Academic Year 2025-26

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).

Does linguistic reference to the external world require a basis, or grounding in, perception? Do today’s large language model (LLM)-based chatbots have such perceptual grounding? In this talk, I give a qualified “yes” answer to the first question, focusing on a notion of reference as a direct relation to reality and a broad notion of perception. I do not answer the second question definitively, but I propose that the answer depends on whether LLM chatbots’ processing of linguistic input itself can provide the needed perceptual basis. I make a start on assessing this by considering whether and how prior linguistic uptake serves to ground human referring, especially in the broadly Kripkean chains of communication that have also been appealed to recently to explain reference by LLM chatbots.

This paper engages with fictionalist accounts of verbal human-AI interaction, according to which, although lay AI-users know that AIs are not intentional agents (and hence that their linguistic output is strictly speaking meaningless), laypeople pretend/imagine that they are intentional agents when talking to them. Reviewing the relevant experimental philosophy literature, I suggest that a substantial group of lay AI-users — the uncaring users — engage in bullshit action: They do not know, and, more importantly, do not care whether AIs are intentional agents. Still, they act as if this is the case when talking to them.

The uncanny entities in the AI “suitcase” increasingly straddle the border between person and thing. Western legal systems however have for two millennia relied on a strict, tertium non datur person-thing dichotomy. Their resistance to change recently took the form of the AI Act, in which AI systems are treated as things, products on the internal market, despite earlier calls from the EU Parliament for the attribution of legal personhood to the most complex ones. I argue that there are multiple ways in which AI systems escape the binary categorization as either things, due to their anthropomorphic features, or persons, due to the imperfect overlap between their capacities with human ones. This mismatch, if ignored in jurisprudence, creates gaps in liability attribution and legal protection generally.

I propose that the capacities setting AI systems apart could amount to legal agency. The concept of legal agency employed here is different from the law of agency. It does not only refer to contractual legal relations of representation, but to a legal status found somewhere in-between legal personhood and thinghood, or rather beyond these. Legal agents do not quite overlap with legal persons, lacking the complete, robust array of rights and obligations that persons hold, but they would not fall into the passive category of mere things that legal relations are about either. At the same time, not all legal persons are legal agents, such as infants, who have rights but lack the capacity to act. Legal agents have the capacity to act with legal effect, that is, to perform acts and enter legal relations (e.g., entering contracts, acquiring rights, incurring liabilities) that the legal system recognizes as legally valid and binding.

However, for AI systems to be jurisprudentially recognized as legal agents, they need to be considered the source of an action. The standard theory of agency in philosophy associates this with mental states, as does the legal theory and philosophy of law. Thus, from this realist position, it is argued in philosophy that an AI system may not have internal states to ground the ascription of representational mental states, thus being incapable of intentional agency. Assuming that law follows closely this philosophical account, there are two avenues for artificial legal agents then: either display (something akin to) mental states or then comply with a set of less demanding requirements that “naturalize” agency. As for the first, the sparse accounts of AI agency in legal literature have so far relied either on the “sense-think-act paradigm” coming from the technical domain to describe the internal states of these artificial agents that mimic mental states, or on  what is known in the philosophy of action as “the intentional stance”, which involves treating AI agents as if they are intentional entities as a strategy to deal with their actions and without looking into their make-up.

As to the second, the law lags behind philosophy, without concepts such as agency without mental representations in its toolbox. I propose changing that and replacing “Liberal” agency, which has been the only legal concept thereof for the better part of two centuries. Since persons with disabilities, animals, and now AI systems seem to mark the end of the individual, highly rational, autonomous person as the only legal agent that can be taken into account, this contribution will thus flesh out a concept of legal agency that is mindful at least of the seeming mindlessness of AI.

How do we ensure that AI systems remain aligned with human values? Recently, worries have been expressed that the autonomy and increased ‘intelligence’ of AI systems may lead to systems that get out of control and are harmful to humans. This has led to proposals for AI systems that ensure value alignment by tracking human values or preferences in real-time (for example, Stuart Russell in his book Human Compatible: Artificial Intelligence and the Problem of Control). In my talk, I will criticize such proposals for being insufficient for addressing the alignment problem.