Safe and Interpretable Machine Learning: A Methodological Review

By Clemens Otte. 2013

59 citations recorded by the source corpus.

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In Artificial Intelligence

Citations in the corpus

Explanation in Human-AI Systems: A Literature Meta-Review, Synopsis of Key Ideas and Publications, and Bibliography for Explainable AI (2019, neutral): “Reporting and opinion pieces in the past several years have discussed social justice, equity, and fairness issues that are implicated by "inscrutable" AI (Adler, et al., 2018; Amodei, et al., 2016; Belotti and Edwards, 2001; Bostrom andYudkowsky, 2014; Dwork, et al., 2012; Fallon and Blaha, 2018; Ha”
IMLI: An Incremental Framework for MaxSAT-Based Learning of Interpretable Classification Rules (2019, background): “For domains such as movie recommendations and ad predictions, accuracy is usually the primary objective but for safety critical domains (Otte 2013) such as medical and legal, interpretability, privacy, and fairness (Barocas, Hardt, and Narayanan 2017) are of paramount importance.”
The Threat of Algocracy: Reality, Resistance and Accommodation (2016, background): “in making such systems more interpretable (Vellido, Guerrero and Lisboa 2012; Lisboa 2013; Otte 2013; Chase Lipton 2015; Zeng, Ustun and Rudin 2015).”
A Roadmap for a Rigorous Science of Interpretability (2017, background): “…has led to a surge of interest in systems optimized not only for expected task performance but also other important criteria such as safety [Otte, 2013, Amodei et al., 2016, Varshney and Alemzadeh, 2016], nondiscrimination [Bostrom and Yudkowsky, 2014, Ruggieri et al., 2010, Hardt et al.,…”
Explainable Neural Computation via Stack Neural Module Networks (2018, background): “Recent years have seen increased interest in various aspects of interpretability in learned models [24], particularly in neural networks [23].”
Explainable Artificial Intelligence: a Systematic Review (2020, unknown): “The fifth cluster contains reviews that analysed the methods for explainability for either regression [47] or classification [31, 48, 49] problems.”
Probabilistic Object Detection: Definition and Evaluation (2018, background): “Mistakes in object detection could lead to catastrophic outcomes that not only risk the success of the robot’s mission, but potentially endanger human lives [1, 2, 24, 32, 35, 49].”
Asking 'Why' in AI: Explainability of intelligent systems - perspectives and challenges (2018, background): “While many examples are given by the authors (and others, e.g., Lipton, 2017) — including being nondiscriminatory (as in fairness), safety (Otte, 2013), and satisfying a user's right to explanation (as in Goodman & Flaxman, 2016) — there does not yet appear to be a comprehensive typology of these…”