HR technology is becoming increasingly regulated. Laws in New York, California, and Illinois are among the regulations that may soon influence the decision making of companies.
With the great resignation and war for talent continuing to accelerate, HR leaders can’t afford to lose traction in their hiring and workforce efforts because of back-fitting compliant tech systems. Understanding the components of responsible AI–explainability, fairness algorithms, unintended bias detection–and what to look for in a compliant solution now can mitigate that risk and continue reinforcing DEI efforts.
In this video, retrain.ai Co-Founder Isabelle Bichler-Eliasaf discusses what constitutes Responsible AI, how to identify if technology solutions are at risk, and how retrain.ai is building a Responsible AI Talent Intelligence System.
Key Takeaways:
- Learn about fairness algorithms and how they work to protect against unintended bias
- Learn the key differences between semantics-based models and keyword-based models
- Understand the importance of checking your AI system regularly and testing to optimize unbiased machine learning
- Discover how to best prepare for the future using RAI for a more equitable workforce
