Engineering Equity: How AI Can Help Reduce the Harm of Implicit Bias

Lin, Ying; Hung, Tzu-Wei; Huang, Linus Ta-Lun

doi:10.1007/s13347-020-00406-7

Cited by 26 publications

(18 citation statements)

References 64 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Moreover, we identified a group of articles that established ethical considerations on AI recruiting, while taking a technical perspective. Some papers (Chwastek, 2017;Köchling et al, 2020;Lin et al, 2020;Mujtaba & Mahapatra, 2019;Persson, 2016;Williams et al, 2018) explained emerging ethical problems by looking at the mechanisms of algorithms used. Others (Fernández-Martínez & Fernández, 2020;Pena et al, 2020;Vasconcelos et al, 2018) presented technical solutions to implement ethical principles into algorithmic code or design.…”

Section: Technical Perspectivementioning

confidence: 99%

“…Moreover, human bias (e.g., related to applicants' physical appearance or other attributes) can be reduced, as AI can be taught to ignore people's personal attributes and focus only on specified skills and behaviors (e.g., Bîgu & Cernea, 2019;Chamorro-Premuzic & Akhtar, 2019;Fernández-Martínez & Fernández, 2020). Lastly, human bias can be removed from the process, as the required skills and qualities for successful candidates are not determined by bias-prone intuitions from recruiters, but based on analyzing the characteristics of the company's top performers (Lin et al, 2020).…”

Section: Human and Algorithmic Biasmentioning

confidence: 99%

“…In addition, the use of more personal data, which may lead to more accurate predictions, is controversial (see also the next section). Thus, it is currently an unresolved normative question the extent to which a company may legally and ethically collect, store, and use personal data from applicants, such as the information available on social media platforms (Lin et al, 2020).…”

Section: Privacy and Informed Consentmentioning

confidence: 99%

“…This question becomes even more difficult when firms are not developing the AI themselves, but instead buying the technology from third-party vendors who want to protect their intellectual property and may not be willing to grant full transparency into the algorithms used (Sánchez-Monedero et al, 2020;Tambe et al, 2019). Lin et al (2020) outlined that in the recruiting process, agents with different roles in the collective decision-making process can have a collective responsibility (i.e., each agent fulfills his or her role and shares a collective responsibility). Thus, when a recruiter makes a morally wrong decision based on a problematic recommendation by an AI, which in turn results from the negligence of a software engineer, both the recruiter and the engineer are collectively responsible and accountable for the wrong decision.…”

Section: Accountabilitymentioning

confidence: 99%

See 3 more Smart Citations

Ethics of AI-Enabled Recruiting and Selection: A Review and Research Agenda

2022

View full text Add to dashboard Cite

Companies increasingly deploy artificial intelligence (AI) technologies in their personnel recruiting and selection process to streamline it, making it faster and more efficient. AI applications can be found in various stages of recruiting, such as writing job ads, screening of applicant resumes, and analyzing video interviews via face recognition software. As these new technologies significantly impact people’s lives and careers but often trigger ethical concerns, the ethicality of these AI applications needs to be comprehensively understood. However, given the novelty of AI applications in recruiting practice, the subject is still an emerging topic in academic literature. To inform and strengthen the foundation for future research, this paper systematically reviews the extant literature on the ethicality of AI-enabled recruiting to date. We identify 51 articles dealing with the topic, which we synthesize by mapping the ethical opportunities, risks, and ambiguities, as well as the proposed ways to mitigate ethical risks in practice. Based on this review, we identify gaps in the extant literature and point out moral questions that call for deeper exploration in future research.

show abstract

Section: Technical Perspectivementioning

confidence: 99%

Section: Human and Algorithmic Biasmentioning

confidence: 99%

Section: Privacy and Informed Consentmentioning

confidence: 99%

Section: Accountabilitymentioning

confidence: 99%

See 2 more Smart Citations

Ethics of AI-Enabled Recruiting and Selection: A Review and Research Agenda

2022

View full text Add to dashboard Cite

show abstract

“…This means a number of bias strategies that recently emerge or position themselves in the nexus of different disciplines could not be categorized. For example, this includes bias interventions that directly influence organizational structures by strategically changing diversity-related dimensions ( Feng et al, 2020 ), or how artificial intelligence can tackle behavioral consequences of biases ( Lin et al, 2020 ). Further, change management as a discipline has its own challenges as high rate of failure, contradictory approaches and a lack of empirical evidence ( By, 2005 ).…”

Section: Further Discussionmentioning

confidence: 99%

What Bias Management Can Learn From Change Management? Utilizing Change Framework to Review and Explore Bias Strategies

Nguyen-Phuong-Mai

2021

Front. Psychol.

View full text Add to dashboard Cite

This paper conducted a preliminary study of reviewing and exploring bias strategies using a framework of a different discipline: change management. The hypothesis here is: If the major problem of implicit bias strategies is that they do not translate into actual changes in behaviors, then it could be helpful to learn from studies that have contributed to successful change interventions such as reward management, social neuroscience, health behavioral change, and cognitive behavioral therapy. The result of this integrated approach is: (1) current bias strategies can be improved and new ones can be developed with insight from adjunct study fields in change management; (2) it could be more sustainable to invest in a holistic and proactive bias strategy approach that targets the social environment, eliminating the very condition under which biases arise; and (3) while implicit biases are automatic, future studies should invest more on strategies that empower people as “change agents” who can act proactively to regulate the very environment that gives rise to their biased thoughts and behaviors.

show abstract