Showing papers by "Claude Castelluccia published in 2022"

Taking Advice from (Dis)Similar Machines: The Impact of Human-Machine Similarity on Machine-Assisted Decision-Making

Abstract: Machine learning algorithms are increasingly used to assist human decision-making. When the goal of machine assistance is to improve the accuracy of human decisions, it might seem appealing to design ML algorithms that complement human knowledge. While neither the algorithm nor the human are perfectly accurate, one could expect that their complementary expertise might lead to improved outcomes. In this study, we demonstrate that in practice decision aids that are not complementary, but make errors similar to human ones may have their own benefits. In a series of human-subject experiments with a total of 901 participants, we study how the similarity of human and machine errors influences human perceptions of and interactions with algorithmic decision aids. We find that (i) people perceive more similar decision aids as more useful, accurate, and predictable, and that (ii) people are more likely to take opposing advice from more similar decision aids, while (iii) decision aids that are less similar to humans have more opportunities to provide opposing advice, resulting in a higher influence on people’s decisions overall.

Mapping the Use of Facial Recognition in Public Spaces in Europe – Part 1: A Quest for Clarity: Unpicking the 'Catch-All' Term

Abstract: Our study covers both “facial recognition” , as understood in its traditional sense (and closely associated with the concept of “biometric data” ) and “face analysis” , where strictly speaking no “facial recognition” takes place, but where there is use of what is defined, via an emerging new term, as “biometric -based data” (see below). Before explaining the scope of our study, it is therefore important to dive into the important issue of how these AI-related terms are defined. As we will see, the existing definitions are not entirely satisfactory, and this has encouraged various stakeholders to introduce new terms and categories, which may resolve certain problems but is also accentuating the overall terminological confusion.