WILL HUMANS-IN-THE-LOOP BECOME BORGS? MERITS AND PITFALLS OF WORKING WITH AI

被引:135
作者
Fuegener, Andreas [1 ]
Grahl, Jorn [1 ]
Gupta, Alok [2 ]
Ketter, Wolfgang [1 ,3 ]
机构
[1] Univ Cologne, Fac Management Econ & Social Sci, Cologne, Germany
[2] Univ Minnesota, Carlson Sch Management, Minneapolis, MN 55455 USA
[3] Erasmus Univ, Rotterdam Sch Management, Rotterdam, Netherlands
关键词
Artificial intelligence; unique human knowledge; future of work; wisdom of crowds; analytical model; machine learning; AI-human complementarity; ORGANIZATIONAL DESIGN; INTELLIGENCE; ABILITY; WISDOM; HEADS; TEAM;
D O I
10.25300/MISQ/2021/16553
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
We analyze how advice from an AI affects complementarities between humans and AI, in particular what humans know that an AI does not know: "unique human knowledge." In a multi-method study consisting of an analytical model, experimental studies, and a simulation study, our main finding is that human choices converge toward similar responses improving individual accuracy. However, as overall individual accu-racy of the group of humans improves, the individual unique human knowledge decreases. Based on this finding, we claim that humans interacting with AI behave like "Borgs," that is, cyborg creatures with strong individual performance but no human individuality. We argue that the loss of unique human knowledge may lead to several undesirable outcomes in a host of human-AI decision environments. We demonstrate this harmful impact on the "wisdom of crowds." Simulation results based on our experimental data suggest that groups of humans interacting with AI are far less effective as compared to human groups without AI assistance. We suggest mitigation techniques to create environments that can provide the best of both worlds (e.g., by personalizing AI advice). We show that such interventions perform well individually as well as in wisdom of crowds settings.
引用
收藏
页码:1527 / 1556
页数:30
相关论文
共 49 条
[41]   ImageNet Large Scale Visual Recognition Challenge [J].
Russakovsky, Olga ;
Deng, Jia ;
Su, Hao ;
Krause, Jonathan ;
Satheesh, Sanjeev ;
Ma, Sean ;
Huang, Zhiheng ;
Karpathy, Andrej ;
Khosla, Aditya ;
Bernstein, Michael ;
Berg, Alexander C. ;
Fei-Fei, Li .
INTERNATIONAL JOURNAL OF COMPUTER VISION, 2015, 115 (03) :211-252
[42]   Mastering the game of Go with deep neural networks and tree search [J].
Silver, David ;
Huang, Aja ;
Maddison, Chris J. ;
Guez, Arthur ;
Sifre, Laurent ;
van den Driessche, George ;
Schrittwieser, Julian ;
Antonoglou, Ioannis ;
Panneershelvam, Veda ;
Lanctot, Marc ;
Dieleman, Sander ;
Grewe, Dominik ;
Nham, John ;
Kalchbrenner, Nal ;
Sutskever, Ilya ;
Lillicrap, Timothy ;
Leach, Madeleine ;
Kavukcuoglu, Koray ;
Graepel, Thore ;
Hassabis, Demis .
NATURE, 2016, 529 (7587) :484-+
[43]  
Surowiecki, 2004, The wisdom of crowds why the many are smarter than the few and how collective wisdom shapes business, economies, societies, and nations
[44]   Rethinking the Inception Architecture for Computer Vision [J].
Szegedy, Christian ;
Vanhoucke, Vincent ;
Ioffe, Sergey ;
Shlens, Jon ;
Wojna, Zbigniew .
2016 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR), 2016, :2818-2826
[45]  
Szegedy C, 2015, PROC CVPR IEEE, P1, DOI 10.1109/CVPR.2015.7298594
[46]  
Tan Sarah, 2018, LEARNING GLOBAL ADDI
[47]   Prediction markets [J].
Wolfers, J ;
Zitzewitz, E .
JOURNAL OF ECONOMIC PERSPECTIVES, 2004, 18 (02) :107-126
[48]   Efect of Confidence and Explanation on Accuracy and Trust Calibration in AI-Assisted Decision Making [J].
Zhang, Yunfeng ;
Liao, Q. Vera ;
Bellamy, Rachel K. E. .
FAT* '20: PROCEEDINGS OF THE 2020 CONFERENCE ON FAIRNESS, ACCOUNTABILITY, AND TRANSPARENCY, 2020, :295-305
[49]  
Zhou J, 2019, IJCAI 2019 WORKSH EX