Addressing Bias in Machine Learning
Your how-to guide for identifying bias issues and improving fairness in machine learning models
Understand, manage, and avoid fairness challenges in AI.
There is increasing customer and regulator concern over algorithmic bias. And rightfully so - even well-intentioned models can display bias over time.
So what does fairness look like? And how do you ensure fairness as time goes on and data shifts?
This whitepaper gets to the heart of these questions by giving a deep dive into:
- What does it mean for a model to be fair?
- How do models become biased, and how can you think about addressing bias?
- How can you establish a fairness workflow for understanding, measuring, and debugging model fairness issues?
Get the Guide Here
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.