Recruiters increasingly turn to algorithms and AI-driven tools to streamline hiring. Algorithms are now used for resume parsing, skill matching, video interview assessments, and even personality or soft-skill prediction based on digital data. 

A study from Chicago Booth showed that when algorithms include fairness and diversity constraints, interview pools can become more inclusive without greatly sacrificing candidate “quality.” For instance, firms can increase the chances of candidates from underrepresented groups by subtly adjusting selection priorities. Another example: “exploration-based” models identify applicants whose potential is less known, giving them a chance. These models led to higher hiring rates and more diverse shortlists in tests versus traditional recruiter screening.

However, these tools come with risks. Biases can get baked in via historical hiring data. Algorithms may reinforce existing inequalities if data reflects them. Also, transparency is an issue: candidates and regulators often don’t know which traits or behaviors are being evaluated. 

To maximise benefits, companies are advised to: audit algorithmic tools for bias; clearly communicate to applicants when AI is used; combine human oversight with automated screening; and ensure fairness constraints are built in from the start. This hybrid approach could improve efficiency, inclusivity, and trust in modern hiring.