Beware the Lure of Narratives
“Hungry Judges” Should Not Motivate the Use of “Artificial Intelligence” in Law
Bibliographic Data
| ID | 21971615 |
|---|---|
| Authors | Konstantin Chatziathanasiou (0000-0002-3609-2096, University of Münster, corresponding author) |
| Year | 2022 |
| Volume | 23 |
| Issue | 4 |
| Pages | 452-464 |
| Publication date | 2022-05-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | German Law Journal (JOURNAL) |
| Journal identifiers | ISSN: 2071-8322 • E-ISSN: 2071-8322 |
| Publisher | Cambridge University Press (CUP) (PUBLISHER) |
| DOI | 10.1017/glj.2022.32 |
| OpenAlex | W4281554561 |
| Language | EN |
| Citations received | 5 |
| References cited | 3 |
The “hungry judge” effect, as presented by a famous study, is a common point of reference to underline human bias in judicial decision-making. This is particularly pronounced in the literature on “artificial intelligence” (AI) in law. Here, the effect is invoked to counter concerns about bias in automated decision-aids and to motivate their use. However, the validity of the “hungry judge” effect is doubtful. In our context, this is problematic for, at least, two reasons. First, shaky evidence leads to a misconstruction of the problem that may warrant an AI intervention. Second, painting the justice system worse than it actually is becomes a dangerous argumentative strategy, as it undermines institutional trust. Against this background, this article revisits the original “hungry judge” study and argues that it cannot be relied on as an argument in the AI discourse or beyond. The case of “hungry judges” demonstrates the lure of narratives, the dangers of “problem gerrymandering,” and, ultimately, the need for a careful reception of social science
Argumentative · Economic Justice · Economics · Law and economics · Narrative · Political science · Sociology · Warrant · Artificial Intelligence in Law · Ethics and Social Impacts of AI · History · Law · Law in Society and Culture · Philosophy
| Unique citing works | 5 |
|---|---|
| Citations per year | 2.5 |
| Citation span | 2024 - 2025 (2) |
| Citation velocity | recent |
| Highly cited | No |
| Citation types | Neutral: 5 |