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 particularly concerning 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 decision-makers without making restrictive ex-ante assumptions about how recommendations affect decisions. We formulate an algorithmic-design problem that leverages the potential-outcomes framework from causal inference to model the effect of recommendations on a human decision-maker's binary treatment choice. Within this model, we introduce a monotonicity assumption that leads to an intuitive classification of human responses to the algorithm. Under this assumption, we can express the human's response to algorithmic recommendations in terms of their compliance with the algorithm and the active decision they would take if the algorithm sends no recommendation. We showcase the utility of our framework using an online experiment that simulates a hiring task. We argue that our approach can make sense of the relative performance of different recommendation algorithms in the experiment and can help design solutions that realize human-AI complementarity. Finally, we leverage our approach to derive minimax optimal recommendation algorithms that can be implemented with machine learning using limited training data.
Advancing Human-AI Complementarity: The Impact of User Expertise and Algorithmic Tuning on Joint Decision Making
Microsoft Research（微软研究院）；University of Virginia（弗吉尼亚大学）；University of Washington（华盛顿大学）；University of Michigan（密歇根大学）；Human Computation Institute（人类计算研究所）；LMU Munich（慕尼黑大学）；Western Washington University（西华盛顿大学）
Designing the virtual CAT: A digital tool for algorithmic thinking assessment in compulsory education
Università della Svizzera italiana (USI), Dalle Molle Institute for Artificial Intelligence (IDSIA)（瑞士联邦理工学院（USI）、Dalle Molle人工智能研究所（IDSIA））；University of Applied Sciences and Arts of Southern Switzerland (SUPSI), Department of Education and Learning (DFA)（瑞士南部应用科学与艺术大学（SUPSI）、教育与学习系）
Human-centered explanation does not fit all: The interplay of sociotechnical, cognitive, and individual factors in the effect AI explanations in algorithmic decision-making
University of Pittsburgh（匹兹堡大学）；Northeastern University（东北大学）；Syracuse University（Syracuse大学）
Situated Action in Pre-Hospital Critical Care Dispatch: Identifying where and how Algorithmic Assistance might be useful in the daily work of specialist Emergency Medical Dispatchers
Swansea University（斯旺西大学）；EMRTS, Swansea Bay UHB（EMRTS，斯旺西湾大学健康保健董事会）
Algorithmic Prompt Generation for Diverse Human-like Teaming and Communication with Large Language Models
Thomas Lord Department of Computer Science, University of Southern California（美国南加州大学汤姆·劳德计算机科学系）；School of Computing and Information, University of Pittsburgh（美国匹兹堡大学计算与信息学院）；Robotics Institute, Carnegie Mellon University（卡内基梅隆大学机器人研究所）；Sibley School of Mechanical and Aerospace Engineering, Cornell University（康奈尔大学西伯利机械与航空航天工程学院）
