Protecting Society From AI Harms: Amnesty International’s Matt Mahmoudi and Damini Satija (Part Two)

**Sam Ransbotham** (0:02)
What can corporations learn from an activist organization that works to protect people from the harms of AI? Find out on today's episode.

**Damini Satija** (0:12)
I'm Damini Satija.

**Matt Mahmoudi** (0:13)
And I'm Matt Mahmoudi from Amnesty International.

**Damini Satija** (0:16)
And you're listening to Me, Myself, and AI.

**Sam Ransbotham** (0:20)
Welcome to Me, Myself, and AI, a podcast on artificial intelligence and business. Each episode, we introduce you to someone innovating with AI.
I'm Sam Ransbotham, professor of analytics at Boston College. I'm also the AI and Business Strategy guest editor at MIT Sloan Management Review.

**Shervin Khodabandeh** (0:38)
And I'm Shervin Khodabandeh, senior partner with BCG and one of the leaders of our AI business. Together, MIT SMR and BCG have been researching and publishing on AI since 2017, interviewing hundreds of practitioners and surveying thousands of companies on what it takes to build and to deploy and scale AI capabilities and really transform the way organizations operate.

**Sam Ransbotham** (1:06)
Welcome back, everyone. On our last episode, Damini and Matt joined us to share a bit about what their organization, Amnesty Tech, is doing to combat troublesome uses of AI.
Today, we're picking up on that discussion and sharing more detail about how you can be more aware of the dangers of artificial intelligence and, importantly, how you can help. Damini, let's pick up where we left off last episode. For our listeners, we recommend you go back first and listen to our last episode, if you haven't yet, to get some more context on Amnesty Tech and the work that Matt and Damini are doing.
Damini, you and Matt were starting to talk about AI regulation and how they can help us address challenging tech problems like housing algorithms, social work, and facial recognition systems. Let's pick up from there. Can you share more about your perspective on regulating AI?

**Damini Satija** (1:55)
Regulation is a key part of the toolkit here. We're working really hard on the EU's Artificial Intelligence Act, which is right now one of the most comprehensive frameworks out there for regulating AI.
I think what's really important with regulation and what we're really missing right now is regulatory frameworks which really focus on the outcomes that we want to prevent or even promote.
And by that, what I mean is that a lot of the regulation we're seeing, even in the AI Act, which is a very advanced piece of AI regulation, but even in the case of the AI Act, is very often tied to tech hype cycles and the technology that is the hype of the moment.
And the way we've seen this really clearly with the AI Act is that in the last few weeks and months, as the conversation has really picked up around generative AI, we've seen policymakers who are deep in the AI Act, which is really in its last phases, not know how to absorb generative AI into the framework.
And I think we don't have a very robust regulatory framework if it cannot absorb a new technological development. And that's not what the goal was. In the early days, there was a lot of work done upfront with the AI Act saying we want to, quote unquote, future-proof this regulation. It will be an instrument that is ready to impose the restrictions and protections we need as the technology develops. But right now, it seems like it's not doing that. And I think that's because the regulation attempt itself is so tied to the technology hype cycle, as I say. And what we need is to be more focused on the outcomes we want to prevent. And so many of those outcomes are embedded in the way we think about human rights. So the right to non-discrimination, the right to privacy, there are certain outcomes we know we need to get to to protect human rights, regardless of what the technology we're talking about is. So that's what I would add on the regulation front and what I think is really missing right now. I'd also add to the urgency for this. Given the rapid pace of technological development, but also slightly tangentially, but algorithmic and AI and technology in general picked up in public sector environments, which is much of my focus, a lot of Matt's focus, in these restrained environments, they've been called austerity machines for that reason. And given where the world is right now in the latest stages of the pandemic, the global economy seeing multiple shocks, we can very easily anticipate that these austerity machines could become even more commonplace. And that's why this applies to AI in general, but just thinking about the area that my team works very specifically on in the welfare and social protection context, that urgency feels very dire right now. And secondly, these efficiency tools are often designed to detect or weed out fraudulent applicants for welfare and public services. So these are really punitive tools as well in the name of efficiency. That's where the disproportionate impact happens on low-income groups, communities of color, et cetera. So this entire narrative drives really harmful outcomes. We see that narrative only accelerating given the context that we're in. And so the case and the urgency for that regulation is very strong right now.