What good is all this wonderful generative AI if we can’t trust it? And by that, I don’t mean AI watermarks, certificates of authenticity, and all that technical stuff.
I’m taking a more fundamental approach right now (sorry about that)… and over the past few weeks, I’ve been delving deeply into the topic of AI from a different angle—philosophy. It’s a fascinating subject.
And I’ve come to the conclusion that we finally need to take AI out of the “technology” box. It’s not about whether AI is intelligent or “knows” anything. Rather, what’s important is that we’re already dealing today with an entire knowledge infrastructure through which knowledge is generated, verified, processed, and utilized—whether we like it or not.
We all do it. ChatGPT and similar tools churn out knowledge that sounds correct.
We must therefore focus instead on determining under what conditions human trust in AI-generated statements can be established—and when such trust is even justified.
We humans have learned to accept knowledge from credible witnesses (whether direct or indirect)—because something is in the newspaper, because a teacher told us so, because a judge ruled that way, and so on. And the intriguing question is, after all, what does it take for AI to assume such a role?
I believe that, at a minimum, the following criteria must be met:
– Users must be able to tell whether an answer is based on verifiable sources, generative reconstruction, or mere probabilistic extrapolation
– Systems must make uncertainties, limitations, and potential errors visible
– We need the ability to integrate with human review. AI responses must not be the end point, but rather the starting point for critical reflection
– Context-dependent trust in AI results is required. Different requirements apply to a recipe at home than to a scientific paper
– Trust in AI must not lead to the delegation of epistemic responsibility. The formation of convictions and decision-making must remain with humans.
It is precisely at these points that computer science and philosophy intersect. And that is both right and important! I am convinced that we need to “join forces” here, talk to one another, and learn from each other in order to develop viable solutions.
And I’m also convinced that we need to view generative AI as an epistemic infrastructure—with all that entails in terms of evidence, authority, trust, and reliability. That’s much more important than all the technical stuff. Philosophy has been dealing with this for ages, and we should make use of that in computer science (P.S.: and vice versa, by the way).
If you’re in the mood for a mental journey, I’ve written it down here: http://www.stefan-wagenpfeil.de/public/Vertrauen_im_Zeitalter_generativer_KI.pdf
And if you’re interested in an interdisciplinary exchange—you’re always welcome!