Assessing biases through visual contexts

Bias detection in the computer vision field is a necessary task, to achieve fair models. These biases are usually due to undesirable correlations present in the data and learned by the model. Although explainability can be a way to gain insights into model behavior, reviewing explanations is not straightforward. This work proposes a methodology to analyze the model biases without using explainability. By doing so, we reduce the potential noise arising from explainability methods, and we minimize human noise during the analysis of explanations. The proposed methodology combines images of the original distribution with images of potential context biases and analyzes the effect produced in the model’s output. For this work, we first presented and released three new datasets generated by diffusion models. Next, we used the proposed methodology to analyze the context impact on the model’s prediction. Finally, we verified the reliability of the proposed methodology and the consistency of its results. We hope this tool will help practitioners to detect and mitigate potential biases, allowing them to obtain more reliable models.This work received funding from the European Union’s H2020-INFRAIA-2019-1 program under the Grant Agreement n.871042 (SoBigData++: European Integrated Infrastructure for Social Mining and Big Data Analytics) and from the HORIZON-INFRA-2021-DEV-02 program under the Grant Agreement n.101079043 (SoBigData RI Preparatory Phase Project). Additionally, this work was supported by the Departament de Recerca i Universitats of the Generalitat de Catalunya, under the Industrial Doctorate Grant DI 2018-100.Peer ReviewedPostprint (published version