We invite submission of original papers on Artificial Intelligence & Law, covering the following topics but are not limited to:

For BEFAIR2 track:

• – Methods for detecting algorithmic discrimination
• – Bias in machine learning process, including data collection, data preparation, modeling, evaluation, and deployment
• – Debiasing strategies for the legal domain
• – Ethical behavior from autonomous systems
• – Legal design, visual law and legal knowledge base visualization
• – Design justice and fairness by design
• – Differences and similarities between legal and moral reasoning
• – Ethics in data mining and argument mining from legal databases and texts
• – Fairness analysis on computer-assisted dispute resolution
• – Counterfactual reasoning for fairness
• – Algorithmic fairness and learning challenges
• – Demographic Parity, Equal Opportunity, Equalized Odds, Disparate Treatment, Counterfactual Fairness, Fairness through Awareness, and other fairness definitions
• – The impossibility theorem of fairness definitions
• – Measurement and mitigation of unfairness in machine learning
• – Transparency and accountability as a fundamental design requirement for AI&L
  architectures
• – 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)
• – 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
• – Deep learning and data analytics applied to ethics in the legal domain
• – Explainability, interpretability, traceability, data and model lineage

For EMAI track:

• – Ethical issues in knowledge representation and legal reasoning
• – Formal and computational models of ethical reasoning in the legal domain (e.g.
  using argumentation frameworks, case-based reasoning)
• – Computational models of ethical decision making
• – Computational models of moral reasoning
• – Computational models of various ethical theories
• – Moral decision-making frameworks for legal artificial agents
• – Ethics, ontologies and legal knowledge representation
• – Experimental implementation of moral and ethical reasoning systems into AI
  driven devices
• – Transparency of ethical decision making
• – Value-based reasoning
• – Implementation of ethics in machine learning-based systems
• – Deep learning and data analytics applied to ethics in the legal domain
• – Integration of data- and knowledge-driven approaches to model ethical decision
  making
• – Argumentation with values
• – Ethical constraints for reinforcement learning models
• – The models of evaluation of cases in the light of different values