Machine learning, social learning and the governance of self-driving cars
By Jack Stilgoe. Social Studies of Science, 2018
326 citations recorded by the source corpus.
Subfields: Information Systems, Safety Research
View Paper: https://www.semanticscholar.org/paper/95615c6bce2123f12e39c3d9eb293ebb759501aa
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Information Systems
- Mark Ryan
- Marina Jirotka
- Madeleine Clare Elish
- Giulio Mecacci
- Alan Frank Thomas Winfield
- Filippo Santoni de Sio
In Safety Research
- Giulio Mecacci
- Filippo Santoni de Sio
- Mark Ryan
- Marina Jirotka
- Madeleine Clare Elish
- Alan Frank Thomas Winfield
- Paul Rodway
- Astrid Schepman
Citations in the corpus
- Four Responsibility Gaps with Artificial Intelligence: Why they Matter and How to Address them (2021, background): “…even when this is not the intention of their authors, these proposals even run the risk of fuelling the temptation of “solutionism” (Morozov, 2013); (Stilgoe, 2017), the belief that complex socio-technical and political problems can be “solved” (or avoided) by the introduction of new techniques.”
- In AI We Trust: Ethics, Artificial Intelligence, and Reliability (2020, background): “So far, this has been a fairly effective procedure, despite some controversial incidents such as the Tesla Model S fatality in 2016 (Stilgoe 2018).”
- The ethics of algorithms: key problems and solutions (2020, unknown): “…models and datasets; a lack of appropriate tools to visualise and track large volumes of code and data; code and data that are so poorly structured that they are impossible to read; and ongoing updates and human influence over a model (Diakopoulos and Koliska 2017; Stilgoe 2018; Zerilli et al.”
- Ethical governance is essential to building trust in robotics and artificial intelligence systems (2018, background): “Given that real-world trials of driverless car autopilots have already resulted in several fatal accidents [46,47], there is clearly an urgent need for transparency in order to discover how and why those accidents occurred, remedy any technical or operational faults, and establish accountability.”
- Moral Crumple Zones: Cautionary Tales in Human-Robot Interaction (2019, background): “It is clear to see the parallels to the paradigm of “human in the loop” supervised automation that has developed in aviation.8 8 See Stilgoe (2017) for a related analysis of the Tesla 2016 crash in which a driver was killed while his car was in Autopilot mode.”