Taking Advice from (Dis)Similar Machines: The Impact of Human-Machine Similarity on Machine-Assisted Decision-Making - Centre of Innovation in Telecommunications and Integration of services Accéder directement au contenu
Communication Dans Un Congrès Année : 2022

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

Résumé

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.
Fichier principal
Vignette du fichier
2209.03821.pdf (2.95 Mo) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-03956115 , version 1 (25-01-2023)

Identifiants

Citer

Nina Grgić-Hlača, Claude Castelluccia, Krishna P Gummadi. Taking Advice from (Dis)Similar Machines: The Impact of Human-Machine Similarity on Machine-Assisted Decision-Making. HCOMP 2022 - The Tenth AAAI Conference on Human Computation and Crowdsourcing, AAAI, Nov 2022, Virtual, France. ⟨hal-03956115⟩
17 Consultations
19 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More