New Research on AI and Fairness in Hiring

"Will AI improve or degrade fairness? With nearly 90% of companies now using some form of AI in hiring, this question is top of mind for many leaders, and it tends to split them into two camps. One side believes algorithms will make hiring fairer by reducing human "bias" and "noise" in decision-making. The other warns that algorithms can reproduce and even amplify existing inequalities at scale. Both overlook a crucial reality: When AI is adopted, it reshapes what counts as fair in the first place."

"One side believes algorithms will make hiring fairer by reducing human "bias" and "noise" in decision-making. The other warns that algorithms can reproduce and even amplify existing inequalities at scale. Both overlook a crucial reality: When AI is adopted, it reshapes what counts as fair in the first place."

Nearly 90% of companies now use some form of AI in hiring, producing a sharp split among leaders. One camp expects algorithms to reduce human bias and decision-making noise, improving fairness. The opposing camp warns that algorithms can reproduce and amplify existing inequalities at scale. Both perspectives miss a central consequence: adopting AI changes what organizations treat as fair. AI reconfigures evaluation criteria, decision processes, and standards of fairness, so outcomes depend on how systems are designed, deployed, and integrated into organizational practices.

Read at Harvard Business Review