Michael Veale
Biographic Data
| ID | 2744621 |
|---|---|
| NAME | Michael Veale |
| GIVEN NAMES | Michael |
| FAMILY NAME | Veale |
| SIGNATURE | VEALE M |
| AFFILIATIONS | University College London |
| ORCID | 0000-0002-2342-8785 |
| VERIFIED | Yes |
| TOTAL WORKS | 7 |
| TOTAL CITATIONS | 62 |
| WORKS AS AUTHOR | 7 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2017 |
| LATEST PUBLICATION YEAR | 2023 |
| H-INDEX | 4 |
AI and Global Governance
Artificial intelligence (AI) is a salient but polarizing issue of recent times. Actors around the world are engaged in building a governance regime around it. What exactly the “it” is that is being governed, how, by who, and why-these are all less clear. In this review, we attempt to shine some light on those questions, considering literature on AI, the governance of computing, and regulation and governance more broadly. We take critical stock of…
Adtech and Real-Time Bidding under European Data Protection Law
This article discusses the troubled relationship between contemporary advertising technology (adtech) systems, in particular systems of real-time bidding (RTB, also known as programmatic advertising) underpinning much behavioral targeting on the web and through mobile applications. This article analyzes the extent to which practices of RTB are compatible with the requirements regarding a legal basis for processing, transparency, and security in E…
Certification systems for machine learning
Concerns around machine learning’s societal impacts have led to proposals to certify some systems. While prominent governance efforts to date center around networking standards bodies such as the Institute of Electrical and Electronics Engineers (IEEE), we argue that machine learning certification should build on structures from the sustainability domain. Policy challenges of machine learning and sustainability share significant structural simila…
Demystifying the Draft EU Artificial Intelligence Act — Analysing the good, the bad, and the unclear elements of the proposed approach
Article Demystifying the Draft EU Artificial Intelligence Act — Analysing the good, the bad, and the unclear elements of the proposed approach was published on August 1, 2021 in the journal Computer Law Review International (volume 22, issue 4).
Cybersecurity
Cybersecurity covers the broad range of technical and social issues that must be considered to protect networked information systems. The importance of the concept has increased as so many government, business, and day-to-day activities globally have moved online. It has been increasingly referred to in both academic and mainstream publications since 2003, in fields including software engineering, international relations, crisis management and pu…
Slave to the Algorithm? Why a 'right to an explanation' is probably not the remedy you are looking for
Cite as Lilian Edwards and Michael Veale, 'Slave to the Algorithm? Why a 'right to an explanation' is probably not the remedy you are looking for' (2017) 16 Duke Law and Technology Review 18-84. (First posted on SSRN 24 May 2017)Algorithms, particularly machine learning (ML) algorithms, are increasingly important to individuals' lives, but have caused a range of concerns revolving mainly around unfairness, discrimination and opacity. Transparency…
Fairer machine learning in the real world
Decisions based on algorithmic, machine learning models can be unfair, reproducing biases in historical data used to train them. While computational techniques are emerging to address aspects of these concerns through communities such as discrimination-aware data mining (DADM) and fairness, accountability and transparency machine learning (FATML), their practical implementation faces real-world challenges. For legal, institutional or commercial r…
Slave to the Algorithm? Why a 'right to an explanation' is probably not the remedy you are looking for
Cite as Lilian Edwards and Michael Veale, 'Slave to the Algorithm? Why a 'right to an explanation' is probably not the remedy you are looking for' (2017) 16 Duke Law and Technology Review 18-84. (First posted on SSRN 24 May 2017)Algorithms, particularly machine learning (ML) algorithms, are increasingly important to individuals' lives, but have caused a range of concerns revolving mainly around unfairness, discrimination and opacity. Transparency…
AI and Global Governance
Artificial intelligence (AI) is a salient but polarizing issue of recent times. Actors around the world are engaged in building a governance regime around it. What exactly the “it” is that is being governed, how, by who, and why-these are all less clear. In this review, we attempt to shine some light on those questions, considering literature on AI, the governance of computing, and regulation and governance more broadly. We take critical stock of…
Certification systems for machine learning
Concerns around machine learning’s societal impacts have led to proposals to certify some systems. While prominent governance efforts to date center around networking standards bodies such as the Institute of Electrical and Electronics Engineers (IEEE), we argue that machine learning certification should build on structures from the sustainability domain. Policy challenges of machine learning and sustainability share significant structural simila…
Fairer machine learning in the real world
Decisions based on algorithmic, machine learning models can be unfair, reproducing biases in historical data used to train them. While computational techniques are emerging to address aspects of these concerns through communities such as discrimination-aware data mining (DADM) and fairness, accountability and transparency machine learning (FATML), their practical implementation faces real-world challenges. For legal, institutional or commercial r…
Slave to the Algorithm? Why a 'right to an explanation' is probably not the remedy you are looking for
Cite as Lilian Edwards and Michael Veale, 'Slave to the Algorithm? Why a 'right to an explanation' is probably not the remedy you are looking for' (2017) 16 Duke Law and Technology Review 18-84. (First posted on SSRN 24 May 2017)Algorithms, particularly machine learning (ML) algorithms, are increasingly important to individuals' lives, but have caused a range of concerns revolving mainly around unfairness, discrimination and opacity. Transparency…
Fairer machine learning in the real world
Decisions based on algorithmic, machine learning models can be unfair, reproducing biases in historical data used to train them. While computational techniques are emerging to address aspects of these concerns through communities such as discrimination-aware data mining (DADM) and fairness, accountability and transparency machine learning (FATML), their practical implementation faces real-world challenges. For legal, institutional or commercial r…
Cybersecurity
Cybersecurity covers the broad range of technical and social issues that must be considered to protect networked information systems. The importance of the concept has increased as so many government, business, and day-to-day activities globally have moved online. It has been increasingly referred to in both academic and mainstream publications since 2003, in fields including software engineering, international relations, crisis management and pu…
Demystifying the Draft EU Artificial Intelligence Act — Analysing the good, the bad, and the unclear elements of the proposed approach
Article Demystifying the Draft EU Artificial Intelligence Act — Analysing the good, the bad, and the unclear elements of the proposed approach was published on August 1, 2021 in the journal Computer Law Review International (volume 22, issue 4).
Adtech and Real-Time Bidding under European Data Protection Law
This article discusses the troubled relationship between contemporary advertising technology (adtech) systems, in particular systems of real-time bidding (RTB, also known as programmatic advertising) underpinning much behavioral targeting on the web and through mobile applications. This article analyzes the extent to which practices of RTB are compatible with the requirements regarding a legal basis for processing, transparency, and security in E…
Certification systems for machine learning
Concerns around machine learning’s societal impacts have led to proposals to certify some systems. While prominent governance efforts to date center around networking standards bodies such as the Institute of Electrical and Electronics Engineers (IEEE), we argue that machine learning certification should build on structures from the sustainability domain. Policy challenges of machine learning and sustainability share significant structural simila…
AI and Global Governance
Artificial intelligence (AI) is a salient but polarizing issue of recent times. Actors around the world are engaged in building a governance regime around it. What exactly the “it” is that is being governed, how, by who, and why-these are all less clear. In this review, we attempt to shine some light on those questions, considering literature on AI, the governance of computing, and regulation and governance more broadly. We take critical stock of…
Business (5 works) · Computer Science (5 works) · Political science (5 works) · Ethics and Social Impacts of AI (4 works) · Law (4 works) · Computer security (3 works) · Sociology (3 works) · Corporate governance (2 works) · Cybersecurity and Cyber Warfare Studies (2 works) · Economics (2 works)