Utilize Fairness Metrics and Evaluation Tools

Applying fairness metrics such as demographic parity, equal opportunity, or disparate impact can help in quantifying bias within models. Women professionals can leverage open-source fairness assessment libraries (like IBM’s AI Fairness 360 or Microsoft’s Fairlearn) to systematically detect and monitor bias throughout the ML lifecycle.

Applying fairness metrics such as demographic parity, equal opportunity, or disparate impact can help in quantifying bias within models. Women professionals can leverage open-source fairness assessment libraries (like IBM’s AI Fairness 360 or Microsoft’s Fairlearn) to systematically detect and monitor bias throughout the ML lifecycle.

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

Building a Career as an AI Ethicist

Interested in sharing your knowledge ?

Learn more about how to contribute.

Sponsor this category.

More articles on Building a Career as an AI Ethicist

See all

More articles from related categories

How Can Women Advocate for More Inclusive and Equitable Opportunities in Cloud Architecture?
Becoming a Cloud Solutions Architect
How Do Women Overcome Common Challenges Like Imposter Syndrome in Data Science Careers?
Becoming a Data Scientist: Step-by-Step
Which Automation Tools Are Most Effective for Women Breaking into SDET Positions?
From QA to SDET: The Automation Journey
Which Programming Languages and Tools Are Crucial for Aspiring Machine Learning Engineers?
From Data Analyst to Machine Learning Engineer