Search Results for author: Sandra Wachter

Found 5 papers, 2 papers with code

The Unfairness of Fair Machine Learning: Levelling down and strict egalitarianism by default

no code implementations5 Feb 2023 Brent Mittelstadt, Sandra Wachter, Chris Russell

Many current fairness measures suffer from both fairness and performance degradation, or "levelling down," where fairness is achieved by making every group worse off, or by bringing better performing groups down to the level of the worst off.

Fairness Jurisprudence

The Theory of Artificial Immutability: Protecting Algorithmic Groups Under Anti-Discrimination Law

no code implementations2 May 2022 Sandra Wachter

Artificial Intelligence (AI) is increasingly used to make important decisions about people.


Why Fairness Cannot Be Automated: Bridging the Gap Between EU Non-Discrimination Law and AI

1 code implementation12 May 2020 Sandra Wachter, Brent Mittelstadt, Chris Russell

Through this proposal for procedural regularity in the identification and assessment of automated discrimination, we clarify how to build considerations of fairness into automated systems as far as possible while still respecting and enabling the contextual approach to judicial interpretation practiced under EU non-discrimination law.


Explaining Explanations in AI

no code implementations4 Nov 2018 Brent Mittelstadt, Chris Russell, Sandra Wachter

Recent work on interpretability in machine learning and AI has focused on the building of simplified models that approximate the true criteria used to make decisions.

BIG-bench Machine Learning Philosophy +1

Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR

5 code implementations1 Nov 2017 Sandra Wachter, Brent Mittelstadt, Chris Russell

We suggest data controllers should offer a particular type of explanation, unconditional counterfactual explanations, to support these three aims.

counterfactual Decision Making

Cannot find the paper you are looking for? You can Submit a new open access paper.