open access publication

Article, 2024

Should it stay, or swerve? Trading off lives in dilemma situations involving autonomous cars

Health Economics, ISSN 1057-9230, Volume 33, 5, Pages 929-951, 10.1002/hec.4802

Contributors

Habla W. 0000-0003-1164-0962 (Corresponding author) [1] Kataria M. [2] Martinsson P. 0000-0002-1146-9248 [2] [3] Roeder K. [4]

Affiliations

  1. [1] Baden-Wuerttemberg Cooperative State University
  2. [NORA names: Germany; Europe, EU; OECD];
  3. [2] University of Gothenburg
  4. [NORA names: Sweden; Europe, EU; Nordic; OECD];
  5. [3] Technical University of Denmark
  6. [NORA names: DTU Technical University of Denmark; University; Denmark; Europe, EU; Nordic; OECD];
  7. [4] University of Augsburg
  8. [NORA names: Germany; Europe, EU; OECD]

Abstract

Using a representative survey with 1317 individuals and 12,815 moral decisions, we elicit Swedish citizens' preferences on how algorithms for self-driving cars should be programmed in cases of unavoidable harm to humans. Participants' choices in different dilemma situations (treatments) show that, at the margin, the average respondent values the lives of passengers and pedestrians equally when both groups are homogeneous and no group is to blame for the dilemma. In comparison, the respondent values the lives of passengers more when the pedestrians violate a social norm, and less when the pedestrians are children. Furthermore, we explain why the average respondent in the control treatment needs to be compensated with two to six passengers spared in order to sacrifice the first pedestrian, even though she values the lives of passengers and pedestrians equally at the margin. We conclude that respondents' choices are highly contextual and consider the age of the persons involved and whether these persons have complied with social norms.

Keywords

choice experiments, ethical preferences, random utility model, relative values of life, robot cars, self-driving cars

Funders

  • Augsburg University
  • Göteborgs Universitet

Data Provider: Elsevier