Explainable Artificial Intelligence: Objectives, Stakeholders, and Future Research Opportunities
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This research note describes exemplary risks of black-box AI, the consequent need for explainability, and previous research on Explainable AI (XAI) in information systems research.Abstract:
Artificial Intelligence (AI) has diffused into many areas of our private and professional life. In this research note, we describe exemplary risks of black-box AI, the consequent need for explainab...read more
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Journal ArticleDOI
Explainable artificial intelligence: a comprehensive review
TL;DR: A review of explainable artificial intelligence (XAI) can be found in this article, where the authors analyze and review various XAI methods, which are grouped into (i) pre-modeling explainability, (ii) interpretable model, and (iii) post-model explainability.
Posted Content
User Acceptance of Knowledge-Based System Recommendations: Explanations, Arguments, and Fit
TL;DR: This study examines how the fit between KBS explanations and users’ internal explanations influences acceptance of KBS recommendations and compares the predictions of CFT to those of the person-environment fit (PEF) paradigm to find support for CFT in the sense that people are influenced more by cognitively fitting explanations, however PEF is supported in thesense that people take more time to evaluate the explanation.
Journal ArticleDOI
Human-in-the-loop machine learning: a state of the art
Eduardo Mosqueira-Rey,Elena Hernández-Pereira,David Alonso-Ríos,Jose Ramon Bobes-Bascaran,Ángel Fernández-Leal +4 more
TL;DR: Human-in-the-loop machine learning (HILML) as mentioned in this paper is a new type of interaction between humans and machine learning algorithms, where humans can also be involved in the learning process in other ways.
Journal ArticleDOI
How to explain AI systems to end users: a systematic literature review and research agenda
TL;DR: In this article , the authors investigate how AI systems and their decisions ought to be explained for end users through a systematic literature review, and they provide a design framework for explaining AI systems to end users.
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Deceptive AI Explanations: Creation and Detection
TL;DR: It is confirmed that deceptive explanations can indeed fool humans while machine learning methods can detect seemingly minor attempts of deception with accuracy that exceeds 80\% given sufficient domain knowledge in the form of training data.
References
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Proceedings ArticleDOI
"Why Should I Trust You?": Explaining the Predictions of Any Classifier
TL;DR: In this article, the authors propose LIME, a method to explain models by presenting representative individual predictions and their explanations in a non-redundant way, framing the task as a submodular optimization problem.
Journal ArticleDOI
Mastering the game of Go without human knowledge
David Silver,Julian Schrittwieser,Karen Simonyan,Ioannis Antonoglou,Aja Huang,Arthur Guez,Thomas Hubert,Lucas Baker,Matthew Lai,Adrian Bolton,Yutian Chen,Timothy P. Lillicrap,Fan Hui,Laurent Sifre,George van den Driessche,Thore Graepel,Demis Hassabis +16 more
TL;DR: An algorithm based solely on reinforcement learning is introduced, without human data, guidance or domain knowledge beyond game rules, that achieves superhuman performance, winning 100–0 against the previously published, champion-defeating AlphaGo.
Journal ArticleDOI
Minds, brains, and programs
TL;DR: Only a machine could think, and only very special kinds of machines, namely brains and machines with internal causal powers equivalent to those of brains, and no program by itself is sufficient for thinking.
Book
Minds, Brains, and Programs
TL;DR: In this article, the main argument of this paper is directed at establishing this claim and the form of the argument is to show how a human agent could instantiate the program and still not have the relevant intentionality.
Journal ArticleDOI
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
TL;DR: This Perspective clarifies the chasm between explaining black boxes and using inherently interpretable models, outlines several key reasons why explainable black boxes should be avoided in high-stakes decisions, identifies challenges to interpretable machine learning, and provides several example applications whereinterpretable models could potentially replace black box models in criminal justice, healthcare and computer vision.