Santiago de Compostela, Spain, 20/10/2024
AI-based decision support systems are increasingly deployed in industry, in the public and private sectors, and in policymaking to guide decisions in important societal spheres, including hiring decisions, university admissions, loan granting, medical diagnosis, and crime prediction. As our society is facing a dramatic increase in inequalities and intersectional discrimination, we need to prevent AI systems to amplify this phenomenon but rather mitigate it. 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.
temi di interesse
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
funge da
evento origine per pubblicazione
Unmasking the Shadows: Leveraging Symbolic Knowledge Extraction to Discover Biases and Unfairness in Opaque Predictive Models (articolo in atti, 2024) — Federico Sabbatini, Roberta Calegari