From Human Resources to Human Rights: Impact Assessments for Hiring Algorithms

Over the years, companies have adopted hiring algorithms because they promise wider job candidate pools, lower recruitment costs and less human bias. Despite these promises, they also bring perils. Using them can inflict unintentional harm on individual human rights. These include the five human rights to work, equality and non-discrimination, privacy, free expression and free association. Despite the human rights harms of hiring algorithms, the AI ethics literature has predominantly focused on abstract ethical principles. This is problematic for two reasons. First, AI principles have been criticized for being vague and not actionable. Second, the use of vague ethical principles to discuss algorithmic risks does not provide any accountability. This lack of accountability creates an algorithmic accountability gap. Closing this gap is crucial because, without accountability, the use of hiring algorithms can lead to discrimination and unequal access to employment opportunities. This paper makes two contributions to the AI ethics literature. First, it frames the ethical risks of hiring algorithms using international human rights law as a universal standard for determining algorithmic accountability. Second, it evaluates four types of algorithmic impact assessments in terms of how effectively they address the five human rights of job applicants implicated in hiring algorithms. It determines which of the assessments can help companies audit their hiring algorithms and close the algorithmic accountability gap.

Focus: Employment
Source: Ethics and Information Technology
Readability: Expert
Type: PDF Article
Open Source: No
Keywords: N/A
Learn Tags: Bias AI and Machine Learning Employment Ethics Fairness
Summary: This papers wades into the AI ethics literature using international human rights law as a benchmark for establishing algorithmic accountability. It assesses four types of algorithmic impact assessments in addressing the human rights of applicants impacted by hiring algorithms.