The 5 Things You Need to
Start Minimizing Bias in AI and Machine Learning
A Short and Handy Guide for Data Scientists
Fairer AI is more effective AI. Beat bias to keep your models up and running.
Bias in AI and machine learning leads to bad outcomes for models, organizations, and customers. We've all seen the news stories that feature AI models gone awry, causing real harm to customers and damaging a company's reputation.
So how do you fix bias in AI? How can ML be made more fair and stay fair?
This e-book gives you the top 5 things you need to get started minimizing bias in AI, including:
- The most frequently used group fairness metrics
- Individual fairness vs. group fairness, explained
- Guide for selecting fairness metrics
- How to set up a fairness workflow
- How to think about fairness tradeoffs
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TruEra provides AI Quality solutions that analyze machine learning, drive model quality improvements, and build trust. Powered by enterprise-class Artificial Intelligence (AI) Explainability technology based on six years of research at Carnegie Mellon University, TruEra’s suite of solutions provides much-needed model transparency and analytics that drive high model quality and overall acceptance, address unfair bias, and ensure governance and compliance.