Explainable AI: from black box to glass box

By Arun Rai. Journal of the Academy of Marketing Science, 2019

1,050 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Health Informatics

View Paper: https://www.semanticscholar.org/paper/2cc3338709ea9c14ff422025ae4a8ad09f9598ba

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

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Citations in the corpus

Trustworthy artificial intelligence (2020, background): “…of major importance since it will not only allow organizations to meet compliance requirements when employing AI (e.g., by means of enabling independent third-party audits) but will also be a key driver for acceptance of AI by managers, the general workforce, and consumers (Hagras 2018; Rai 2020).”
Setting the future of digital and social media marketing research: Perspectives and research propositions (2020, background): “Explainable AI provides a precise rationale of the marketing decision-making process and provides marketing managers with a human-understandable explanation (Overgoor et al., 2019; Rai, 2020).”
The future of technology and marketing: a multidisciplinary perspective (2019, background): “In addition, Rai (2020) discusses that if patients are to trust and rely on personalized recommendations, explaining why these recommendations are made is critical (i.e., providing more visibility into the AI algorithm), especially for recommendations that are consequential to their health.”
Measuring Bias (2023, methodology): “Such models are used in different fields such as cancer diagnosis in medicine, credit scoring in financial services, resume screening in job applications, recidivism predictions in criminal justice, and so on [43].”
Transforming business using digital innovations: the application of AI, blockchain, cloud and data analytics (2020, neutral): “Although three-quarters of executives believe that AI will help businesses further develop and enable them to achieve a competitive advantage, research states that companies are yet to put AI into practice (Rai 2020; Davenport and Ronanki 2018).”
Unbox the black-box for the medical explainable AI via multi-modal and multi-centre data fusion: A mini-review, two showcases and beyond (2021, background): “Comprehensive surveys on general XAI can be found elsewhere, e.g., [1–4]; therefore, here we provide an overview of most important concepts of the XAI.”
A Systematic Literature Review of User Trust in AI-Enabled Systems: An HCI Perspective (2022, background): “Nevertheless, with this uptake, more concerns have been raised about certain AI characteristics, such as being opaque ( “ black box ” ), uninterpretable, and biased, and the risks these characteristics impose (Gulati et al., 2019; Rai, 2020).”
Augmenting Medical Diagnosis Decisions? An Investigation into Physicians' Decision-Making Process with Artificial Intelligence (2021, background): “Whereas much research currently focuses on how to develop more accurate and transparent AI-based systems to counter these challenges (Rai 2020), too little effort is spent on understanding the cognitive challenges of decision makers that hinder successful decision augmentation with AI advice…”
Explainable Artificial Intelligence Applications in Cyber Security: State-of-the-Art in Research (2022, background): “Arun [82] proposed concerns about the role of XAI in 539”
Generative AI (2023, neutral): “Yet, due to the black-box nature of state-of-the-art AI models (Rai 2020), the usage of such systems critically hinges on users’ trust in reliable outputs.”
Explainability in Graph Neural Networks: A Taxonomic Survey (2020, background): “Recent survey works [23], [24], [25], [26] provides good systematic reviews and taxonomies of these”
Transparency and the Black Box Problem: Why We Do Not Trust AI (2021, methodology): “An important question, however, is whether XAI gives us reasons to trust the system that is being modeled or what seems more likely, the model itself (Rai, 2020, 139).”
A strategic framework for artificial intelligence in marketing (2020, background): “Thus, there is a need for scholars to develop explainable AI so that thinking AI can be used for trustworthy and fair marketing exchanges (Rai 2020).”