The Interrelation Between Data and AI Ethics in the Context of Impact Assessments

Kazim, Emre; Koshiyama, Adriano

doi:10.2139/ssrn.3627561

Cited by 3 publications

(3 citation statements)

References 13 publications

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“…It is likely that the relationship between data protection and AI ethics will emerge as a contentious issue within the literature. This is because privacy and fairness can be thought of as in direct conflict with one another [39] and therefore demand an integration of data protection and AI impact assessments through a framework of priorities, not by conforming AI to data protection provisions but by implementing a negotiated approach that recognizes the fundamental tension between these two values.…”

Section: Recommendations and Future Researchmentioning

confidence: 99%

AI auditing and impact assessment: according to the UK information commissioner’s office

Kazim

Denny²,

Koshiyama

2021

AI Ethics

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As the use of data and artificial intelligence systems becomes crucial to core services and business, it increasingly demands a multi-stakeholder and complex governance approach. The Information Commissioner's Office’s ‘Guidance on the AI auditing framework: Draft guidance for consultation’ is a move forward in AI governance. The aim of this initiative is toward producing guidance that encompasses both technical (e.g. system impact assessments) and non-engineering (e.g. human oversight) components to governance and represents a significant milestone in the movement towards standardising AI governance. This paper will summarise and critically evaluate the ICO effort and try to anticipate future debates and present some general recommendations.

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Section: Recommendations and Future Researchmentioning

confidence: 99%

AI auditing and impact assessment: according to the UK information commissioner’s office

Kazim

Denny²,

Koshiyama

2021

AI Ethics

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“…Conversely, in the case of fairness the concern is that systems perform equally for all protected attributes and, as such, systems need to be as transparent as possible for fairness to be assured. The tension between privacy and fairness becomes apparent, where a greater degree of privacy is likely to come at the price of fairness concerns (Kazim and Koshiyama, 2020a).…”

Section: Explainability Vs Robustness (Accuracy)mentioning

confidence: 99%

Algorithm Auditing: Managing the Legal, Ethical, and Technological Risks of Artificial Intelligence, Machine Learning, and Associated Algorithms

2022

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Business reliance on algorithms are becoming ubiquitous, and companies are increasingly concerned about their algorithms causing major financial or reputational damage. High-profile cases include VW's Dieselgate scandal with fines worth of $34.69B, Knight Capital's bankruptcy (~$450M) by a glitch in its algorithmic trading system, and Amazon's AI recruiting tool being scrapped after showing bias against women. In response, governments are legislating and imposing bans, regulators fining companies, and the Judiciary discussing potentially making algorithms artificial "persons" in Law. Soon there will be 'billions' of algorithms making decisions with minimal human intervention; from autonomous vehicles and finance, to medical treatment, employment, and legal decisions. Indeed, scaling to problems beyond the human is a major point of using such algorithms in the first place. As with Financial Audit, governments, business and society will require Algorithm Audit; formal assurance that algorithms are legal, ethical and safe. A new industry is envisaged: Auditing and Assurance of Algorithms (cf. Data privacy), with the remit to professionalize and industrialize AI, ML and associated algorithms.The stakeholders range from those working on policy and regulation, to industry practitioners and developers. We also anticipate the nature and scope of the auditing levels and framework presented will inform those interested in systems of governance and compliance to regulation/standards. Our goal in this paper is to survey the key areas necessary to perform auditing and assurance, and instigate the debate in this novel area of research and practice.

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“…Auditing and impact assessments involve the creation of metrics for tracing and tracking decisions, making the technologies accessible for verification and accountability. 68 The most well-established form of this is Data Protection Impact Assessments (DPIA) 69 , 70 To date more general “data ethics” canvases and process frameworks have emerged, 71 and impact assessments specific to AI are being called for and developed. 54 , 72…”

Section: Three Approaches: Principles Processes and Ethical Consciousnessmentioning

confidence: 99%

A high-level overview of AI ethics

Kazim

Koshiyama

2021

Patterns

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Summary Artificial intelligence (AI) ethics is a field that has emerged as a response to the growing concern regarding the impact of AI. It can be read as a nascent field and as a subset of the wider field of digital ethics, which addresses concerns raised by the development and deployment of new digital technologies, such as AI, big data analytics, and blockchain technologies. The principle aim of this article is to provide a high-level conceptual discussion of the field by way of introducing basic concepts and sketching approaches and central themes in AI ethics. The first part introduces concepts by noting what is being referred to by “AI” and “ethics”, etc.; the second part explores some predecessors to AI ethics, namely engineering ethics, philosophy of technology, and science and technology studies; the third part discusses three current approaches to AI ethics namely, principles, processes, and ethical consciousness; and finally, the fourth part discusses central themes in translating ethics in to engineering practice. We conclude by summarizing and noting the inherent interdisciplinary future directions and debates in AI ethics.

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The Interrelation Between Data and AI Ethics in the Context of Impact Assessments

Cited by 3 publications

References 13 publications

AI auditing and impact assessment: according to the UK information commissioner’s office

AI auditing and impact assessment: according to the UK information commissioner’s office

Algorithm Auditing: Managing the Legal, Ethical, and Technological Risks of Artificial Intelligence, Machine Learning, and Associated Algorithms

A high-level overview of AI ethics

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