Human-machine coordination in mixed traffic as a problem of Meaningful Human Control
Source
AI & Society, 38, 3, (2023), pp. 1151-1166ISSN
Publication type
Article / Letter to editor
Display more detailsDisplay less details
Organization
SW OZ DCC AI
Journal title
AI & Society
Volume
vol. 38
Issue
iss. 3
Languages used
English (eng)
Page start
p. 1151
Page end
p. 1166
Subject
Cognitive artificial intelligenceAbstract
The urban traffic environment is characterized by the presence of a highly differentiated pool of users, including vulnerable ones. This makes vehicle automation particularly difficult to implement, as a safe coordination among those users is hard to achieve in such an open scenario. Different strategies have been proposed to address these coordination issues, but all of them have been found to be costly for they negatively affect a range of human values (e.g. safety, democracy, accountability...). In this paper, we claim that the negative value impacts entailed by each of these strategies can be interpreted as lack of what we call Meaningful Human Control over different parts of a sociotechnical system. We argue that Meaningful Human Control theory provides the conceptual tools to reduce those unwanted consequences, and show how “designing for meaningful human control” constitutes a valid strategy to address coordination issues. Furthermore, we showcase a possible application of this framework in a highly dynamic urban scenario, aiming to safeguard important values such as safety, democracy, individual autonomy, and accountability. Our meaningful human control framework offers a perspective on coordination issues that allows to keep human actors in control while minimizing the active, operational role of the drivers. This approach makes ultimately possible to promote a safe and responsible transition to full automation.
This item appears in the following Collection(s)
- Academic publications [246515]
- Electronic publications [134102]
- Faculty of Social Sciences [30494]
- Open Access publications [107633]
Upload full text
Use your RU credentials (u/z-number and password) to log in with SURFconext to upload a file for processing by the repository team.