Generative AI models should be evaluated for potential biases or harmful outputs that could perpetuate discrimination or misinformation.
- Bias Auditing: Evaluating whether the model exhibits racial, gender, or cultural biases in the generated content. For instance, does a text generation model show gender bias in pronouns, or does an image model produce biased representations of different ethnic groups?
- Fairness Metrics: Analyzing whether the outputs are equitable and free from harmful stereotypes. This might involve testing the model with inputs that probe for bias, such as names associated with different genders or ethnicities, to see if the model generates biased or discriminatory content.
Tools and Techniques for Bias Testing:
- AI Fairness 360 (IBM)
- Fairness Indicators (Google)
- Bias in AI Datasets: Use of benchmark datasets like CrowS-Pairs for testing racial and gender biases in language models.
