The advent of generative artificial intelligence and the widespread adoption of it in society engendered intensive debates about its ethical implications and risks. These risks often differ from those associated with traditional discriminative machine learning. To synthesize the recent discourse and map its normative concepts, I conducted a scoping review on the ethics of generative artificial intelligence, including especially large language models and text-to-image models. The paper is available as a preprint on arXiv, accessible via this link. It provides a taxonomy of 378 normative issues in 19 topic areas and ranks them according to their prevalence in the literature. The new study offers a comprehensive overview for scholars, practitioners, or policymakers, condensing the ethical debates surrounding fairness, safety, harmful content, hallucinations, privacy, interaction risks, security, alignment, societal impacts, and others.
This time, I am seeking applications for a student research assistant position (f/m/d) in my independent research group at the University of Stuttgart. For more details on how to apply, visit this link.
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.
Somehow, this paper must be something special. It got desk-rejected without review not by one, not by two, but by three different journals! This never happened to me before and I can only speculate about the underlying reasons. However, I am grateful to the editors of AI and Ethics who had the guts to let our research be peer-reviewed and published. But what is it all about? Massive efforts are made to reduce machine biases in order to render AI applications fair. However, the AI fairness field succumbs to a blind spot, namely its insensitivity to discrimination against animals. In order to address this, I wrote a paper together with Peter Singer and colleagues about “speciesist bias” in AI. We investigated several different datasets and AI systems, in particular computer vision models trained on ImageNet, word embeddings, and large language models like GPT-3, revealing significant speciesist biases in them. Our conclusion: AI technologies currently play a significant role in perpetuating and normalizing violence against animals, especially farmed animals. This can only be changed when AI fairness frameworks widen their scope and include mitigation measures for speciesist biases.
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.
Fairness biases in AI systems are a severe problem (as shown in my paper on “speciesist bias”). However, biases are not bad in and of itself. In our new paper, Sarah Fabi and I stress the actual importance of biases in the field of AI in two regards. First, in order to foster efficient algorithmic decision-making in complex, unstable, and uncertain real-world environments, we argue for the structurewise implementation of human cognitive biases in learning algorithms. Secondly, we argue that in order to achieve ethical machine behavior, filter mechanisms have to be applied for selecting biased training stimuli that represent social or behavioral traits that are ethically desirable.
I wrote a critical piece about my own field of research. It discusses the conservative nature of AI ethics’ main principles as well as the disregarding of negative externalities of AI technologies. The paper was recently published in AI and Ethics and can be accessed here.
Recently, three new papers have been published. Together with Kristof Meding, I conducted an empirical study on industry partners in AI research. The study is based on an analysis of nearly 11,000 publications from the most important AI conferences. The paper was published in “AI & Society” and can be read here.
A further paper appeared in “Minds and Machines” in which I argue for providing AI systems only those “environmental stimuli” for training that result in ethically desirable machine behavior. The idea is to overcome the Big Data principle of n=all in order to use new dimensions of data quality to better segregate which datafied behaviors are allowed to become training stimuli for machine learning applications in the first place. The paper can be viewed here.
Another paper I co-authored with my colleague Paula Helm critically addresses AI-based policing software. While predictive policing systems are often studied in this area, we explicitly looked at software used for criminal prosecution. An overview of these and other publications can be found here.