Growing media attention has exposed critical issues concerning intelligent systems’ efficiency in aiding/automating decisions with direct human/societal impact. As a result, trustworthiness in Artificial Intelligence (AI) is now an unavoidable issue that we must tackle urgently. Massive efforts are in place, especially related to explainability and causality, providing critical insights into how systems behave in specific moments.

The aim of the track of Ethics and Responsibility in AI (ERAI) is, firstly, to collect contributions describing innovative approaches to address ethical challenges in AI. Secondly, focusing on trustworthiness, explainability, and interpretability, we also aim to accept contributions on this subject to advance or improve responsibility and accountability in AI. Finally, to address the disparity between the proliferation of research and the need for practical solutions that also focus on social, ethical, and privacy aspects. Real-world applications, from design to implementation level, are welcome.

TOPICS OF INTEREST

• Trustworthiness, explainability, and interpretability to promote Ethics and Responsibility in AI
• Social, ethical, and privacy aspects of Ethics and Responsibility in AI
• Metrics for evaluating Ethics and Responsibility in AI
• Human in the Loop for Ethics and Responsibility in AI
• Bias challenges in Ethics and Responsibility in AI
• Visualization and interaction strategies
• Applications of Ethics and Responsibility in AI