Sentient Media reported on our research regarding speciesist biases in AI systems. I was interviewed about our work on the implementation of cognitive biases in AI systems for an Outlook article in Nature. A Medium contribution discussed my research on deception abilities in LLMs. Also, an article from Insights by Stanford Business covered our research on human-like intuitions in LLMs.
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.
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.