“Blinding” — purposefully limiting the information incorporated into an evaluation to reduce the risk of bias — is a policy solution employed in various domains to increase the fairness and accuracy of evaluations. However, at many important organizational junctures, such as hiring decisions, promotion assessments, and performance reviews, blinding policies are relatively rare. For example, hiring managers generally have broad, rather than constrained, autonomy in selecting the information that is incorporated into hiring decisions. Consequentially, hiring decisions are susceptible to bias via non-credential information gathered through unstructured interview procedures or internet searches. The goals of the present research were twofold. First, using a sample of HR practitioners with extensive hiring experience, we explored preferences for self-blinding — a personal choice to avoid receiving potentially biasing information about a target of evaluation — in a mock hiring task. Second, we gauged practitioners’ professional experience and familiarity with blinding policies in organizational settings.
{"title":"Preferences for, and Familiarity With, Blinding Among HR Practitioners","authors":"Sean Fath, S. Zhu","doi":"10.2139/ssrn.3768039","DOIUrl":"https://doi.org/10.2139/ssrn.3768039","url":null,"abstract":"“Blinding” — purposefully limiting the information incorporated into an evaluation to reduce the risk of bias — is a policy solution employed in various domains to increase the fairness and accuracy of evaluations. However, at many important organizational junctures, such as hiring decisions, promotion assessments, and performance reviews, blinding policies are relatively rare. For example, hiring managers generally have broad, rather than constrained, autonomy in selecting the information that is incorporated into hiring decisions. Consequentially, hiring decisions are susceptible to bias via non-credential information gathered through unstructured interview procedures or internet searches. The goals of the present research were twofold. First, using a sample of HR practitioners with extensive hiring experience, we explored preferences for self-blinding — a personal choice to avoid receiving potentially biasing information about a target of evaluation — in a mock hiring task. Second, we gauged practitioners’ professional experience and familiarity with blinding policies in organizational settings.","PeriodicalId":321336,"journal":{"name":"DecisionSciRN: Recruiting & Hiring (Sub-Topic)","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129989951","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Andrew M. Carnes, Kevin G. Knotts, T. Munyon, J. Heames, Jeffery D. Houghton
This study integrates past research addressing decision‐making, employee selection, and influence processes in an attempt to provide both a theoretical and empirical foundation for future research addressing initial impressions throughout the interview process. Using data from a simulated hiring situation comprised of 28 recruiters and 229 applicants, the results suggest that initial impressions formed at the beginning of the interview make a substantive impact on final impressions. However, impressions formed at the career fair do not appear to impact final impressions without considering the interactive effects of decision confidence. Hypotheses proposing that decision confidence would moderate linkages between initial impressions formed at the beginning of the interview and both interview scores and final impressions were not supported. We discuss the theoretical and practical implications of these findings for selection.
{"title":"Think Fast: The Role of Thin Slices of Behavior in Employee Selection Decisions","authors":"Andrew M. Carnes, Kevin G. Knotts, T. Munyon, J. Heames, Jeffery D. Houghton","doi":"10.1111/ijsa.12257","DOIUrl":"https://doi.org/10.1111/ijsa.12257","url":null,"abstract":"This study integrates past research addressing decision‐making, employee selection, and influence processes in an attempt to provide both a theoretical and empirical foundation for future research addressing initial impressions throughout the interview process. Using data from a simulated hiring situation comprised of 28 recruiters and 229 applicants, the results suggest that initial impressions formed at the beginning of the interview make a substantive impact on final impressions. However, impressions formed at the career fair do not appear to impact final impressions without considering the interactive effects of decision confidence. Hypotheses proposing that decision confidence would moderate linkages between initial impressions formed at the beginning of the interview and both interview scores and final impressions were not supported. We discuss the theoretical and practical implications of these findings for selection.","PeriodicalId":321336,"journal":{"name":"DecisionSciRN: Recruiting & Hiring (Sub-Topic)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127062531","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper introduces a new role of quotas, e.g., labor market quotas: the attentional role. We study the effect of quota implementation on the attention allocation strategy of a rationally inattentive (RI) agent. Our main result is that a RI agent who is forced to fulfill a quota never hires the candidates without acquiring information about them, unlike an unrestricted RI agent who in some cases bases her decision on prior belief only. We also show that in our context quotas are equivalent to other types of affirmative policies such as subsidies and blind resume policy. We show how our results can be used to set a quota level that increases the expected value of the chosen candidate and also decreases statistical discrimination and discrimination in terms of how much attention is paid to each applicant. At the same time, quota implementation could be destructive if the social planner has imperfect information about the parameters of the model.
{"title":"Attentional Role of Quota Implementation","authors":"A. Matveenko, Sergei Mikhalishchev","doi":"10.2139/ssrn.3500604","DOIUrl":"https://doi.org/10.2139/ssrn.3500604","url":null,"abstract":"This paper introduces a new role of quotas, e.g., labor market quotas: the attentional role. We study the effect of quota implementation on the attention allocation strategy of a rationally inattentive (RI) agent. Our main result is that a RI agent who is forced to fulfill a quota never hires the candidates without acquiring information about them, unlike an unrestricted RI agent who in some cases bases her decision on prior belief only. We also show that in our context quotas are equivalent to other types of affirmative policies such as subsidies and blind resume policy. We show how our results can be used to set a quota level that increases the expected value of the chosen candidate and also decreases statistical discrimination and discrimination in terms of how much attention is paid to each applicant. At the same time, quota implementation could be destructive if the social planner has imperfect information about the parameters of the model.","PeriodicalId":321336,"journal":{"name":"DecisionSciRN: Recruiting & Hiring (Sub-Topic)","volume":"199 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124466576","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Even as laws have been enacted to grant equal opportunity to job applicants, new socio-technical developments have ushered in novel mechanisms for discrimination. The high bar of proof to demonstrate a disparate impact cause of action under Title VII of the Civil Rights Act, coupled with the “black box” nature of many automated hiring systems, renders the detection and redress of bias in such algorithmic systems difficult. This Article, with contributions at the intersection of administrative law, employment & labor law, and law & technology, makes the central claim that the automation of hiring both facilitates and obfuscates employment discrimination. That phenomenon and the deployment of intellectual property law as a shield against the scrutiny of automated systems combine to form an insurmountable obstacle for disparate impact claimants. The first contribution of this Article then is an eye-opening, in-depth examination of how bias is introduced, replicated, and also hidden by automated hiring systems. The second contribution is a hybrid approach to remedies that moves beyond the litigation-based paradigm in employment law to include redress mechanisms from administrative and labor law. To ensure against the identified “bias in, bias out” phenomenon associated with automated decision-making, I argue that the goal of equal opportunity in employment creates an “auditing imperative” for algorithmic hiring systems. This auditing imperative mandates both internal and external audits of automated hiring systems, as well as record-keeping initiatives for job applications. Such audit requirements have precedent in other areas of law, as they are not dissimilar to the Occupational Safety and Health Administration (OSHA) audits in labor law or the Sarbanes-Oxley Act audit requirements in securities law. Conjointly, I propose that employers that subject their automated hiring platforms to external audits could receive a certification mark, “the Fair Automated Hiring Mark,” which would serve to positively distinguish them in the labor market. I also discuss how labor law mechanisms such as collective bargaining could be an effective approach to combating the bias in automated hiring by establishing criteria for the data deployed in automated employment decision-making and creating standards for the protection and portability of said data. The Article concludes by noting that automated hiring, which captures a vast array of applicant data, merits greater legal oversight given the potential for “algorithmic blackballing,” a phenomenon that could continue to thwart an applicant’s future job bids.
尽管已经制定了法律，为求职者提供平等的机会，但新的社会技术发展带来了新的歧视机制。根据《民权法案》(Civil Rights Act)第七条，证明不同影响的诉因的证据门槛很高，再加上许多自动化招聘系统的“黑匣子”性质，使得在此类算法系统中发现和纠正偏见变得困难。本文通过对行政法、就业与劳动法以及法律与技术的交叉研究，提出了一个核心主张，即招聘自动化既促进了就业歧视，也混淆了就业歧视。这种现象，再加上利用知识产权法作为盾牌来抵御自动化系统的审查，对不同影响的索赔人构成了不可逾越的障碍。本文的第一个贡献是大开眼界，深入研究了自动化招聘系统是如何引入、复制和隐藏偏见的。第二个贡献是一种混合的补救办法，超越了就业法中以诉讼为基础的范例，纳入了行政法和劳动法的补救机制。为了防止与自动化决策相关的“入偏出偏”现象，我认为，就业机会均等的目标为算法招聘系统创造了一种“审计的必要性”。这种审计要求对自动招聘系统进行内部和外部审计，以及对工作申请进行记录保存。这种审计要求在其他法律领域有先例，因为它们与劳动法中的职业安全与健康管理局(OSHA)审计或证券法中的萨班斯-奥克斯利法案审计要求没有什么不同。同时，我建议将其自动化招聘平台纳入外部审计的雇主可以获得一个认证标志，“公平自动化招聘标志”，这将有助于在劳动力市场上积极区分他们。我还讨论了劳动法机制，如集体谈判，如何通过为自动化就业决策中部署的数据建立标准，并为所述数据的保护和可移植性创建标准，成为对抗自动化招聘中的偏见的有效方法。文章最后指出，考虑到潜在的“算法排斥”现象(这种现象可能会继续阻碍求职者未来的工作竞标)，自动招聘需要更大的法律监督，因为它捕获了大量求职者的数据。
{"title":"An Auditing Imperative for Automated Hiring","authors":"Ifeoma Ajunwa","doi":"10.2139/ssrn.3437631","DOIUrl":"https://doi.org/10.2139/ssrn.3437631","url":null,"abstract":"Even as laws have been enacted to grant equal opportunity to job applicants, new socio-technical developments have ushered in novel mechanisms for discrimination. The high bar of proof to demonstrate a disparate impact cause of action under Title VII of the Civil Rights Act, coupled with the “black box” nature of many automated hiring systems, renders the detection and redress of bias in such algorithmic systems difficult. This Article, with contributions at the intersection of administrative law, employment & labor law, and law & technology, makes the central claim that the automation of hiring both facilitates and obfuscates employment discrimination. That phenomenon and the deployment of intellectual property law as a shield against the scrutiny of automated systems combine to form an insurmountable obstacle for disparate impact claimants.\u0000 The first contribution of this Article then is an eye-opening, in-depth examination of how bias is introduced, replicated, and also hidden by automated hiring systems. The second contribution is a hybrid approach to remedies that moves beyond the litigation-based paradigm in employment law to include redress mechanisms from administrative and labor law. To ensure against the identified “bias in, bias out” phenomenon associated with automated decision-making, I argue that the goal of equal opportunity in employment creates an “auditing imperative” for algorithmic hiring systems. This auditing imperative mandates both internal and external audits of automated hiring systems, as well as record-keeping initiatives for job applications. Such audit requirements have precedent in other areas of law, as they are not dissimilar to the Occupational Safety and Health Administration (OSHA) audits in labor law or the Sarbanes-Oxley Act audit requirements in securities law. Conjointly, I propose that employers that subject their automated hiring platforms to external audits could receive a certification mark, “the Fair Automated Hiring Mark,” which would serve to positively distinguish them in the labor market. I also discuss how labor law mechanisms such as collective bargaining could be an effective approach to combating the bias in automated hiring by establishing criteria for the data deployed in automated employment decision-making and creating standards for the protection and portability of said data. The Article concludes by noting that automated hiring, which captures a vast array of applicant data, merits greater legal oversight given the potential for “algorithmic blackballing,” a phenomenon that could continue to thwart an applicant’s future job bids.","PeriodicalId":321336,"journal":{"name":"DecisionSciRN: Recruiting & Hiring (Sub-Topic)","volume":"85 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134219159","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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