Topics

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