• Publications
  • Influence
Counterfactual Explanations Without Opening the Black Box: Automated Decisions and the GDPR
TLDR
It is suggested data controllers should offer a particular type of explanation, unconditional counterfactual explanations, to support these three aims, which describe the smallest change to the world that can be made to obtain a desirable outcome, or to arrive at the closest possible world, without needing to explain the internal logic of the system.
The ethics of algorithms: Mapping the debate
TLDR
This paper makes three contributions to clarify the ethical importance of algorithmic mediation, including a prescriptive map to organise the debate, and assesses the available literature in order to identify areas requiring further work to develop the ethics of algorithms.
Principles alone cannot guarantee ethical AI
TLDR
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.
Explaining Explanations in AI
TLDR
This work contrasts the different schools of thought on what makes an explanation in philosophy and sociology, and suggests that machine learning might benefit from viewing the problem more broadly.
Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation
TLDR
The problems show that the GDPR lacks precise language as well as explicit and well-defined rights and safeguards against automated decision-making, and therefore runs the risk of being toothless.
The Ethics of Big Data: Current and Foreseeable Issues in Biomedical Contexts
TLDR
This article systematically and comprehensively analyses academic literature concerning the ethical implications of Big Data, providing a watershed for future ethical investigations and regulations and identifies eleven themes that provide a thorough critical framework to guide ethical assessment and governance of emerging Big Data practices.
A Right to Reasonable Inferences: Re-Thinking Data Protection Law in the Age of Big Data and AI
TLDR
It is argued that a new data protection right, the ‘right to reasonable inferences’, is needed to help close the accountability gap currently posed ‘high risk inferences' , meaning inferences that are privacy invasive or reputation damaging and have low verifiability in the sense of being predictive or opinion-based.
Ethics of the health-related internet of things: a narrative review
  • B. Mittelstadt
  • Medicine, Computer Science
    Ethics and Information Technology
  • 4 July 2017
TLDR
This paper maps the main ethical problems that have been identified by the relevant literature and identifies key themes in the on-going debate on ethical problems concerning H-IoT.
Artificial Intelligence and the ‘Good Society’: the US, EU, and UK approach
TLDR
It is concluded that the reports address adequately various ethical, social, and economic topics, but come short of providing an overarching political vision and long-term strategy for the development of a ‘good AI society’.
Why Fairness Cannot Be Automated: Bridging the Gap Between EU Non-Discrimination Law and AI
TLDR
It is shown that automating fairness or non-discrimination in Europe may be impossible because the law does not provide a static or homogenous framework suited to testing for discrimination in AI systems, and a standard set of statistical evidence for automated discrimination cases is needed.
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