These tools and metrics are designed to help AI actors develop and use trustworthy AI systems and applications that respect human rights and are fair, transparent, explainable, robust, secure and safe.
Unsupervised bias scan tool
The tool identifies potentially unfairly treated groups of similar users by an algorithmic-driven decision-support system. The tool returns clusters of users for which the system is underperforming compared to the rest of the data set. The tool makes use of clustering – an unsupervised statistal learning method – to detect anomalies. This means that no data are required on protected attributes of users, e.g., gender, nationality or ethnicity, to detect indirect discrimination, also referred to as multidimensional proxy or intersectional discrimination. The metric by which bias is defined can be manually chosen and is referred to as the bias metric.
The tool returns a report which presents the cluster with the highest bias and describes this cluster by the features that characterizes it. This is quantitatively expressed by the (statistically significant) differences in feature means between the identified cluster and the rest of the data. The report also visualizes the outcomes.
Paper: https://arxiv.org/abs/2502.01713
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Tags:
- ai ethics
- trustworthy ai
- validation of ai model
- python
- ai auditing
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