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As AI systems increasingly influence critical workplace decisions such as resume screening, performance evaluations, promotion recommendations, and compensation analysis, organizations face a significant challenge: how to harness AI's efficiency while maintaining transparency and fairness. Research from Harvard's School of Engineering and Applied Sciences reveals that "many of the algorithms used by recruiters to manage their hiring process have been shown to reproduce, (and sometimes amplify), biases and human errors they are supposed to eliminate". These systems often perpetuate embedded biases from their training data, which was written by predominantly light-skinned, able-bodied men. Thus, the AI training data will inherently mimic a certain world view that may not take into account the systemic inequities that are still built into our government, education and business structures.

McKinsey research shows that hiring for skills is five times more predictive of job performance than hiring based on education, yet the transition requires substantial organizational commitment. Forward-thinking organizations must develop sophisticated approaches for identifying, assessing and developing skills because the most successful and retained hires still have room for growth.