How to Build Ethics into Artificial Intelligence (2020-2021)

This project team examined ways to build morality into artificial intelligence by incorporating new morally relevant features based on data gathered by the previous years’ project teams. The project employed crowdsourcing of online moral judgments about which features of an action are and should be seen as relevant to the moral status of the action.

The project’s pilot study focuses on kidney exchanges, using experiments and online platforms to ask people what types of factors they believe kidney exchange algorithms should and should not take into account in determining who gets a kidney. Team members constructed scenarios where these factors vary in systematic ways and used machine learning to construct an algorithm that predicts human moral judgments, tests its extension to a novel set of scenarios and reveals how various moral factors interact.

To examine whether people would be more willing to accept computers’ decisions if they have been exposed to computers making decisions in similar contexts before, team members also asked participants whether they think humans or computers should be making decisions in a wide range of scenarios and applications.

Finally, team members also asked participants to play the classic “trust game” and report how morally wrong or acceptable they thought their actions and their partner’s actions were.

Timing

Fall 2020 – Spring 2021

Team Outputs

Adrien Meynard, Gayan Seneviratna, Elliot Doyle, Joyanne Becker, Hau-Tieng Wu, Jana Schaich Borg. 2021. Predicting Trust Using Automated Assessment of Multivariate Interactional Synchrony. IEEE.

Lok Chan, Jana Schaich Borg, Vincent Conitzer, Dominic Wilkinson, Julian Savulescu, Hazem Zohny, Walter Sinnott-Armstrong. 2022. Which features of patients are morally relevant in ventilator triage? A survey of the UK public. BMC Medical Ethics.

Team website

Poster presentation