Workshop: Aims and Scope

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.

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


Images credits:
Banner 1: Anne Fehres and Luke Conroy & AI4Media / Better Images of AI / Data is a Mirror of Us / Licenced by CC-BY 4.0
Banner 2: Image by Comuzi / © BBC / Better Images of AI / Mirror D / Licenced by CC-BY 4.0
Banner 3: Amritha R Warrier & AI4Media / Better Images of AI / error cannot generate / Licenced by CC-BY 4.0