Assessing Biases through Visual Contexts
نویسندگان
چکیده
Bias detection in the computer vision field is a necessary task, to achieve fair models. These biases are usually due undesirable correlations present data and learned by model. Although explainability can be way gain insights into model behavior, reviewing explanations not straightforward. This work proposes methodology analyze without using explainability. By doing so, we reduce potential noise arising from methods, minimize human during analysis of explanations. The proposed combines images original distribution with context analyzes effect produced model’s output. For this work, first presented released three new datasets generated diffusion Next, used impact on prediction. Finally, verified reliability consistency its results. We hope tool will help practitioners detect mitigate biases, allowing them obtain more reliable
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ژورنال
عنوان ژورنال: Electronics
سال: 2023
ISSN: ['2079-9292']
DOI: https://doi.org/10.3390/electronics12143066