Explainable AI XAI Frameworks

Explainable AI frameworks such as LIME (Local Interpretable Model-agnostic Explanations) and SHAP (SHapley Additive exPlanations) help HR professionals understand how AI models make decisions. By providing clear, interpretable insights into the algorithms’ reasoning, these tools ensure transparency and help avoid biases in recruitment and employee evaluation.

Explainable AI frameworks such as LIME (Local Interpretable Model-agnostic Explanations) and SHAP (SHapley Additive exPlanations) help HR professionals understand how AI models make decisions. By providing clear, interpretable insights into the algorithms’ reasoning, these tools ensure transparency and help avoid biases in recruitment and employee evaluation.

Empowered by Artificial Intelligence and the women in tech community.
Like this article?

Using AI in Talent Analytics Ethically and Transparently

Interested in sharing your knowledge ?

Learn more about how to contribute.

Sponsor this category.

More articles on Using AI in Talent Analytics Ethically and Transparently

See all

More articles from related categories

What Challenges Arise When Measuring the Impact of Diversity on Business KPIs?
Correlating Diversity Metrics with Performance KPIs
In What Ways Can AI Promote Inclusive Talent Acquisition and Retention for Women in Tech?
Using AI in Talent Analytics Ethically and Transparently
How Can Bias Awareness Training Transform the Tech Hiring Landscape?
Tools for Identifying Unconscious Bias Patterns in Hiring
How Do Predictive Hiring Tools Impact Gender Diversity and Retention Rates in Technology Teams?
Predictive Hiring Analytics