Perceptions of Justice: Assessing the Perceived Effectiveness of Punishments by Artificial Intelligence versus Human Judges
-
Gilles Grolleau
Abstract
Using an original experimental survey, we analyze how people perceive punishments generated by artificial intelligence (AI) compared to the same punishments generated by a human judge. We use two vignettes pertaining to two different albeit relatively common illegal behaviors, namely not picking up one’s dog waste on public roads and setting fire in dry areas. In general, participants perceived AI judgements as having a larger deterrence effect compared to the those rendered by a judge. However, when we analyzed each scenario separately, we found that the differential effect of AI is only significant in the first scenario. We discuss the implications of these findings.
Appendix A. Survey Translation
A.1 Anonymous Survey
In the following, we present a hypothetical scenario. We invite you to read it carefully and answer the questions. There are no true or false answers: we are only interested in your honest opinion.
Treatment 1: For health reasons, dog waste is prohibited on sidewalks and public roads. For refusing to pick up his/her dog’s waste, the judicial institution, through a judge who analyzed the offense committed, sanctioned the guilty person to carry out 2 h of community service (cleaning of public spaces damaged by dog waste and other dirt).
Treatment 2: For health reasons, dog waste is prohibited on sidewalks and public roads. For refusing to pick up his/her dog’s waste, the judicial institution, through an artificial intelligence (AI) program which analyzed the offense committed, sanctioned the guilty person to carry out 2 h of community service (cleaning of public spaces damaged by dog waste and other dirt).
Treatment 3: Last summer, a person was convicted by a court for setting fire to dry grass in the middle of summer, using a lighter. Because of this infraction, 10 ha of a century-old forest went up in smoke. The court, through a judge who analyzed this offense, sentenced this person to 1 year in prison.
Treatment 4: Last summer, a person was convicted by a court for setting fire to dry grass in the middle of summer, using a lighter. Because of this infraction, 10 ha of a century-old forest went up in smoke. The court, through artificial intelligence (AI) program which analyzed this offense, sentenced this person to 1 year in prison.
Please assess whether this sanction discourages individuals from leaving their pet’s waste on public roads (T3, T4: setting fire to dry grass in the middle of summer):
| 1 not discouraging at all | 2 | 3 | 4 | 5 | 6 | 7 very discouraging |
To what extent does this sanction seem (un)just and (un)fait to you?
| 1 completely unjust and unfair | 2 | 3 | 4 | 5 | 6 | 7 completely just and fair |
In my opinion, not picking up your pet’s waste on sidewalks and public spaces (T3, T4: deliberately setting fire to dry grass) is an act:
| 1 not serious at all | 2 | 3 | 4 | 5 | 6 | 7 very serious |
Generally speaking, people seek to justify themselves by finding excuses for their actions (e.g. other serious problems at the same time):
| 1 fully disagree | 2 | 3 | 4 | 5 | 6 | 7 fully agree |
Please complete the following information:
Appendix B. Mean Values for the Variables used in Estimations
| Variables | Scenario 1 (dog waste) | Scenario 2 (setting fire) | |||
|---|---|---|---|---|---|
| Treatment 1 | Treatment 2 | Treatment 3 | Treatment 4 | ||
| (judge; N = 49) | (AI; N = 44) | (judge; N = 65) | (AI; N = 51) | ||
| Deterrence | 3.837 | 5.068 | 4.369 | 3.941 | |
| Fair | 5.571 | 5.614 | 4.846 | 4.490 | |
| Seriousness | 5.082 | 4.909 | 5.846 | 5.961 | |
| Justification | 4.898 | 4.409 | 5.108 | 5.216 | |
| Age (continuous) | 29.428 | 25.909 | 30.461 | 43.412 | |
| Gender (=1 if female) | 0.571 | 0.682 | 0.415 | 0.725 | |
| Education | Cat. 1 (ref) | 0.041 | 0.045 | 0.061 | 0.059 |
| Cat. 2 | 0.571 | 0.364 | 0.338 | 0.196 | |
| Cat. 3 | 0.388 | 0.477 | 0.477 | 0.647 | |
| Income | Cat. 1 (ref) | 0.306 | 0.409 | 0.169 | 0.333 |
| Cat. 2 | 0.204 | 0.341 | 0.123 | 0.118 | |
| Cat. 3 | 0.449 | 0.136 | 0.385 | 0.216 | |
| Cat. 4 | 0.041 | 0.114 | 0.323 | 0.333 | |
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Education categories: baccalaureate or less, 1–3 years of university studies, and 4 years or more of university studies. Income categories: ≤€800/month, €801–€1,300/month, €1,301–€2,300/month, and ≥€2,301/month.
Appendix C. Linear Regression Estimates of the Effect of AI on Deterrence by Treatment
| Variables | Model 1 (without interaction) | Model 2 (with interaction) | |||
|---|---|---|---|---|---|
| Scenario 1 | (setting fire) | (dog waste) | (setting fire) | ||
| (dog waste) | (setting fire) | (dog waste) | (setting fire) | ||
| Treatment | Judge (ref) | ||||
| AI | 1.131*** (0.315) | −0.275 (0.394) | 2.332 (1.616) | −2.053 (1.710) | |
| Fair | 0.158 (0.104) | 0.377*** (0.089) | 0.144 (0.107) | 0.380*** (0.089) | |
| Seriousness | 0.023 (0.118) | 0.012 (0.128) | 0.102 (0.211) | −0.005 (0.173) | |
| Justification | 0.251*** (0.081) | −0.048 (0.094) | 0.327** (0.126) | −0.177 (0.133) | |
| Age (continuous) | −0.011 (0.015) | −0.001 (0.013) | −0.009 (0.016) | −0.003 (0.013) | |
| Gender (=1 if female) | 0.093 (0.305) | −0.175 (0.331) | −0.046 (0.312) | −0.100 (0.335) | |
| Education | Cat. 1 (ref) | ||||
| Cat. 2 | −0.358 (0.565) | 0.769 (0.480) | −0.312 (0.572) | 0.694 (0.495) | |
| Cat. 3 | −0.859 (0.536) | 0.650 (0.449) | −0.831 (0.541) | 0.608 (0.449) | |
| Income | Cat. 1 (ref) | ||||
| Cat. 2 | 0.371 (0.397) | −0.422 (0.572) | 0.405 (0.403) | −0.465 (0.573) | |
| Cat. 3 | −0.345 (0.414) | −0.340 (0.467) | −0.374 (0.420) | −0.402 (0.469) | |
| Cat. 4 | 0.184 (0.634) | 0.344 (0.573) | 0.315 (0.657) | 0.445 (0.577) | |
| Treatment##Seriousness | −0.110 (0.258) | 0.063 (0.246) | |||
| Treatment##Justification | −0.138 (0.177) | 0.267 (0.190) | |||
| Constant | 1.364 (1.283) | 3.177** (1.522) | 1.731 (1.654) | 3.141** (1.272) | |
| Observations | 93 | 116 | 93 | 116 | |
| F | 3.74*** | 2.61*** | 3.17*** | 2.38*** | |
| R 2 | 0.3368 | 0.2163 | 0.3432 | 0.2325 | |
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Education categories: baccalaureate or less, 1–3 years of university studies, and 4 years or more of university studies. Income categories: ≤€800/month, €801–€1,300/month, €1,301–€2,300/month, and ≥€2,301/month. *** and ** Refer to significance at the levels of 1 % and 5 % respectively. The values between brackets correspond to Standard Errors.
Appendix D. Testing the Effect of AI in Interaction with Age and Gender (Standard Errors Between Brackets)
| Variables | Interaction AI##Age | Interaction AI##Gender | |
|---|---|---|---|
| Treatment | Judge (ref) | ||
| AI | 1.236** (0.563) | 0.616* (0.363) | |
| Fair | 0.317*** (0.065) | 0.319*** (0.065) | |
| Seriousness | 0.004 (0.085) | −0.008 (0.085) | |
| Justification | 0.095 (0.063) | 0.083 (0.063) | |
| Age (continuous) | 0.000 (0.014) | −0.015* (0.009) | |
| Gender (=1 if female) | −0.088 (0.231) | −0.034 (0.298) | |
| Education | Cat. 1 (ref) | ||
| Cat. 2 | 0.218 (0.354) | 0.253 (0.355) | |
| Cat. 3 | −0.078 (0.337) | −0.048 (0.338) | |
| Income | Cat. 1 (ref) | ||
| Cat. 2 | 0.376 (0.330) | 0.390 (0.332) | |
| Cat. 3 | −0.089 (0.313) | −0.108 (0.315) | |
| Cat. 4 | 0.826** (0.393) | 0.823** (0.395) | |
| Treatment##Age | −0.024 (0.016) | ||
| Treatment##Gender | −0.237 (0.463) | ||
| Constant | 1.774** (0.838) | 2.330*** (0.749) | |
| Observations | 209 | 209 | |
| F | 3.70*** | 3.50*** | |
| R 2 | 0.1846 | 0.1765 | |
-
Education categories: baccalaureate or less, 1–3 years of university studies, and 4 years or more of university studies. Income categories: ≤€800/month, €801–€1,300/month, €1,301–€2,300/month, and ≥€2,301/month. ***, ** and *Refer to significance at the levels of 1 %, 5 % and 10 % respectively.
Appendix E. Testing the Interaction Between Age and Fairness (Dep. Variable: Deterrence)
| Variables | Coefficients and significance | |
|---|---|---|
| Treatment | Judge (ref) | |
| AI | 0.522** (0.236) | |
| Fair | 0.035 (0.161) | |
| Seriousness | −0.033 (0.085) | |
| Justification | 0.094 (0.062) | |
| Age (continuous) | −0.058** (0.023) | |
| Fair##Age | 0.008* (0.004) | |
| Gender (=1 if female) | −0.107 (0.229) | |
| Education | Cat. 1 (ref) | |
| Cat. 2 | 0.228 (0.351) | |
| Cat. 3 | −0.129 (0.337) | |
| Income | Cat. 1 (ref) | |
| Cat. 2 | 0.386 (0.329) | |
| Cat. 3 | −0.139 (0.312) | |
| Cat. 4 | 0.925** (0.395) | |
| Constant | 3.957*** (1.116) | |
| Observation | 209 | |
| F | 3.85*** | |
| R 2 | 0.1908 | |
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Education categories: baccalaureate or less, 1–3 years of university studies, and 4 years or more of university studies. Income categories: ≤€800/month, €801–€1,300/month, €1,301–€2,300/month, and ≥€2,301/month. ***, ** and *Refer to significance at the levels of 1 %, 5 % and 10 % respectively. The values between brackets correspond to Standard Errors.
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Articles in the same Issue
- Frontmatter
- Articles
- The Impact of Online Dispute Resolution on the Judicial Outcomes in India
- Legal Compliance and Detection Avoidance: Results on the Impact of Different Law-Enforcement Designs
- Women in Piracy. Experimental Perspectives on Copyright Infringement
- Is Investment in Prevention Correlated with Insurance Fraud? Theory and Experiment
- Bias, Trust, and Trustworthiness: An Experimental Study of Post Justice System Outcomes
- Do Sanctions or Moral Costs Prevent the Formation of Cartel Agreements?
- Efficiency and Distributional Fairness in a Bankruptcy Procedure: A Laboratory Experiment
- Soft Regulation for Financial Advisors
- Conciliation, Social Preferences, and Pre-Trial Settlement: A Laboratory Experiment
- The Impact of Tax Culture on Tax Rate Structure Preferences: Results from a Vignette Study with Migrants and Non-Migrants in Germany
- Perceptions of Justice: Assessing the Perceived Effectiveness of Punishments by Artificial Intelligence versus Human Judges
- Judged by Robots: Preferences and Perceived Fairness of Algorithmic versus Human Punishments
- The Hidden Costs of Whistleblower Protection
- The Missing Window of Opportunity and Quasi-Experimental Effects of Institutional Integration: Evidence from Ukraine
Articles in the same Issue
- Frontmatter
- Articles
- The Impact of Online Dispute Resolution on the Judicial Outcomes in India
- Legal Compliance and Detection Avoidance: Results on the Impact of Different Law-Enforcement Designs
- Women in Piracy. Experimental Perspectives on Copyright Infringement
- Is Investment in Prevention Correlated with Insurance Fraud? Theory and Experiment
- Bias, Trust, and Trustworthiness: An Experimental Study of Post Justice System Outcomes
- Do Sanctions or Moral Costs Prevent the Formation of Cartel Agreements?
- Efficiency and Distributional Fairness in a Bankruptcy Procedure: A Laboratory Experiment
- Soft Regulation for Financial Advisors
- Conciliation, Social Preferences, and Pre-Trial Settlement: A Laboratory Experiment
- The Impact of Tax Culture on Tax Rate Structure Preferences: Results from a Vignette Study with Migrants and Non-Migrants in Germany
- Perceptions of Justice: Assessing the Perceived Effectiveness of Punishments by Artificial Intelligence versus Human Judges
- Judged by Robots: Preferences and Perceived Fairness of Algorithmic versus Human Punishments
- The Hidden Costs of Whistleblower Protection
- The Missing Window of Opportunity and Quasi-Experimental Effects of Institutional Integration: Evidence from Ukraine
