Regulating Autonomous Systems: Beyond Standards

By David Danks, Alex John London. 2017

52 citations recorded by the source corpus.

Subfields: Medical Laboratory Technology, Safety, Risk, Reliability and Quality, Surgery

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

A Survey of Methods for Explaining Black Box Models (2018, background): “This impacts not only information on ethics but also on accountability [59], on safety [23], and on industrial liability [53].”
Meaningful Explanations of Black Box AI Decision Systems (2019, background): “This limitation impacts not only information ethics, but also accountability, safety and industrial liability (Danks and London 2017; Kingston 2016; Kroll et al. 2017).”
Open the Black Box Data-Driven Explanation of Black Box Decision Systems (2018, background): “This impacts not only information ethics, but also accountability, safety and industrial liability [9, 24, 27].”
Governing autonomous vehicles: emerging responses for safety, liability, privacy, cybersecurity, and industry risks (2018, background): “Autonomous systems are characterised as systems capable of making decisions independently of human interference (Brodsky, 2016; Collingwood, 2017), but, unlike mere automation, they can make these decisions while facing uncertainty (Danks & London, 2017).”
Auditing large language models: a three-layered approach (2023, background): “Further, not all ethical tensions stem from technology design alone, as some are intrinsic to specific tasks or applications [148].”
Generative causal explanations of black-box classifiers (2020, background): “Explanation methods have the potential to play a major role in enabling the safe and fair deployment of machine learning systems [2, 76], and explainability is a oft-mentioned constraint in their legal and ethical analysis.”