Current cases of AI misalignment and their implications for future risks - Dung, Leonard (Synthese, 2023)
How can one build AI systems such that they pursue the goals their designers want them to pursue? This is the alignment problem. Numerous authors have raised concerns that, as research advances and systems become more powerful over time, misalignment might lead to catastrophic outcomes, perhaps even to the extinction or permanent disempowerment of humanity. In this paper, I analyze the severity of this risk based on current instances of misalignment. More specifically, I argue that contemporary large language models and game-playing agents are sometimes misaligned. These cases suggest that misalignment tends to have a variety of features: misalignment can be hard to detect, predict and remedy, it does not depend on a specific architecture or training paradigm, it tends to diminish a system’s usefulness and it is the default outcome of creating AI via machine learning. Subsequently, based on these features, I show that the risk of AI alignment magnifies with respect to more capable systems. Not only might more capable systems cause more harm when misaligned, aligning them should be expected to be more difficult than aligning current AI.
CRIS-Team Scopus Importer:2023-11-03
Keywords: AI alignment; Existential risk; Large-language models; Reward hacking; Superintelligence

From animal to plant sentience: Is there credible evidence? - Dung, Leonard (Animal Sentience, 2023)
Segundo-Ortin & Calvo argue that plants have a surprisingly varied and complex behavioral repertoire. Which of these behavioral capacities are credible indicators of sentience? If we use the standards of evidence common in discussions of animal sentience, the behavioral capacities reviewed are insufficient evidence of sentience. Even if some putative indicators of animal sentience are present in plants, it is not clear whether what we should conclude is that plants are sentient or that those indicators are inadequate.
CRIS-Team Scopus Importer:2023-05-26

How to deal with risks of AI suffering - Dung, Leonard (Inquiry-An Interdisciplinary Journal of Philosophy, 2023)
We might create artificial systems which can suffer. Since AI suffering might potentially be astronomical, the moral stakes are huge. Thus, we need an approach which tells us what to do about the risk of AI suffering. I argue that such an approach should ideally satisfy four desiderata: beneficence, action-guidance, feasibility and consistency with our epistemic situation. Scientific approaches to AI suffering risk hold that we can improve our scientific understanding of AI, and AI suffering in particular, to decrease AI suffering risks. However, such approaches tend to conflict with either the desideratum of consistency with our epistemic situation or with feasibility. Thus, we also need an explicitly ethical approach to AI suffering risk. Such an approach tells us what to do in the light of profound scientific uncertainty about AI suffering. After discussing multiple views, I express support for a hybrid approach. This approach is partly based on the maximization of expected value and partly on a deliberative approach to decision-making.
CRIS-Team Scopus Importer:2023-09-29
Keywords: AI sentience; expected value; Machine consciousness; measuring AI consciousness; precautionary principle; suffering

Implementing artificial consciousness - Dung, Leonard; Kersten, Luke (Mind & Language, 2024)
Implementationalism maintains that conventional, silicon-based artificial systems are not conscious because they fail to satisfy certain substantive constraints on computational implementation. In this article, we argue that several recently proposed substantive constraints are implausible, or at least are not well-supported, insofar as they conflate intuitions about computational implementation generally and consciousness specifically. We argue instead that the mechanistic account of computation can explain several of the intuitions driving implementationalism and non-computationalism in a manner which is consistent with artificial consciousness. Our argument provides indirect support for computationalism about consciousness and the view that conventional artificial systems can be conscious.
CRIS-Team Scopus Importer:2024-11-22
Keywords: artificial consciousness; computational implementation; functionalism; implementationalism; substantive constraints

On the need for a global AI ethics - Lundgren, Björn; Catena, Eleonora; Robertson, Ian; Hellrigel-Holderbaum, Max; Jaja, Ibifuro; Dung, Leonard (Journal of Global Ethics, 2024)
The impact of artificial intelligence (AI) is not only global but globally varied. Yet, AI ethics is all too often overly localised. This paper discusses the potential of a global AI ethics, highlighting several important variables that it should take into account if it is to be as successful an enterprise as it needs to be.
CRIS-Team Scopus Importer:2024-12-13
Keywords: AI ethics; AI guidelines; global AI ethics; international relations; technological disparities

Preserving the Normative Significance of Sentience - Dung, Leonard (Journal of Consciousness Studies, 2024)
According to an orthodox view, the capacity for conscious experience (sentience) is relevant to the distribution of moral status and value. However, physicalism about consciousness might threaten the normative relevance of sentience. According to the indeterminacy argument, sentience is metaphysically indeterminate while indeterminacy of sentience is incompatible with its normative relevance. According to the introspective argument (by François Kammerer), the unreliability of our conscious introspection undercuts the justification for belief in the normative relevance of consciousness. I defend the normative relevance of sentience against these objections. First, I demonstrate that physicalists only have to concede a limited amount of indeterminacy of sentience. This moderate indeterminacy is in harmony with the role of sentience in determining moral status. Second, I argue that physicalism gives us no reason to expect that introspection is unreliable with respect to the normative relevance of consciousness.
CRIS-Team Scopus Importer:2024-03-15

Tests of Animal Consciousness are Tests of Machine Consciousness - Dung, Leonard (Erkenntnis, 2023)
If a machine attains consciousness, how could we find out? In this paper, I make three related claims regarding positive tests of machine consciousness. All three claims center on the idea that an AI can be constructed “ad hoc”, that is, with the purpose of satisfying a particular test of consciousness while clearly not being conscious. First, a proposed test of machine consciousness can be legitimate, even if AI can be constructed ad hoc specifically to pass this test. This is underscored by the observation that many, if not all, putative tests of machine consciousness can be passed by non-conscious machines via ad hoc means. Second, we can identify ad hoc AI by taking inspiration from the notion of an ad hoc hypothesis in philosophy of science. Third, given the first and the second claim, the most reliable tests of animal consciousness turn out to be valid and useful positive tests of machine consciousness as well. If a non-ad hoc AI exhibits clusters of cognitive capacities facilitated by consciousness in humans which can be selectively switched off by masking and if it reproduces human behavior in suitably designed double dissociation tasks, we should treat the AI as conscious.
CRIS-Team Scopus Importer:2023-11-24

The argument for near-term human disempowerment through AI - Dung, Leonard (AI and Society, 2024)
Many researchers and intellectuals warn about extreme risks from artificial intelligence. However, these warnings typically came without systematic arguments in support. This paper provides an argument that AI will lead to the permanent disempowerment of humanity, e.g. human extinction, by 2100. It rests on four substantive premises which it motivates and defends: first, the speed of advances in AI capability, as well as the capability level current systems have already reached, suggest that it is practically possible to build AI systems capable of disempowering humanity by 2100. Second, due to incentives and coordination problems, if it is possible to build such AI, it will be built. Third, since it appears to be a hard technical problem to build AI which is aligned with the goals of its designers, and many actors might build powerful AI, misaligned powerful AI will be built. Fourth, because disempowering humanity is useful for a large range of misaligned goals, such AI will try to disempower humanity. If AI is capable of disempowering humanity and tries to disempower humanity by 2100, then humanity will be disempowered by 2100. This conclusion has immense moral and prudential significance.
CRIS-Team Scopus Importer:2024-05-03
Keywords: AI alignment; AI ethics; AI safety; Existential risk; Instrumental convergence; Scaling hypothesis