In order to build more useful AI systems, a natural inclination is to try to make them more agentic . But while agents built from language models are touted as the next big advance [Wang et al., 2024],

Agency is a system's capacity to steer outcomes toward a goal, and is a central topic of study across biology, philosophy, cognitive science, and artificial intelligence. Determining if a system exhibits agency is a notoriously difficult question: Dennett (1989), for instance, highlights the puzzle of determining which principles can decide whether a rock, a thermostat, or a robot each possess agency. We here address this puzzle from the viewpoint of reinforcement learning by arguing that agency is fundamentally frame-dependent: Any measurement of a system's agency must be made relative to a reference frame. We support this claim by presenting a philosophical argument that each of the essential properties of agency proposed by Barandiaran et al. (2009) and Moreno (2018) are themselves frame-dependent. We conclude that any basic science of agency requires frame-dependence, and discuss the implications of this claim for reinforcement learning.

We define maximum entropy goal-directedness (MEG), a formal measure of goal-directedness in causal models and Markov decision processes, and give algorithms for computing it. Measuring goal-directedness is important, as it is a critical element of many concerns about harm from AI. It is also of philosophical interest, as goal-directedness is a key aspect of agency. MEG is based on an adaptation of the maximum causal entropy framework used in inverse reinforcement learning. It can measure goal-directedness with respect to a known utility function, a hypothesis class of utility functions, or a set of random variables. We prove that MEG satisfies several desiderata and demonstrate our algorithms with small-scale experiments.

As AI systems increasingly operate with autonomy and adaptability, the traditional boundaries of moral responsibility in techno-social systems are being challenged. This paper explores the evolving discourse on the delegation of responsibilities to intelligent autonomous agents and the ethical implications of such practices. Synthesizing recent developments in AI ethics, including concepts of distributed responsibility and ethical AI by design, the paper proposes a functionalist perspective as a framework. This perspective views moral responsibility not as an individual trait but as a role within a socio-technical system, distributed among human and artificial agents. As an example of 'AI ethical by design,' we present Basti and Vitiello's implementation. They suggest that AI can act as artificial moral agents by learning ethical guidelines and using Deontic Higher-Order Logic to assess decisions ethically. Motivated by the possible speed and scale beyond human supervision and ethical implications, the paper argues for 'AI ethical by design', while acknowledging the distributed, shared, and dynamic nature of responsibility. This functionalist approach offers a practical framework for navigating the complexities of AI ethics in a rapidly evolving technological landscape.

This study investigates the potential of deterministic systems, specifically large language models (LLMs), to exhibit the functional capacities of moral agency and compatibilist free will. We develop a functional definition of free will grounded in Dennett's compatibilist framework, building on an interdisciplinary theoretical foundation that integrates Shannon's information theory, Dennett's compatibilism, and Floridi's philosophy of information. This framework emphasizes the importance of reason-responsiveness and value alignment in determining moral responsibility rather than requiring metaphysical libertarian free will. Shannon's theory highlights the role of processing complex information in enabling adaptive decision-making, while Floridi's philosophy reconciles these perspectives by conceptualizing agency as a spectrum, allowing for a graduated view of moral status based on a system's complexity and responsiveness. Our analysis of LLMs' decision-making in moral dilemmas demonstrates their capacity for rational deliberation and their ability to adjust choices in response to new information and identified inconsistencies. Thus, they exhibit features of a moral agency that align with our functional definition of free will. These results challenge traditional views on the necessity of consciousness for moral responsibility, suggesting that systems with self-referential reasoning capacities can instantiate degrees of free will and moral reasoning in artificial and biological contexts. This study proposes a parsimonious framework for understanding free will as a spectrum that spans artificial and biological systems, laying the groundwork for further interdisciplinary research on agency and ethics in the artificial intelligence era.

Can large language models be trained to produce philosophical texts that are difficult to distinguish from texts produced by human philosophers? To address this question, we fine-tuned OpenAI's GPT-3 with the works of philosopher Daniel C. Dennett as additional training data. To explore the Dennett model, we asked the real Dennett ten philosophical questions and then posed the same questions to the language model, collecting four responses for each question without cherry-picking. We recruited 425 participants to distinguish Dennett's answer from the four machine-generated answers. Experts on Dennett's work (N = 25) succeeded 51% of the time, above the chance rate of 20% but short of our hypothesized rate of 80% correct. For two of the ten questions, the language model produced at least one answer that experts selected more frequently than Dennett's own answer. Philosophy blog readers (N = 302) performed similarly to the experts, while ordinary research participants (N = 98) were near chance distinguishing GPT-3's responses from those of an "actual human philosopher".

Natural language processing based on large language models (LLMs) is a booming field of AI research. After neural networks have proven to outperform humans in games and practical domains based on pattern recognition, we might stand now at a road junction where artificial entities might eventually enter the realm of human communication. However, this comes with serious risks. Due to the inherent limitations regarding the reliability of neural networks, overreliance on LLMs can have disruptive consequences. Since it will be increasingly difficult to distinguish between human-written and machine-generated text, one is confronted with new ethical challenges. This begins with the no longer undoubtedly verifiable human authorship and continues with various types of fraud, such as a new form of plagiarism. This also concerns the violation of privacy rights, the possibility of circulating counterfeits of humans, and, last but not least, it makes a massive spread of misinformation possible.

In recent years, computer scientists have been trying to create increasingly advanced dialogue and information systems. The release of ChatGPT and other highly performing language models are demonstrating just how far artificial intelligence can go in answering user questions, writing texts and conversing with humans. This model, presented in a paper published on the pre-print server arXiv, can autonomously generate answers that closely resemble those produced by human philosophers. "Anna Strasser, Matthew Crosby and I had noticed that people were creating GPT-3 outputs in the style of various writers or other philosophers," Eric Schwitzgebel, one of the researchers who carried out the study, told Tech Xplore. "We thought it would be interesting to see if we could fine-tune GPT-3 (Generative Pre-trained Transformer 3) on the body of work of a philosopher, then ask it questions and see if it said things that the real philosopher might have said."

A study has found that OpenAI's GPT-3 is capable of being indistinguishable from a human philosopher. The now infamous GPT-3 is a powerful autoregressive language model that uses deep learning to produce human-like text. Eric Schwitzgebel, Anna Strasser, and Matthew Crosby set out to find out whether GPT-3 can replicate a human philosopher. The team "fine-tuned" GPT-3 based on philosopher Daniel Dennet's corpus. Ten philosophical questions were then posed to both the real Dennet and GPT-3 to see whether the AI could match its renowned human counterpart.