Designing Algorithmic Recommendations to Achieve Human-AI Complementarity

arXiv:2405.01484v1 Announce Type: cross
Abstract: Algorithms frequently assist, rather than replace, human decision-makers. However, the design and analysis of algorithms often focus on predicting outcomes and do not explicitly model their effect on human decisions. This discrepancy between the design and role of algorithmic assistants becomes of particular concern in light of empirical evidence that suggests that algorithmic assistants again and again fail to improve human decisions. In this article, we formalize the design of recommendation algorithms that assist human …

abstract algorithms analysis and analysis arxiv assistants cs.hc cs.lg decision decisions design designing econ.em focus however human light makers recommendations role stat.ml type