We invite submission of original papers on Artificial Intelligence & Law, covering Bias, Ethics and Fairness in knowledge representation and legal reasoning.
Topics may include, but are not limited to, the following:
- • Bias in machine learning process, including legal data collection, legal data preparation, modeling, evaluation, and deployment
- • Methods for detecting algorithmic discrimination
- • Debiasing strategies for the legal domain
- • Ethical issues in knowledge representation and legal reasoning
- • Ontologies and legal knowledge representation
- • Formal and computational models of ethical reasoning in the legal domain (e.g. argumentation frameworks, case-based reasoning)
- • Ethics in data mining and argument mining from databases and texts
- • Ethical behavior from autonomous systems
- • Moral decision-making frameworks for legal artificial agents
- • Legal design, visual law and legal knowledge base visualization
- • Design justice and fairness by design
- • Fairness analysis on computer-assisted dispute resolution
- • Counterfactual reasoning for fairness
- • Algorithmic fairness and learning challenges (e.g. imbalanced data or rare classes)
- • Measurement and mitigation of unfairness in machine learning
- • Deep learning and data analytics applied to ethics in the legal domain
- • Explainability, interpretability, traceability, data and model lineage
- • Transparency and accountability as a fundamental design requirement for AI&L architectures
BEFAIR² is keen to broader its scope to include topics of growing importance. Therefore, we want to invite submission of papers related to innovation and fairness of algorithms.
Papers will be assessed in the same rigorous reviewing procedure as the main conference.