Section 4.21.1.0.1.1 - Part 1 - Wikipedia - Explainable Artificial Intelligence

Section 4.21.1.0.1.2 - Part 2

Section 4.21.1.0.2.1 - Part 1 - Samek,Wiegand,Muller - Explainable AI: Understanding, Visualizing and Interpretting Deep Learning Models

Section 4.21.1.0.2.2 - Part 2 - Darpa,Guning - Explainable AI (XAI)

Section 4.21.1.0.2.3 - Part 3 - Doran,Schulz,Besold - What Does Explainable AI Really Mean? A New Conceptualization of Perspectives

Section 4.21.1.0.2.4 - Part 4 - Monaco - What is explainable AI and why is it important

Section 4.21.1.0.2.5 - Part 5 - Tjoa,Guan - A Survey of Explainable AI (XAI): toward Medical AI

Section 4.21.1.0.2.6 - Part 6 - Hall - Guidelines for Responsible and Human-Centered Use of Explainable Machine Learning

Section 4.21.1.0.2.7 - Part 7 - Abdul,Vermeulen,Wang,Lim,Kankanhalli - Trends and Trajectories for Explainable, Accountable and Intelligible Systems: An HCI Research Agenda

Section 4.21.1.0.2.8 - Part 8 - Abdul,Wang,Lim,Yang - Designing Theory-Driven User-Centric Explainable AI

Section 4.21.1.0.3.1 - Part 1 - Raphaël Meudec - TF-Explain

Section 4.21.1.0.3.2 - Part 2 - IBM - AI Explainability 360

Section 4.21.2.0.2.1 - Part 1 - Dignum - Responsible AI

Section 4.21.2.0.2.2 - Part 2 - D'Souza - Defining a Sandbox for Responsible AI

Section 4.21.3.0.2.1 - Part 1 - Armstrong,Sotala,Oh´Eigeartaigh - The errors, insights and lessons of famous AI predictions – and what they mean for the future

Section 4.21.3.0.2.2 - Part 2 - Venugopal,Gazettie,Gkuofas - Shadow Puppets: Cloud-level Accurate AI Inference at the Speed and Economy of Edge

Section 4.21.3.0.2.3 - Part 3 - Merler,Ratha,Feris,Smith - Diversity in Faces

Section 4.21.5.0.2.1 - Part 1 - AI Fairness 360: An extensible toolkit for detecting, understanding and mitigating unwanted algorithmic bias

Section 4.21.5.0.2.2 - Part 2 - Vakkuri,Abrahamsson - The Key Concepts of Ethics of AI

Section 4.21.5.0.2.3 - Part 3 - Top 10 Principles for Ethical AI

Section 4.21.5.0.2.4 - Part 4 - Wang,Siau - Ethical and Moral Issues with AI: A Case study on Healthcare Robots

Section 4.21.5.0.2.5 - Part 5 - Hofheinz - The ethics of AI

Section 4.21.5.0.2.6 - Part 6 - Chander,Srinivansan,Chelian,Wang,Uchino - Working with Beliefs: AI Transparency in the Enterprise

Section 4.21.5.0.2.7 - Part 7 - Sethumadhavan,Levulis - Designing for Transparent AI

Section 4.21.5.0.2.8 - Part 8 - Goyal,Mohapatra,Parikh,Batra - Towards Transparent AI Systems: Interpretting Visual Question Answering Models

Section 4.21.5.0.2.9 - Part 9 - Geyik,Ambler,Kenthapadi - Fairness-aware Ranking in Search & Recommendation Systems with Application to LinkedIn Talent Search

Section 4.21.5.0.2.10 - Part 10 - Sühr,Biega,Zehlike - Two-Sided Fairness for Repeated Matchings in Two-Sided Markets: A Case Study of a Ride-Hailing Platform

Section 4.21.5.0.3.1 - Part 1 - IBM - AI Fairness 360