A Manifesto for Responsible AI: Healthcare Use-Case of the TAFES Framework

Document Type

Conference Proceeding

Source of Publication

Lecture Notes in Networks and Systems

Publication Date

10-1-2025

Abstract

This paper articulates a framework on responsible AI, focusing on the TAFES framework, standing on principles of Transparency, Accountability, Fairness, Ethics, and Safety. Taken as a practical use-case, the paper reviews the application of these principles in the design and implementation of an AI-powered diagnostic tool for the early detection of skin cancer in the context of health care. TAFES principles may be applied to the lifecycle of Generative AI use-cases, which faces major challenges: bias, data privacy, lack of explainability, and ethics in various applications, especially in health care. The paper illustrates the application of the guiding principles of TAFES with a hypothetical use-case of an AI-powered diagnostic tool for early skin cancer detection. Following the analysis of such a scenario, the paper concludes with a manifesto that of how policymakers, developers, users, and other stakeholders may harness the transformative power of AI through empathic mitigation of risks to human well-being and essential societal values.

ISBN

[9789819692477]

ISSN

2367-3370

Publisher

Springer Nature Singapore

Volume

1539 LNNS

First Page

367

Last Page

379

Disciplines

Computer Sciences

Keywords

AI governance, AI risk assessment, Deep learning, Generative AI, Large language model

Scopus ID

105020200273

Indexed in Scopus

yes

Open Access

no

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