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AI4People—An Ethical Framework for a Good AI Society: Opportunities, Risks, Principles, and Recommendations

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TLDR
The core opportunities and risks of AI for society are introduced; a synthesis of five ethical principles that should undergird its development and adoption are presented; and 20 concrete recommendations are offered to serve as a firm foundation for the establishment of a Good AI Society.
Abstract
This article reports the findings of AI4People, an Atomium—EISMD initiative designed to lay the foundations for a “Good AI Society”. We introduce the core opportunities and risks of AI for society; present a synthesis of five ethical principles that should undergird its development and adoption; and offer 20 concrete recommendations—to assess, to develop, to incentivise, and to support good AI—which in some cases may be undertaken directly by national or supranational policy makers, while in others may be led by other stakeholders. If adopted, these recommendations would serve as a firm foundation for the establishment of a Good AI Society.

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Journal ArticleDOI

The global landscape of AI ethics guidelines

TL;DR: In this article, a global convergence emerging around five ethical principles (transparency, justice and fairness, non-maleficence, responsibility and privacy), with substantive divergence in relation to how these principles are interpreted, why they are deemed important, what issue, domain or actors they pertain to, and how they should be implemented.
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The Ethics of AI Ethics -- An Evaluation of Guidelines

TL;DR: In this paper, a comprehensive evaluation of AI ethics guidelines is presented, highlighting overlaps but also omissions, and the extent to which the respective ethical principles and values are implemented in the practice of research, development and application of AI systems.
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Principles alone cannot guarantee ethical AI

TL;DR: Brent Mittelstadt highlights important differences between medical practice and AI development that suggest a principled approach to AI development may not work in the case of AI.
Proceedings ArticleDOI

“Everyone wants to do the model work, not the data work”: Data Cascades in High-Stakes AI

TL;DR: In this paper, the authors report on data practices in high-stakes AI, from interviews with 53 AI practitioners in India, East and West African countries, and USA, and define, identify, and present empirical evidence on Data Cascades, compounding events causing negative, downstream effects from data issues.
Journal ArticleDOI

Principles alone cannot guarantee ethical AI

TL;DR: Significant differences exist between medical practice and AI development that suggest a principled approach may not work in the case of AI, and Brent Mittelstadt highlights these differences.
References
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Book

The Ethics of Information

TL;DR: In this article, the authors present a defence of information-theory in the context of e-NVIRONMENTAL ETHICS, which they call the "defence of information business ethics".

Ethics of Information

TL;DR: In this article, the authors present a defence of information-theory in the context of e-NVIRONMENTAL ETHICS, which they call the "defence of information business ethics".
Journal ArticleDOI

Soft Ethics and the Governance of the Digital

TL;DR: In this paper, the relation between the ethics, the law, and the governance of the digital world is discussed and the most reasonable answer is given. But it is not discussed in detail.
Journal ArticleDOI

Artificial Intelligence Crime: An Interdisciplinary Analysis of Foreseeable Threats and Solutions

TL;DR: This article offers the first systematic, interdisciplinary literature analysis of the foreseeable threats of AIC, providing ethicists, policy-makers, and law enforcement organisations with a synthesis of the current problems, and a possible solution space.
Journal ArticleDOI

Soft Ethics and the Governance of the Digital

TL;DR: In this paper, the relation between the ethics, the law, and the governance of the digital world is discussed and the most reasonable answer is given. But it is not discussed in detail.
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