Discusses the growing use of Large Language Models (LLMs) in candidate scoring for recruitment, highlighting both their potential benefits and considerable risks. It details how LLMs analyze candidate data, but focuses heavily on inherent biases (gender, race, age, socioeconomic) that can lead to discriminatory hiring outcomes, citing real-world examples like Amazon and iTutorGroup.

The document also explains the complex and evolving legal and ethical landscape, covering regulations in the EU, US, and Canada and emphasizing the principles of Fairness, Accountability, and Transparency (FAT) and the challenge of the AI's "black box" nature. Finally, it provides strategic recommendations for risk mitigation, stressing the importance of human oversight, robust data governance, and proactive bias detection to ensure responsible and ethical AI deployment in hiring.