Lilian Edwards

TL;DR: It is argued that a right to an explanation in the GDPR is unlikely to be a complete remedy to algorithmic harms, particularly in some of the core "algorithmic war stories" that have shaped recent attitudes in this domain and it is feared that the search for a "right to an explanations" in theGDPR may be at best distracting, and at worst nurture a new kind of "transparency fallacy".

TL;DR: A set of five ethical principles, together with seven high-level messages, as a basis for responsible robotics.

TL;DR: Recent work from the information security literature around ‘model inversion’ and ‘membership inference’ attacks is presented, which indicates that the process of turning training data into machine-learned systems is not one way, and how this could lead some models to be legally classified as personal data.

TL;DR: In this paper, the authors present recent work from the information security literature around ''model inversion'' and ''membership inference'' attacks, which indicate that the process of turning training data into machine learned systems is not one-way, and demonstrate how this could lead some models to be legally classified as personal data.

TL;DR: A perspective outside the US is introduced by critically examining how three prominent automated hiring systems in regular use in the UK, HireVue, Pymetrics and Applied, understand and attempt to mitigate bias and discrimination, before situating these in the socio-legal context of the UK.