AI Ethics – Too Principled to Fail?

Mittelstadt, Brent

doi:10.2139/ssrn.3391293

Cited by 69 publications

(60 citation statements)

References 67 publications

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“…The virtue ethics approach, on the other hand, is based on character dispositions, moral intuitions or virtues. In the light of these two approaches, the traditional type of AI ethics can be assigned to the deontological concept (Mittelstadt 2019). Ethics guidelines postulate a fixed set of universal principles and maxims which technology developers should adhere to (Ananny 2016).…”

Section: Virtue Ethicsmentioning

confidence: 99%

The Ethics of AI Ethics: An Evaluation of Guidelines

2020

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Current advances in research, development and application of artificial intelligence (AI) systems have yielded a far-reaching discourse on AI ethics. In consequence, a number of ethics guidelines have been released in recent years. These guidelines comprise normative principles and recommendations aimed to harness the "disruptive" potentials of new AI technologies. Designed as a comprehensive evaluation, this paper analyzes and compares these guidelines highlighting overlaps but also omissions. As a result, I give a detailed overview of the field of AI ethics. Finally, I also examine to what extent the respective ethical principles and values are implemented in the practice of research, development and application of AI systems -and how the effectiveness in the demands of AI ethics can be improved.

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Section: Virtue Ethicsmentioning

confidence: 99%

The Ethics of AI Ethics: An Evaluation of Guidelines

2020

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“…These documents borrow heavily from core principles in bioethics or medical ethics (Floridi and Cowls, 2019). But unlike medical ethics, which is guided by the common aim of promoting the health of the patient, AI development does not share one common goal but many goals (Mittelstadt, 2019). Furthermore, many of these AI ethics principles are at tension with each other (Whittlestone et al, 2019).…”

Section: Background 21 Public Opinion and Ai Governancementioning

confidence: 99%

U.S. Public Opinion on the Governance of Artificial Intelligence

Zhang

Dafoe

2020

Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society

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Artificial intelligence (AI) has widespread societal implications, yet social scientists are only beginning to study public attitudes toward the technology. Existing studies find that the public's trust in institutions can play a major role in shaping the regulation of emerging technologies. Using a large-scale survey (N =2000), we examined Americans' perceptions of 13 AI governance challenges as well as their trust in governmental, corporate, and multistakeholder institutions to responsibly develop and manage AI. While Americans perceive all of the AI governance issues to be important for tech companies and governments to manage, they have only low to moderate trust in these institutions to manage AI applications.

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“…According to Mittelstadt [49], at least 63 public-private initiatives have produced statements describing high-level principles, values and other tenets to guide the ethical development, deployment and governance of AI. Important values such as ensuring AI technologies are subject to human direction and control, and avoiding the creation or reinforcement of unfair bias, have been included in many organizations' ethical charters.…”

Section: Ai Principles As Customized Ethical Standardsmentioning

confidence: 99%

“…Important values such as ensuring AI technologies are subject to human direction and control, and avoiding the creation or reinforcement of unfair bias, have been included in many organizations' ethical charters. However, the AI industry lacks proven methods to translate principles into practice [49], and AI principles have been criticized for being vague and providing little to no means of accountability [27,82]. Nevertheless, such principles are becoming common methods to define the ethical priorities of an organization and thus the operational goals for which to aim [34,83].…”

Section: Ai Principles As Customized Ethical Standardsmentioning

confidence: 99%

Closing the AI accountability gap

Raji

Smart

White

et al. 2020

Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency

419

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Rising concern for the societal implications of artificial intelligence systems has inspired a wave of academic and journalistic literature in which deployed systems are audited for harm by investigators from outside the organizations deploying the algorithms. However, it remains challenging for practitioners to identify the harmful repercussions of their own systems prior to deployment, and, once deployed, emergent issues can become difficult or impossible to trace back to their source. In this paper, we introduce a framework for algorithmic auditing that supports artificial intelligence system development end-to-end, to be applied throughout the internal organization development lifecycle. Each stage of the audit yields a set of documents that together form an overall audit report, drawing on an organization's values or principles to assess the fit of decisions made throughout the process. The proposed auditing framework is intended to contribute to closing the accountability gap in the development and deployment of large-scale artificial intelligence systems by embedding a robust process to ensure audit integrity. CCS CONCEPTS • Social and professional topics → System management; Technology audits; • Software and its engineering → Software development process management.

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AI Ethics – Too Principled to Fail?

Cited by 69 publications

References 67 publications

The Ethics of AI Ethics: An Evaluation of Guidelines

The Ethics of AI Ethics: An Evaluation of Guidelines

U.S. Public Opinion on the Governance of Artificial Intelligence

Closing the AI accountability gap

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