Model and content audits for fairness, drift, and disparate impact.
Bias Detection
- The Challenge AI models can unknowingly learn and amplify historical biases present in data. This can lead to unfair or discriminatory outcomes, creating significant legal, ethical, and reputational risks.
- Our Approach We conduct comprehensive audits of your models and datasets to proactively identify and mitigate fairness issues. Our process includes rigorous testing for performance drift and analyzing for any disparate impact across different demographic groups.
- Our Experience To promote fairness in housing services, we implemented an equity review process that includes ongoing monitoring of waitlist and eligibility decisions to detect and address any potential disparities.
- The Outcomes Build and deploy fairer, more accurate, and more ethical AI systems. Reduce legal and reputational risk while increasing trust with your customers, regulators, and the community.
