Building Trustworthy Open Source AI - Sebnem Erener (EP 38)
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What does βtrustβ mean in the context of AI and why is it important?
My guest today is Sepnum Arener. She's a leading expert on frontier AI governance and policy, having worked at Klarna as their head of AI legal, regulatory and ethics before she now turned into an independent AI governance expert with focus on AI regulations in Europe. We discussed her recent research on open source AI, what it can and cannot do. by providing models openly, like the weights and the data available, and why it doesn't necessarily make it fully transparent or trustworthy. Hi Eva. Thanks
for having me. I'm looking forward to our conversation. And uh
Our conversation today is amongst others about the background on open source, but also on particularly the legal and ethics perspective, because you've been working in that field for many years and you also think a lot about how to trust technology. And so I wanna start with you at the intersection of it. And what's the first thing that comes to your mind when you think about tech and trust?
Hmm. Thanks for that question. When I think about trust, what strikes me usually is how many distinct meanings the word trust has in the context of technology. So I often try to determine like what meaning are we using it. For example, in the engineering sense, trust means reliability, dependability. Like it's about does this system do what it claims to do? It's very straightforward. In the security or like cryptology sense, it's about mathematical proofs. It has nothing to do with human confidence. It's it's actually the paradigm is zero trust. It's the philosophy is never trust, always verify. So it's the rejection of trust. And then like when we say I trust this company with my data, like we don't actually trust someone.
What we trust is it's politics. Policies, its incentives, the accountability mechanisms, it's the institutional trust. There's a certain type of trust that I'm really unsure of like how it will go. It's about what the technology tells us. Does it tell us the truth? It's the epistemic trust. I mean, it's relevant with news platforms, search engines, but also more and more with AI and like LLMs. And there has been like throughout About the technological development, there's been responses to this. Like Wikipedia has verifiability norms, like journalism has source checking and there's academic citation system, but AI is now challenging all of these. So we don't really know how it will go. But I think I care about, yes, trust is important, but I care about trustworthiness more than trust.
And in Instead of like trying like w to trust technology or asking like, you know, citizens to trust technology more, I'm really seeking like the technology and the institutions behind it to be more trustworthy. So for me these two words belong together. And When
we now look into ways of how to achieve that, one of the perspectives that you bring to the conversation is also open source. And particularly for AI, you focused on the topic for a long time and you also highlight how much of a critical issue it is to provide that, particularly from a European perspective. Uh can you iterate a bit and tell us about the paper that you've been working on and the focus on your topic and how come that you think open source AI is connected to trustworthiness and trust overall?
Yes, I I do believe like open source AI is an important pillar of pluralism and inclusivity. And I've been yeah, the the paper I wrote is not published yet, but it's about governance of open source AI intermediaries. So I I I looked into the EU AI Act closure and AI Act sets up a tiered set of obligations for frontier Models, it's called general purpose AI models. And it actually carves out certain exemptions for open source models. Like if a if a provider publishes a model with an open license and don't monetize it and makes the weights of that model public, it's actually exempt from certain duties. And this is in theory logical because if you're already publishing your weights and and some of the training data, then it opens it for inspection, any additional duties or you know it creates compliance burden.
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Chapters
3 chapters
1
What does βtrustβ mean in the context of AI and why is it important?
0:06β12:19
2
How does an openβsource AI model travel through five or six different hands before reaching users?
12:19β23:08
3
Why does openness sometimes make accountability harder for regulators and companies?
23:08β23:36
Speakers
1 identifiedMore from The In-Between Tech and Trust Podcast
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