Uppsala Vienna AI Colloquium

Does consciousness require biology or can systems made out of other materials be conscious? I develop an argument for the view that it is (nomologically) possible that some non-biological creatures are conscious, including conventional, silicon-based AI systems. It assumes the iterative natural kind (INK) strategy, according to which one should investigate consciousness by treating it as a natural kind which iteratively explains observable patterns and correlations between potentially consciousness-relevant features. The argument is based on the insight that we can already anticipate that future developments would give us reasons to attribute consciousness to some non-biological creatures. According to the argument, an idealized scientific investigation – based on the INK strategy – would deliver the result that some possible non-biological creatures are conscious, and the outcome of such an ideal application corresponds to what is actually the case. My argument for the former premise is based on the claim that theoretical virtues and pre-theoretical principles support attributing consciousness to psychological duplicates, i.e., non-biological, silicon-based creatures which share the coarse-grained functional organization of humans.

The question whether a machine – a computer, a robot or any other form of artificial system – could be sentient is certainly entertaining, no end of science fiction deals with the question and sometimes very engagingly. But why is the question of artificial sentience (or “awareness”, or “consciousness”) raised in science and why invest public funding in this research? Is conscious AI at all possible, or even desirable?

Here is a very popular view on what user rational trust in AI requires: The Explanation View of AI Trust: User rational trust in AI requires an explanation of why the AI has reached the conclusion it has. We think that The Explanation View of AI Trust is wrong. It’s not true of trust in general that rational trust (even typically) requires understanding why, and it’s not the case that AI communication generates any special normative requirement that there should be an explanation why that grounds rational trust. This doesn’t mean that we think there is nothing to be gained by XAI—we prefer explainability all else being equal! But understanding how to increase trust (when appropriate) in AI requires the right diagnosis. In order to understand how to increase trust in AI, we think it’s better to focus not on AI explainability but instead on AI trustworthiness. That is, in this talk I will defend what we call the Simple View: The Simple View of AI Trust: User rational trust in AI requires AI trustworthiness.

From the perspective of artificial intelligence application, law is at a crossroad—on the one hand, it is an ideal application because it is entirely based on written text and therefore suitable for methods based on LLMs. On the other hand, however, law is bound by a number of legal, ethical, and other rules for its application, and the use of artificial intelligence sheds new light on these rules. I will present this paradox of the legal domain using the example of the Constitutional Court case law search engine that I am developing. The key question here is: how should assistance tools for legal practice work in order to be effective and lead to more accessible law, but at the same time not pose a risk to the protection of fundamental rights?

Consuming information online today means dealing with AI-powered technologies from filter algorithms to deepfakes on a daily basis. The bells of the so-called infopocalypse or epistemic apocalypse have been sounded and are being discussed both inside and outside academic settings with claims that the inherent opacity of these technologies could harm our epistemic practices. This paper challenges two narratives: that epistemic opacity is primarily a technological problem requiring technical solutions and that it represents a fundamental threat to human epistemic agency that must be overcome. In particular I argue that the necessity of using AI-mediated technology to stay informed forces us to adopt a r(el)ational approach to ignorance: we must strategically choose what not to know (rational ignorance) while negotiating how we depend on others through technologically mediated epistemically opaque channels (relational ignorance).