Ethical principles in machine learning and artificial intelligence: Cases from the field and possible ways forward
Samuele Lo Piano · Zenodo (CERN European Organization for Nuclear Research) · 2020
Decision-making on numerous aspects of our daily lives is being outsourced to machine-learning algorithms and \ac{ai} \parencite{oneil_weapons_2016}, motivated by speed and efficiency in the decision process. \\ \ac{ml} approaches - one of the typologies of algorithms underpinning artificial intelligence - are typically developed as black boxes \parencite{lewis_ai_2017}. The implication is that \ac{ml} code scripts are rarely scrutinised; interpretability is usually sacrificed in favour of usability and effectiveness \parencite{lipton_mythos_2016}. Room for improvement in practices associated with programme development have also been flagged along other dimensions, including \textit{inter alia} fairness, accuracy, accountability, and transparency. \\ In our contribution, we will discuss the production of guidelines and dedicated documents around these themes. We will outline the applications of \textit{AI-driven} decision making to: a) Risk assessment in the criminal justice system, and b) autonomous vehicles, highlighting points of friction across ethical principles. \\ We will finally examine possible ways forward towards the implementation of governance on \ac{ai}.