Governments around the world are increasingly turning to algorithms to support or fulfil administrative tasks – from automating social benefit regulation to the automatic determination of the merit of court cases. With this global trend came along an increasing number of scandals that often resulted from a mismatch between implementation of an algorithmic process by the government and public expectation. These (violated) expectations concern several factors of the deployment, which are currently understudied. This article analyses how six of these factors – institution, aim, legal field, data provenance, trigger, and level of automation – influence social acceptance of automating administrative and legal processes in France. From our factorial vignette study with 6 variables across 540 vignettes and N= 7,560 assessments, we draw three main conclusions. First, we find that the institution, aim, data provenance, trigger and level of automation are statistically significant in driving the acceptance of automatically processable regulations. Second, we find that acceptance across all conditions is only slightly positive on average, when expressed on a scale from −5 to +5. Overall, our study confirms that the social acceptability of the implementation of algorithms in administrative and legal processes is highly dependent on specific implementation and contextual details; public discourse on the implementation of such algorithms hence requires information about specifics of their deployment.
Guitton et al. (Sun,) studied this question.
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