Meta recently released FACET, a new benchmark designed to assess fairness in AI systems that classify images and videos. With over 32,000 labeled images, FACET allows for evaluating biases related to gender, race, age, and other attributes. Meta states that FACET enables more thorough bias testing than previous benchmarks. However, Meta's mixed track record on algorithmic fairness raises questions about how effectively FACET will be leveraged.
A More Comprehensive Benchmark
FACET incorporates detailed labeling not found in prior benchmarks. Images are annotated for physical attributes, demographics, and activities. This granularity enables nuanced bias evaluations like determining if models struggle
