As we use automated decision support systems to formalize, scale, and accelerate processes, we have the opportunity, as well as the duty, to revisit the existing processes for the better, avoiding perpetuating existing patterns of injustice, by detecting, diagnosing and repairing them. To trust these systems, domain experts and stakeholders need to trust the decisions. Despite the increased amount of work in this area in the last few years, we still lack a comprehensive understanding of how pertinent concepts of bias or discrimination should be interpreted in the context of AI and which socio-technical options to combat bias and discrimination are both realistically possible and normatively justified.

This workshop provides a forum for the exchange of ideas, presentation of results and preliminary work in all areas related to fairness and bias in AI; including, but not limited to:

Bias and Fairness by Design; Fairness measures and metrics; Counterfactual reasoning; Metric learning; Impossibility results; Multi-objective strategies for fairness, explainability, privacy, class-imbalancing, rare events, etc.; Federated learning; Resource allocation; Personalized interventions; Debiasing strategies on data, algorithms, procedures; Human-in-the-loop approaches; Methods to Audit, Measure, and Evaluate Bias and Fairness; Auditing methods and tools; Benchmarks and case studies; Standard and best practices; Explainability, traceability, data and model lineage; Visual analytics and HCI for understanding/auditing bias and fairness; HCI for bias and fairness; Software engineering approaches; Legal perspectives on fairness and bias; Social and critical perspectives on fairness and bias