An Applied Perspective on Fairness, Accountability, Transparency, and Explainable AI An Introduction to Machine Learning Interpretability

Citations in the corpus

• On the Art and Science of Machine Learning Explanations (2018, background): “While most users will be familiar with the wide-spread use of ANNs in pattern recognition, they are also used for more traditional data mining applications such as fraud detection, and even for regulated applications such as credit scoring [14]”
• Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI) (2018, background): “Available: https://gcrinstitute.org/about/ [36] K. S. Gill, ‘‘Artificial super intelligence: Beyond rhetoric,’’ AI Soc., vol. 31, no. 2, pp. 137–143, 2016.”
• Do Not Trust Additive Explanations (2019, background): “The need for higher transparency and explainability of models has been a hot topic in the last year, both in the Machine Learning community (Gill and Hall, 2018) as well as in the legal community that coined the phrase „Right to Explanation” in the discussion around General Data Protection…”
• Explainable AI: from black box to glass box (2019, background): “A number of classifications of XAI techniques for deep learning models have been proposed (e.g., Hall and Gill 2019; Du et al. 2018; Ribeiro et al. 2016).”