Towards Transparent Systems: Semantic Characterization of Failure Modes

By Aayush Bansal, Ali Farhadi, Devi Parikh. 2014

42 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications, Information Systems

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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, background): “These include: • Transparency (Bansal, Farhadi, and Parikh, 2014; Fallon and Blaha, 2018; Hayes and Shah, 2017; Wachter, Mittelstadt, and Russel, 2017), • Accountability (Adler, et al., 2018; Belotti and Edwards, 2001; Kroll et al, 2016), • Safety and privacy (Amodei, Olah, Steinhardt, Christiano,…”
Do explanations make VQA models more predictable to a human? (2018, methodology): “While Bansal et al. (2014) and Zhang et al. (2014) predict failures of a model using simpler statistical models, we explicitly train a person to do this.”
Relaxed Softmax: Efficient Confidence Auto-Calibration for Safe Pedestrian Detection (2018, background): “Several authors studied the reliability of classical logistic regression [1] and SVM [13, 29, 7] classiﬁers.”
Semantically Equivalent Adversarial Rules for Debugging NLP models (2018, background): “Producing such adversarial examples systematically can significantly aid in debugging ML models, as it allows users to detect problems that happen in the real world, instead of oversensitivity only to malicious attacks such as intentionally scrambling, misspelling, or removing words (Bansal et al., ”
On Interpretability of Artificial Neural Networks: A Survey (2020, background): “For example, A. Bansal et al. [12] developed a model-agnostic algorithm to identify which instances a neural network is likely to fail to provide any prediction for.”
It Takes Two to Tango: Towards Theory of AI's Mind (2017, background): “Their ability to estimate Vicki’s success or failure in a scenario is a measure of how well they understand its strengths and weaknesses.”
Introspective perception: Learning to predict failures in vision systems (2016, background): “[21] learn a semantic characterization of failure modes.”
Towards Accountable AI: Hybrid Human-Machine Analyses for Characterizing System Failure (2018, methodology): “Previous work in error detection (Zhang et al. 2014; Bansal, Farhadi, and Parikh 2014) (Nushi et al. 2017).”
Association for Computational Linguistics " Why Should I Trust You? " Explaining the Predictions of Any Classifier (2016, unknown): “Some recent work aims to anticipate failures in machine learning, specifically for vision tasks [3, 29].”