investment in our, skill creation in hiring more, professionals in,

18:58 - the public sector that understand the technology so that we can do more of that work of harnessing AI for good. one huge risk is, increased market concentration. And so we're seeing that already with, market dominance. what I mentioned before is that the, the bigger companies will do thi these kind of things anyway. so we have, you know, open AI and we have, Google

19:30 - and Microsoft, all kind of racing ahead. but the smaller kind of new entry, fresh blood, more competition over, you know, pricing and consumer welfare, we'll see less of that because we don't have the, the public arm to offset that disparity in abilities to, to sort to, to develop. so that's one risk. and so it's kind of another, way of thinking about competition law and antitrust policy

20:06 - to think about investment in technology as a public good. Technology is not just a private tool for, you know, the strongest companies to profit from. It really is a public good that we need to leverage for, the welfare of everybody. And that, again, that's the role of, the regulator. the other thing is that we will see you just more fails, because, you know, if we don't, insist on, the fact that technology can actually correct for stereotypes for,

20:47 - for example, in, in the book, in the equality machine, i, I go through, a lot of the kind of stereotypes that have been injected into, personal digital assistance, to, robots that are designed with like our very like, gendered way of thinking. Like, so, so the private sector that's not, you know, too interested in public looking values will likely have like a, robot that is a doctor designed as a male and,

21:26 - and a nurse doctor, or sorry, nurse robot, you know, as, as a, as a female, you know, you, you're see, starting to see all those kind of like, stereotypes that are age old just being replicated into our, embodied ai. but a public, looking, you know, role is, or, or, action by, by agencies can be to develop these kind of unexpected surprising,

22:01 - if stereotype flipping or resisting, access or, or availability of, of these tools. other examples, and, and, you know, you might wanna ask me about this or you, you may have wanted to ask me about this, in, in next questions, but in the law of AI for good, I talk about how, another fallacy is, our over privileging of the value of privacy versus other, values.

22:36 - And, all along, you know, in, in all of my research, I I very much care about privacy. I think it's, it, it is an important democratic value. I think we should be concerned about, you know, over, surveillance and misuse of information. but I also show that it's one of many other democratic values that we have and goals and norms that we, have always, strived for in a democracy. And that, if we

23:15 - just have kind of that lead of, okay, now that we have ai, there's even a greater risk that our privacy will be, infringe upon. So do not collect, do not misuse, do not, there's a kind of data minimization at, at the eu, GDPR and the eu, AI act. This kind of a very like controlling lens of, minimize the data that's collecting, if that's the kind of single dominant, rule, then there, and, and it's not contextualized

23:50 - and not offset by other, significant values and interests. for example, having, representative data sets, completing missing, information and missing data. for example, in, in our health data. I have a whole chapter in the equality machine that starts from this understanding that's very well documented in the empirical research on how, clinical trials and, medical device and pharma, development has have,

24:27 - have long had this problem of basing a lot of the development on just kind of the skewed partial data of what, the white, you know, male physique biology, is. now we have this opportunity to race ahead with, AI finding more patterns across different, databases that exist in, in hospitals and clinics and, and finding, you know, what works, what, what heals what, what is better patient care for all. so, so again, what we need to kind of insist on is

25:09 - what are our public goals and how do we get there with the help of private, public, regulation. First and foremost, I want us to all be part of the conversation in the equality machine. I use the term skin in the game. I think everybody, all of us around the world should have skin in the game, in envisioning, in designing, in deploying, in, benefiting from our newfound capabilities

25:38 - of artificial intelligence. And so we can only do that. We actually have rational conversations, informed conversations about what it is that we want and what it is that we have now and, and can have, in the future. and, I think that, you know, when we have, these much more balanced conversations and examples of all the wonderful ways that AI can actually assist our lives, then that's when,

26:14 - things start moving along to, you know, the better and brighter future that I envision.

Faculti AI can make mistakes