Judged by Robots: Preferences and Perceived Fairness of Algorithmic versus Human Punishments
-
Irene Locci
and
Sébastien Massoni
Abstract
Automated decision-making is increasingly prevalent, prompting discussions about AI replacing judges in court. This paper explores how machine-made sentencing decisions are perceived through an experimental study using a public good game with punishment. The study examines preferences for human versus automated punishers and the perceived fairness of penalties. Results indicate that rule violators prefer algorithmic punishment when penalty severity is uncertain and violations are significant. While human judges are typically reluctant to delegate, they are more likely to do this when they do not have discretion over the sanction level. Fairness perceptions are similar for both humans and algorithms, except when human judges choose a less severe penalty, which enhances perceived fairness.
Funding source: Program FUTURE LEADER of Lorraine Université d’Excellence within the program Investissements Avenir
Award Identifier / Grant number: ANR-15-IDEX-04-LUE
A.1 Contributions and Number of Violators
A.1.1 Percentage of Violators
Marginal effects from a Probit regression of full contribution violations.
| (1) violation dy/dx (se) | |
|---|---|
| Treatment 1 | 0.1101 |
| (0.0717) | |
| Treatment 2 | 0.1787b |
| (0.0726) | |
| Attitudes towards AI | −0.1058a |
| (0.0594) | |
| Round | −0.0131c |
| (0.0028) | |
| Age | −0.0077a |
| (0.0032) | |
| Female | 0.1545a |
| (0.0599) | |
| Risk seeking | 0.0487c |
| (0.0123) | |
| Level of studies | 0.0267 |
| (0.0174) | |
| Observations | 1761 |
| log (likelihood) | −1,101.3929 |
| Wald χ2(8) | 52.51 |
| Prob > χ2 | < 0.0001 |
| Pseudo-R2 | 0.0931 |
-
Standard errors in parentheses clustered at the individual level. ap < 0.10, bp < 0.05, cp < 0.01.
OLS regression of the amounts contributed to the public good.
| (1) player.contribution b/se | |
|---|---|
| Treatment 2 | −13.9886a |
| (7.1562) | |
| Treatment 3 | −6.9854 |
| (7.7199) | |
| Attitudes towards AI | 7.7685 |
| (6.3866) | |
| Round | 1.1500c |
| (0.3115) | |
| Age | 0.4705 |
| (0.3285) | |
| Female | −11.4674a |
| (6.6734) | |
| Risk seeking | −5.8013c |
| (1.8295) | |
| Level of studies | −3.0778a |
| (1.7091) | |
| Constant | 166.8885c |
| (29.2351) | |
| Observations | 1761 |
| log (likelihood) | −9.56e+03 |
| F-test | 4.3412 |
| R 2 | 0.0789 |
-
Standard errors in parentheses clustered at the individual level. ap < 0.10, cp < 0.01.
Wilcoxon rank sum tests of fairness ratings between human and algorithm based on treatment and severity of sanction.
| Treatment | Severity | z | P > |z| |
|---|---|---|---|
| 1 | Mild | −1.164 | 0.2444 |
| Severe | 0.397 | 0.6912 | |
| 2 | Mild | −3.756 | 0.0002 |
| Severe | 1.455 | 0.1457 | |
| 3 | Mild | −1.128 | 0.2592 |
| Severe | −0.982 | 0.3262 |
Dunn test for pairwise comparisons of fairness across conditions based on agent type, severity and treatments with Bonferroni correction following a Kruskal–Wallis test.
| Agent type | Severity | Treatments | z | P > |z| |
|---|---|---|---|---|
| Human | Mild | 1–2 | −3.463 | 0.0008 |
| 1–3 | 0.042 | 1.000 | ||
| 2–3 | 3.401 | 0.0010 | ||
| Human | Severe | 1–2 | 2.536 | 0.0168 |
| 1–3 | 1.022 | 0.4605 | ||
| 2–3 | −1.342 | 0.2692 | ||
| Computer | Mild | 1–2 | −1.053 | 0.4386 |
| 1–3 | −0.221 | 1.000 | ||
| 2–3 | 0.815 | 0.6228 | ||
| Computer | Severe | 1–2 | 1.448 | 0.2214 |
| 1–3 | 2.499 | 0.0187 | ||
| 2–3 | 1.206 | 0.3419 |
Example of the table for the punishment in Treatment 1.
| Contribution | Rule |
|---|---|
| 0 | 195 |
| 10 | 185 |
| 20 | 175 |
| 30 | 165 |
| 40 | 155 |
| 50 | 145 |
| 60 | 135 |
| 70 | 125 |
| 80 | 115 |
| 90 | 105 |
| 100 | 95 |
| 110 | 85 |
| 120 | 75 |
| 130 | 65 |
| 140 | 55 |
| 150 | 45 |
| 160 | 35 |
| 170 | 25 |
| 180 | 15 |
| 190 | 5 |
| 200 | 0 |
Rules for the punishment displayed in Treatment 2.
| Contribution | Rule 1 | Rule 2 |
|---|---|---|
| 0 | 195 | 250 |
| 10 | 185 | 240 |
| 20 | 175 | 230 |
| 30 | 165 | 220 |
| 40 | 155 | 210 |
| 50 | 145 | 200 |
| 60 | 135 | 190 |
| 70 | 125 | 180 |
| 80 | 115 | 170 |
| 90 | 105 | 160 |
| 100 | 95 | 150 |
| 110 | 85 | 140 |
| 120 | 75 | 130 |
| 130 | 65 | 120 |
| 140 | 55 | 110 |
| 150 | 45 | 100 |
| 160 | 35 | 90 |
| 170 | 25 | 80 |
| 180 | 15 | 70 |
| 190 | 5 | 60 |
| 200 | 0 | 0 |
Subsection 3.2 presents the mean comparisons between treatments. Table 6 shows the marginal effects of a Probit regression on being a violator.
We can observe that Treatment 3 is still giving a lower amount of violation than Treatment 2 (dy/dx = 0.1787, p = 0.014) but this effect does not hold anymore compared to Treatment 1 (dy/dx = 0.1101, p = 0.125). We can emphasize different interesting effects on the probability of not fully contribute: it decreases over time during the experiment (dy/dx = −0.0131, p < 0.0001) and individual characteristics matter (positively for being risk seeking and being a female, negatively for begin older and having a positive attitude toward A.I.).
A.1.2 Amounts Contributed
In terms of the amount contributed, there is no statistically significant difference between treatments (T1 = 166.05, T2 = 159.07, T3 = 159.53, with all p-values higher than 0.10). Table 7 shows the OLS regression of these amounts on the treatments and our control variables.
Consistent with the probability of being a violator, we find that the level of contribution is negatively affected by the fact of being risk seeking and being a female. It also increases with time over the experiment.
A.2 Fairness Ratings
A.2.1 Between Human Judge and Algorithm
A.2.2 Conditions Across Treatments
B.1 Instructions
You are now taking part in an economic experiment.
Your payoff will depend on your decision and the decision of the other people in your group. It is therefore important that you take your time to understand the instructions.
The instructions which we have distributed to you are for your private information. Please do not communicate with the other participants during the experiment.
Should you have any questions please ask us.
During the experiment we shall not speak of Euros, but of points. Your entire earnings will be calculated in points. At the end of the experiment one random round will be selected and the points you have earned in that round will be converted to Euros at the rate of 1 point = 0.037 €.
The experiment is anonymous and lasts 20 rounds. At the beginning of the experiment the participants will be randomly divided into groups of five. You will therefore always be in a group with 4 other participants that you will not know the identity of. In every round the composition of the group will be different from the previous round. You may play or not play with people you have already played with. For each round, there will be two stages:
B.2 Stage 1
4 people are called contributors: they will have 200 points. In the following, we shall refer to this amount as the “endowment”. Your task is to decide how to use your endowment.
In particular, you have to decide how many of the 200 points you want to contribute to a project (from 0 to 200) and how many of them to keep for yourself. The consequences of your decision are explained in detail below.
Once all the players have decided their contribution to the project you will be informed about the group’s total contribution, your income from the project and your payoff in this stage.
Your payoff from the first stage in each period is calculated using the following formula. If you have any difficulties, do not hesitate to ask us.
Income at the end of the stage = Endowment of points – Your contribution to the Project + 0.5 * Total contribution to the Project
This formula shows that your income at the end of the period consists of two parts:
The points which you have kept for yourself (endowment – contribution),
The income from the project, which equals to 50 % of the group’s total contribution.
The income of each group member from the project is calculated in the same way. This means that each group member receives the same income from the project.
Suppose the sum of the contributions of all group members is 600 points. In this case, each member of the group receives an income from the project of: 0.5 * 600 = 300 points.
If the total contribution to the project is 90 points, then each member of the group receives an income of: 0.5 * 90 = 45 points from the project, regardless of how much they individually contributed to the project.
You always have the option of keeping the points for yourself or contributing them to the project. Each point that you keep raises your end of period income by 1 point.
Supposing you contributed this point to the project instead, then the total contribution to the project would rise by 1 point. Your income from the project would thus rise by 0.5 * 1 = 0.5 points. However, the income of the other group members would also rise by 0.5 points each, so that the total income of the group from the project would be 2 points. Your contribution to the project therefore also raises the income of the other group members.
On the other hand, you also earn an income for each point contributed by the other members to the project. In particular, for each point contributed by any member you earn 0.5 points.
In addition to the 200 points per period, each participant receives a one-off lump sum payment of €5 at the beginning of this part of the experiment. Note that this lump sum payment should not be used to calculate the “End of period income”. It will only be added to your total income from all the periods at the very end.
B.3 Stage 2
At the second stage of each period, you will be informed about how much each group member contributed individually to the project at the first stage. All the participants who did not contribute 200 points will have to be punished.
After the contribution, the participants can choose whether to be punished automatically by the computer or delegate the decision to an external observer.
One person in the group will always be the external observer. He will never contribute in stage 1, but will always have to punish in stage 2.
Example: Suppose you contribute less than 200 points, then your total income after the two stages will be:
Total income at the end of the period = Income from the 1st stage – points of the punishment
B.4 Rules for the Punishment
B.4.1 [Treatment 1]
For the punishment the computer and the external observer will follow the rules given by a table. This table has a certain number of points that will be subtracted for every level of contribution. An example of the table may be the one in Table 10.
For example, if you contribute 100 and chose to be punished by the computer, the computer will automatically subtract 95 points from you as a punishment.
For example, if you contribute 100 and chose to delegate the decision to the external observer, the observer will subtract 95 points from you as a punishment.
B.4.2 [Treatment 2]
For the punishment the computer and the external observer will follow the rules given by the following table. This table has two rules that represent two possible numbers of points that will be subtracted for every level of contribution. The computer will have 50 % chance of using the first punishment rule and 50 % chance of using the second punishment rule. The external observer will have the choice between using the first punishment rule or the second punishment rule. The rules are displayed in Table 11.
For example, if you contribute 100 and chose to be punished by the computer, the computer will subtract 95 points with 50 % probability and 150 points with 50 % probability from you as a punishment.
For example, if you contribute 100 and chose to delegate the decision to the external observer, the observer will choose whether to subtract 95 points or 150 points with from you as a punishment.
B.4.3 [Treatment 3]
For example, if you contribute 100 and chose to be punished by the computer, the computer will subtract some points from you as a punishment.
For example, if you contribute 100 and chose to delegate the decision to the external observer, the observer will subtract some points from you as a punishment.
B.5 Fairness Rating
B.5.1 Algorithm Judge
You contributed x. You decided to be punished by the computer. You have been punished y.
How fair do you think the punishment was?
B.5.2 Human Judge
You contributed x. You decided to be punished by the external observer. You have been punished y.
How fair do you think the punishment was?
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Articles in the same Issue
- Frontmatter
- Articles
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- Legal Compliance and Detection Avoidance: Results on the Impact of Different Law-Enforcement Designs
- Women in Piracy. Experimental Perspectives on Copyright Infringement
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- 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
