Technology & Management Review

Peer Reviewed Article

Vol. 4 (2019)

Explainable AI: Meeting Transparency and Accountability Demands in Data Science

Published
2019-05-30

Abstract

As artificial intelligence (AI) continues to permeate various aspects of society, the demand for transparency and accountability in AI systems, particularly in the context of data science, becomes increasingly critical. This article delves into the challenges and imperatives of achieving explainability in AI, addressing the ethical concerns associated with opaque algorithms. We explore the current landscape of Explainable AI (XAI) techniques and methodologies, evaluating their efficacy in meeting the growing demands for transparency. Additionally, the article discusses the role of explainability in fostering accountability, not only in algorithmic decision-making but also in shaping policies and regulations that govern AI applications. Through a comprehensive examination of real-world cases and emerging standards, we aim to provide insights into the evolving intersection of Explainable AI, transparency, and accountability in the dynamic field of data science.

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