The Role and Limits of Principles in AI Ethics

Whittlestone, Jess; Nyrup, Rune; Alexandrova, Anna; Cave, Stephen

doi:10.1145/3306618.3314289

Cited by 213 publications

(132 citation statements)

References 14 publications

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“…However, broad principles fall short of dictating specific actions in practice. Indeed, it has been acknowledged that these ethical frameworks do not provide enough contextual guidance for engineers to make use of them (e.g., [10], [14], and [22]). For example, the principle of fairness could lead to affirmative action, providing extra support for a group, or not, depending on the context.…”

Section: Ethics Imperative In Healthmentioning

confidence: 99%

Responsible AI—Two Frameworks for Ethical Design Practice

Peters

Vold

Robinson

et al. 2020

IEEE Trans. Technol. Soc.

169

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In 2019, the IEEE launched the P7000 standards projects intended to address ethical issues in the design of autonomous and intelligent systems. This move came amidst a growing public concern over the unintended consequences of artificial intelligence (AI), compounded by the lack of an anticipatory process for attending to ethical impact within professional practice. However, the difficulty in moving from principles to practice presents a significant challenge to the implementation of ethical guidelines. Herein, we describe two complementary frameworks for integrating ethical analysis into engineering practice to help address this challenge. We then provide the outcomes of an ethical analysis informed by these frameworks, conducted within the specific context of Internet-delivered therapy in digital mental health. We hope both the frameworks and analysis can provide tools and insights, not only for the context of digital healthcare but also for data-enabled and intelligent technology development more broadly.

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Section: Ethics Imperative In Healthmentioning

confidence: 99%

Responsible AI—Two Frameworks for Ethical Design Practice

Peters

Vold

Robinson

et al. 2020

IEEE Trans. Technol. Soc.

169

View full text Add to dashboard Cite

show abstract

“…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). For instance, how does one improve the accuracy of algorithmic predictions while ensuring fair and equal treatment of those impacted by the algorithm?…”

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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“…Moreover, different stakeholders may comprise of decision makers from different disciplines and require the research protocol to be communicated at differing levels of abstraction to ensure adequate comprehension. This issue of communication between disciplines and the potential for misinterpretation is highlighted in a recent Nuffield Foundation report 46…”

Section: Discussionmentioning

confidence: 99%

Liaison psychiatry—measurement and evaluation of service types, referral patterns and outcomes (LP-MAESTRO): a protocol

et al. 2019

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IntroductionWe describe the protocol for a project that will use linkage of routinely collected NHS data to answer a question about the nature and effectiveness of liaison psychiatry services in acute hospitals in England.Methods and analysisThe project will use three data sources: (1) Hospital Episode Statistics (HES), a database controlled by NHS Digital that contains patient data relating to emergency department (ED), inpatient and outpatient episodes at hospitals in England; (2) ResearchOne, a research database controlled by The Phoenix Partnership (TPP) that contains patient data relating to primary care provided by organisations using the SystmOne clinical information system and (3) clinical databases controlled by mental health trusts that contain patient data relating to care provided by liaison psychiatry services. We will link patient data from these sources to construct care pathways for patients who have been admitted to a particular hospital and determine those patients who have been seen by a liaison psychiatry service during their admission.Patient care pathways will form the basis of a matched cohort design to test the effectiveness of liaison intervention. We will combine healthcare utilisation within care pathways using cost figures from national databases. We will compare the cost of each care pathway and the impact of a broad set of health-related outcomes to obtain preliminary estimates of cost-effectiveness for liaison psychiatry services. We will carry out an exploratory incremental cost-effectiveness analysis from a whole system perspective.Ethics and disseminationIndividual patient consent will not be feasible for this study. Favourable ethical opinion has been obtained from the NHS Research Ethics Committee (North of Scotland) (REF: 16/NS/0025) for Work Stream 2 (phase 1) of the Liaison psychiatry—measurement and evaluation of service types, referral patterns and outcomes study. The Confidentiality Advisory Group at the Health Research Authority determined that Section 251 approval under Regulation 5 of the Health Service (Control of Patient Information) Regulations 2002 was not required for the study ‘on the basis that there is no disclosure of patient identifiable data without consent’ (REF: 16/CAG/0037).Results of the study will be published in academic journals in health services research and mental health. Details of the study methodology will also be published in an academic journal. Discussion papers will be authored for health service commissioners.

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The Role and Limits of Principles in AI Ethics

Cited by 213 publications

References 14 publications

Responsible AI—Two Frameworks for Ethical Design Practice

Responsible AI—Two Frameworks for Ethical Design Practice

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

Liaison psychiatry—measurement and evaluation of service types, referral patterns and outcomes (LP-MAESTRO): a protocol

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