AI/ML and other Big Data technologies are pervasive features of modern life that not only increasingly mediate our lives (e.g. – product recommenders, traffic navigation apps), but modulate them, as well (e.g. – recidivism prediction systems, resume screening). Their scale, ubiquity, and influence within social structures means ethical considerations are not negligible. While the potential benefits of AI and other Big Data technologies are undeniable, the hazards are numerous and recent years have seen harms befall individuals, groups, and, in some cases, entire societies. Much of the response has focused on high-level ethical principles and frameworks, but practitioners often struggle to put these principles into practice.

This workshop seeks to establish an international venue that brings together researchers and practitioners to promote awareness and research activities on ethical issues in AI and Data science, showcase new methods and best practices, explore the unknown and challenges, and foster cross-cultural collaboration and exchange of ideas. We expressly welcome research that interfaces with abstract normative concepts (e.g. – justice, fairness, trustworthiness, beneficence), moral stakeholders, and wicked sociotechnical problems to characterize issues and understand fundamental tensions and trade-offs. We also welcome research focused on ethically-aware methods for AI and Big Data, from developing diagnostic tools and best practices to formulating contextually-appropriate interventions and technical tools to mitigate harms where possible.

Research facilitating deeper understanding of the Applied AI Ethics problemdomain including, but not limited to:
- Taxonomical research
- Case studies and empirical investigations
- Applied philosophical work
- Relevant work from sociology, psychology, and other socialsciences
- Best practices and procedures, including diagnostic tools, documentation for improving transparency and accountability, and approaches to oversight and governance
- Application papers discussing specific implementations
- Ethically-aware methods in AI, Machine Learning and Big Data
- Contributions to specific sub-domains of Applied AI Ethics, including but not limited to:
--Fairness and Bias (FairML)
--Explainability and Interpretability (XAI)
--Robustness, Reliability, and Trustworthiness (RobustML, AI Alignment, AI Safety)
--Privacy, Anonymity, and Security (Private and Secure AI)
--Sustainability
--Oversight and Governance
--Foresight, Feedback, and Ethical Alignment in Dynamical Systems
--Transparency and Accountability
--Beneficence, Non-maleficence, and AI for Social Good (AI4SG)