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

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

“…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.…”

“…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).…”

“…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).…”

“…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.…”

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

“…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 ).…”