Quantifying Bias in Text-to-Image Generative Models
Jordan Vice Naveed Akhtar Richard Hartley Ajmal Mian
Bias in text-to-image (T2I) models can propagate unfair social
representations and may be used to aggressively market ideas or push
controversial agendas. Existing T2I model bias evaluation methods only focus on
social biases. We look beyond that and instead propose an evaluation
methodology to quantify general biases in T2I generative models, without any
preconceived notions. We assess four state-of-the-art T2I models and compare
their baseline bias characteristics to their respective variants (two for
each), where certain biases have been intentionally induced. We propose three
evaluation metrics to assess model biases including: (i) Distribution bias,
(ii) Jaccard hallucination and (iii) Generative miss-rate. We conduct two
evaluation studies, modelling biases under general, and task-oriented
conditions, using a marketing scenario as the domain for the latter. We also
quantify social biases to compare our findings to related works. Finally, our
methodology is transferred to evaluate captioned-image datasets and measure
their bias. Our approach is objective, domain-agnostic and consistently
measures different forms of T2I model biases. We have developed a web
application and practical implementation of what has been proposed in this
work, which is at https://huggingface.co/spaces/JVice/try-before-you-bias. A
video series with demonstrations is available at
https://www.youtube.com/channel/UCk-0xyUyT0MSd_hkp4jQt1Q