K. Imai, Zhichao Jiang, D. James Greiner, R. Halen, Sooahn Shin
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Moreover, even if fully algorithmic decision-making is more optimal than a hybrid system, we may still prefer the latter because we want to hold humans, rather than algorithms, accountable for the consequences of decisions. The importance of algorithm-assisted human decision making motivated us to develop a set of methodological tools to evaluate and understand how algorithmic recommendations affect human decisions. As echoed by Cruz Cortéz and Gosh, our study sharply contrasts with much of the existing studies whose focus has been the accuracy and fairness of algorithms themselves. We hope other researchers follow up on this important research agenda. Below, we respond to specific comments raised by the discussants.","PeriodicalId":49983,"journal":{"name":"Journal of the Royal Statistical Society Series A-Statistics in Society","volume":null,"pages":null},"PeriodicalIF":1.5000,"publicationDate":"2023-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Reply to the discussants of “Experimental Evaluation of Algorithm-Assisted Human Decision-Making: Application to Pretrial Public Safety Assessment.” Journal of the Royal Statistical Society, Series A, Forthcoming\",\"authors\":\"K. Imai, Zhichao Jiang, D. James Greiner, R. Halen, Sooahn Shin\",\"doi\":\"10.1093/jrsssa/qnad023\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We thank the Research Section of the Royal Statistical Society for providing us with a valuable opportunity to present our paper and receive feedback from many scholars. 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As echoed by Cruz Cortéz and Gosh, our study sharply contrasts with much of the existing studies whose focus has been the accuracy and fairness of algorithms themselves. We hope other researchers follow up on this important research agenda. 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Reply to the discussants of “Experimental Evaluation of Algorithm-Assisted Human Decision-Making: Application to Pretrial Public Safety Assessment.” Journal of the Royal Statistical Society, Series A, Forthcoming
We thank the Research Section of the Royal Statistical Society for providing us with a valuable opportunity to present our paper and receive feedback from many scholars. Despite their diverse perspectives, we believe that all of our discussants are in agreement about the need to develop new statistical methodology to better analyze algorithm-assisted human decision making. While the use of algorithms has become ubiquitous in today’s society, we — humans — still make many consequential decisions with the help of algorithms. As Kumar and VanderWeele noted, such a hybrid decision-making system may allows one to combine human experiences and knowledge with algorithmic recommendations, possibly leading to improved decisions. Moreover, even if fully algorithmic decision-making is more optimal than a hybrid system, we may still prefer the latter because we want to hold humans, rather than algorithms, accountable for the consequences of decisions. The importance of algorithm-assisted human decision making motivated us to develop a set of methodological tools to evaluate and understand how algorithmic recommendations affect human decisions. As echoed by Cruz Cortéz and Gosh, our study sharply contrasts with much of the existing studies whose focus has been the accuracy and fairness of algorithms themselves. We hope other researchers follow up on this important research agenda. Below, we respond to specific comments raised by the discussants.
期刊介绍:
Series A (Statistics in Society) publishes high quality papers that demonstrate how statistical thinking, design and analyses play a vital role in all walks of life and benefit society in general. There is no restriction on subject-matter: any interesting, topical and revelatory applications of statistics are welcome. For example, important applications of statistical and related data science methodology in medicine, business and commerce, industry, economics and finance, education and teaching, physical and biomedical sciences, the environment, the law, government and politics, demography, psychology, sociology and sport all fall within the journal''s remit. The journal is therefore aimed at a wide statistical audience and at professional statisticians in particular. Its emphasis is on well-written and clearly reasoned quantitative approaches to problems in the real world rather than the exposition of technical detail. Thus, although the methodological basis of papers must be sound and adequately explained, methodology per se should not be the main focus of a Series A paper. Of particular interest are papers on topical or contentious statistical issues, papers which give reviews or exposés of current statistical concerns and papers which demonstrate how appropriate statistical thinking has contributed to our understanding of important substantive questions. Historical, professional and biographical contributions are also welcome, as are discussions of methods of data collection and of ethical issues, provided that all such papers have substantial statistical relevance.