CATANIA B.

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The development of technological solutions satisfying non discriminating requirements is currently one of the main challenges for data processing. Concepts like fairness, i.e., lack of bias, and diversity, i.e., the degree to which different kinds of objects are represented in a dataset, have been recently taken into account in designing...

The data science era is characterized by data-driven automated decision systems (ADS) enabling, through data analytics and machine learning, automated decisions in many contexts, deeply impacting our lives. As such, their downsides and potential risks are becoming more and more evident: technical solutions, alone, are not sufficient and an...

When people-related data are used in automated decision making processes, social inequities can be amplified. Thus, the development of technological solutions satisfying nondiscrimination requirements, in terms of, e.g., fairness, diversity, and coverage, is currently one of the main challenges for the data management and data analytics...

Due to the impact of analytical processes on our life, an increasing effort is being devoted to the design of technological solutions that help humans in measuring the bias introduced by such processes and understanding its causes. Existing solutions can refer to either back-end or front-end stages of the data processing pipeline and usually...

This demo presents covRew, a Python toolkit for rewriting slicing operations in pre-processing pipelines (i.e., pipelines to be executed before further tasks, such as data analytics and machine learning) so that the pipeline execution ensures that protected groups are adequately represented (i.e., covered) in the result. The toolkit includes:...