Language models have deception abilities

Aligning large language models (LLMs) with human values is of great importance. However, given the steady increase in reasoning abilities, future LLMs are under suspicion of becoming able to deceive human operators and utilizing this ability to bypass monitoring efforts. As a prerequisite to this, LLMs need to possess a conceptual understanding of deception strategies. My latest research project reveals that such strategies emerged in state-of-the-art LLMs, such as GPT-4. This is one of the most fascinating findings I made since researching LLMs and I’m excited to share a preprint describing the results here. I’ll continue working on this project.

Re:publica talk

I gave a talk addressing speciesist machine bias at this year’s re:publica, which is available for viewing on YouTube.


++++ Sarah Fabi and I updated the paper on human-like intuitive decision-making and errors in large language models by testing ChatGPT, GPT-4, BLOOM, and other models – here’s the new manuscript +++ I co-authored a paper on privacy literacy for the new Routledge Handbook of Privacy and Social Media +++ Together with Leonie Bossert, I published a paper on the ethics of sustainable AI +++ I got my own article series at Golem, called KI-Insider, where I will regularly publish new articles (in German) +++ I attended two further Science Slams in Friedrichshafen and Tübingen and won both of them +++ I was interviewed for a podcast about different AI-related topics (in German) +++

Using psychology to investigate behavior in large language models

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.

I’m hiring!

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.

Why we need biased AI

In a new paper I co-authered together with my wonderful colleague Sarah Fabi, we stress the importance of biases in the field of artificial intelligence (AI). To foster efficient algorithmic decision-making in complex, unstable, and uncertain real-world environments, we argue for the implementation of human cognitive biases in learning algorithms. We use insights from cognitive science and apply them to the AI field, combining theoretical considerations with tangible examples depicting promising bias implementation scenarios. Ultimately, this paper is the first tentative step to explicitly putting the idea forth to implement cognitive biases into machines.

PS: We also wrote a short paper on AI alignment. Check it out here.

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.

New paper with Peter Singer on speciesist bias in AI

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.

PS: I had the opportunity to publish an op-ed article in the German tech magazine Golem as well as a research summary at The AI Ethics Brief regarding the paper.

New papers

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.

Science Slam

In 2019, I competed in my first science slam. Then came covid. But finally, public events are possible again. Thus, I had the pleasure to be invited to a slam for the second time. In the end, the clapometer decided on a draw and I could happily share the win with Aysel Ahadova.

Racing is back

Finally, after two years of race cancellations due to Covid, I was able to compete in my first MTB race this year. I finished 1st in my class and 12th overall (280 participants). My aim was to finish in the top 10, but due to an injury that hindered my race preparation, I couldn’t perform at my best. Next time then.

Why some biases can be important for AI

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.

World record officially confirmed

It took quite some time for Guinness to review all the evidence but now it’s official: I have an entry in the record book, owning an ultracycling record, namely the greatest vertical ascent in 12 hours. It was an incredible undertaking, and I’m really thrilled having achieved this with the help of my wonderful girlfriend, family, and friends.

Blind spots in AI ethics

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, I had the opportunity to talk about AI ethics as a guest on the Cyber Valley Podcast. If you are interested, you can listen to it here. Other recent media appearances can also be found here.

Recent papers

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.


I decided to change the website. I’ll be switching to English from here on in order to make it more accessible for international visitors.