Solving the Black Box Problem: A Normative Framework for Explainable Artificial Intelligence

By Carlos A. Zednik. Philosophy & Technology, 2019

345 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/058ec329a2216d2d689905a9715f1a3563dfbd7a

How researchers see this paper

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

In Artificial Intelligence

1. Longo Luca
2. Warren J. von Eschenbach
3. Mario Brcic
4. Atoosa Kasirzadeh
5. Javier Del Ser
6. Xinyi Zhou

In Computational Theory and Mathematics

1. Warren J. von Eschenbach
2. Longo Luca
3. Atoosa Kasirzadeh
4. Javier Del Ser

Citations in the corpus

• Transparency and the Black Box Problem: Why We Do Not Trust AI (2021, background): “In addition to end users and the technical device itself, we should consider the designers, programmers, “data subjects,” operators, “decision subjects,” and examiners, or those tasked with auditing and inspecting a system (Zednik, 2019).”
• The Algorithmic Leviathan: Arbitrariness, Fairness, and Opportunity in Algorithmic Decision Making Systems (2021, neutral): “Many algorithmic decision-making systems are based on opaque machine learning algorithms such as deep learning (Creel 2020; Zednik 2019).”
• Reasons, Values, Stakeholders: A Philosophical Framework for Explainable Artificial Intelligence (2021, methodology): “popular framework for the analysis of to cognitive systems is used by Zednik [25] to map different analytic techniques (such as input heatmapping), as developed by the explainable AI researchers, to respond to the epistemic demands of six kinds of stakeholders (as suggested by Tomsett et al.”
• Chatbot for Health Care and Oncology Applications Using Artificial Intelligence and Machine Learning: Systematic Review (2021, neutral): “For both users and developers, transparency becomes an issue because they aren’t able to fully understand the solution or intervene to predictably change the chatbot’s behaviour [97].”
• Explainable Artificial Intelligence (XAI) 2.0: A Manifesto of Open Challenges and Interdisciplinary Research Directions (2023, background): “One class of challenge in XAI lies in providing explanations adapted explicitly to the humans receiving them.”