Philosophy Commons^™

Autonomous Vehicles And The Ethical Tension Between Occupant And Non-Occupant Safety, Jason Borenstein, Joseph Herkert, Keith Miller

The Journal of Sociotechnical Critique

Given that the creation and deployment of autonomous vehicles is likely to continue, it is important to explore the ethical responsibilities of designers, manufacturers, operators, and regulators of the technology. We specifically focus on the ethical responsibilities surrounding autonomous vehicles that these stakeholders have to protect the safety of non-occupants, meaning individuals who are around the vehicles while they are operating. The term “non-occupants” includes, but is not limited to, pedestrians and cyclists. We are particularly interested in how to assign moral responsibility for the safety of non-occupants when autonomous vehicles are deployed in a complex, land-based transportation system.

Homo Ludens Moralis: Designing And Developing A Board Game To Teach Ethics For Ict Education, Damian Gordon, Dympna O'Sullivan, Ioannis Stavrakakis, Andrea Curley

Conference papers

The ICT ethical landscape is changing at an astonishing rate, as technologies become more complex, and people choose to interact with them in new and distinct ways, the resultant interactions are more novel and less easy to categorise using traditional ethical frameworks. It is vitally important that the developers of these technologies do not live in an ethical vacuum; that they think about the uses and abuses of their creations, and take some measures to prevent others being harmed by their work.

To equip these developers to rise to this challenge and to create a positive future for the use …

Implementation Considerations For Mitigating Bias In Supervised Machine Learning, Bardia Bijani Aval

CSB and SJU Distinguished Thesis

Machine Learning (ML) is an important component of computer science and a mainstream way of making sense of large amounts of data. Although the technology is establishing new possibilities in different fields, there are also problems to consider, one of which is bias. Due to the inductive reasoning of ML algorithms in creating mathematical models, the predictions and trends found by the models will never necessarily be true – just more or less probable. Knowing this, it is unreasonable for us to expect the applied deductive reasoning of these models to ever be fully unbiased. Therefore, it is important that …