Large language models (LLMs) are currently at the forefront of intertwining AI systems with human communication and everyday life. Therefore, it is of great importance to thoroughly assess and scrutinize their capabilities. Due to increasingly complex and novel behavioral patterns in current LLMs, this can be done by treating them as participants in psychology experiments that were originally designed to test humans. For this purpose, I wrote a new paper introducing the field of “machine psychology”. It aims to discover emergent abilities in LLMs that cannot be detected by most traditional natural language processing benchmarks. A preprint of the paper can be read here.
Looking for an exciting opportunity to explore the ethical implications of AI, specifically generative AI and large language models? I am seeking applications for a Ph.D. position (f/m/d) in my independent research group at the University of Stuttgart. For more details on how to apply, visit this link.
Machine intuition in GPT
Together with two colleagues, Sarah Fabi and Michal Kosinski, I wrote a paper about a phenomenon we call “machine intuition”. We used a state-of-the-art large language model, namely GPT-3.5, and probed it with the Cognitive Reflection Test as well as semantic illusions that were originally designed to investigate intuitive decision-making in humans. Our results show that GPT-3.5 systematically exhibits “machine intuition”, meaning that it produces incorrect responses that are surprisingly equal to how humans respond to the Cognitive Reflection Test as well as to semantic illusions. The paper is available as an arXiv preprint.
Paper #1 – AI ethics and its side-effects (Link)
I wrote a critical article about my own discipline, AI ethics, in which I argue that the assumption that AI ethics automatically decrease the likelihood of unethical outcomes in the AI field is flawed. The article lists risks that either originate from AI ethicists themselves or from the consequences their embedding in AI organizations has. The compilation of risks comprises psychological considerations concerning the cognitive biases of AI ethicists themselves as well as biased reactions to their work, subject-specific and knowledge constraints AI ethicists often succumb to, negative side effects of ethics audits for AI applications, and many more.
Paper #2 – A virtue-based framework for AI ethics (Link)
Many ethics initiatives have stipulated standards for good technology development in the AI sector. I contribute to that endeavor by proposing a new approach that is based on virtue ethics. It defines four “basic AI virtues”, namely justice, honesty, responsibility, and care, all of which represent specific motivational settings that constitute the very precondition for ethical decision-making in the AI field. Moreover, it defines two “second-order AI virtues”, prudence and fortitude, that bolster achieving the basic virtues by helping with overcoming bounded ethicality or hidden psychological forces that can impair ethical decision making and that are hitherto disregarded in AI ethics. Lastly, the paper describes measures for successfully cultivating the mentioned virtues in organizations dealing with AI research and development.
Paper #3 – Ethical and methodological challenges in building morally informed AI systems (Link)
Recent progress in large language models has led to applications that can (at least) simulate possession of full moral agency due to their capacity to report context-sensitive moral assessments in open-domain conversations. However, automating moral decision-making faces several methodological as well as ethical challenges. In the paper, we comment on all these challenges and provide critical considerations for future research on full artificial moral agency.