Predicting the Amount of Compensation for Harm Awarded by Courts Using Machine-Learning Algorithms

Maciej Świtała

Open Access

Predicting the Amount of Compensation for Harm Awarded by Courts Using Machine-Learning Algorithms

Maciej Świtała

| May 26, 2024

Central European Economic Journal

Volume 11 (2024): Issue 58 (January 2024)

About this article

Cite

Page range: 214 - 232

DOI: https://doi.org/10.2478/ceej-2024-0015

Keywords
compensation amount, harm, machine learning, Polish courts, prediction

© 2024 Maciej Świtała, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Distribution of Amounts Awarded as Compensation for Harm Suffered and Their Logarithms

Distribution of Amounts Awarded as Compensation for Harm Suffered among the 25 Most Impactful Features

Partial Dependence Plots for the Most Impactful Features

Example Model Prediction: Judgement of the Regional Court in Gliwice Dated August 31, 2014, File Number I C 1946/14

Example Model Prediction: Judgement of the Regional Court in Zambrów Dated September 24, 2015, File Number I C 335/15

Tokens Characterized by the Highest Differences Between Maximum and Minimum Value of Their Partial Dependence Plots

Id	Token	Average awarded compensation when token does not occur	Minimum awarded compensation	Maximum awarded compensation	Difference between maximum and minimum awarded compensation
1	pension	21,027.80	21,027.80 (0)	38,725.99 (5)	17,698.18
2	family	16,532.26	16,532.26 (0)	34,000.38 (16)	17,468.12
3	hospital	20,559.30	20,559.30 (0)	35,327.96 (20)	14,768.65
4	fracture	19,852.62	19,852.62 (0)	30,671.07 (25)	10,818.46
5	year	19,473.51	19,473.51 (0)	28,048.15 (10)	8,574.65
6	life	19,712.14	19,712.14 (0)	25,905.34 (36)	6,193.20
7	dead	22,645.50	22,645.50 (0)	28,181.21 (7)	5,535.71
8	zloty_monthly	22,557.29	22,557.29 (0)	27,708.96 (29)	5,151.67
9	extend	22,570.76	22,570.76 (0)	26,658.50 (6)	4,087.74
10	substantial	22,346.35	22,346.35 (0)	25,140.15 (19)	2,793.80
11	bones	23,056.96	23,056.96 (0)	25,536.80 (25)	2,479.84
12	child	22,439.85	22,439.85 (0)	24,908.14 (39)	2,468.29
13	bond	23,108.54	23,108.54 (0)	25,423.49 (24)	2,314.95
14	cervical	23,675.45	21,368.98 (22)	23,675.45 (0)	2,306.47
15	disorders	22,655.92	22,655.92 (0)	24,947.08 (17)	2,291.17
16	collar	23,419.00	22,944.30 (11)	23,419.00 (0)	2,264.04
17	disability	22,944.30	22,944.30 (0)	25,129.94 (5)	2,185.64
18	family_bond	23,262.10	23,262.10 (0)	25,430.35 (10)	2,168.25
19	twist	23,417.04	21,541.70 (7)	23,417.04 (0)	1,875.34
20	future	22,640.79	22,640.79 (0)	24,386.69 (22)	1,745.90

Error Measures Obtained on a Test Set With Different Algorithms Applied

Algorithm applied	Predictors	Root mean squared error	Mean Absolute percentage error	Root median squared error	Median absolute percentage error
OLS	Token counts	308,207.87	112.32	11,747.55	52.79
OLS	TF-IDF	100,743.48	94.59	11,759.07	49.25
LASSO	Token counts	287,706.24	109.68	11,474.52	52.64
LASSO	TF-IDF	95,976.82	93.46	11,435.79	48.64
Random forests	Token counts	75,268.12	86.43	11,064.28	47.16
Random forests	TF-IDF	74,674.55	88.59	10,518.75	48.13
XGBoost	Token counts	94,271.95	105.27	12,654.46	52.18
XGBoost	TF-IDF	192,230.66	102.79	12,821.45	51.48
Multilingual BERT	-	95,665.94	426.45	38,062.44	88.03

Error Measures Obtained on a Test Set With Different Algorithms Applied and Models’ Overestimates Assumed as Not Errors

Algorithm applied	Predictors	Root mean squared error	Mean Absolute percentage error	Root median squared error	Median absolute percentage error
OLS	Token counts	61,829.04	23.92	1,024.79	5.29
OLS	TF-IDF	67,563.77	21.83	178.39	1.08
LASSO	Token counts	62,019.89	23.83	1,061.22	5.57
LASSO	TF-IDF	68,215.32	21.78	129.66	0.69
Random forests	Token counts	74,939.24	23.11	1,729.41	8.01
Random forests	TF-IDF	73,939.24	22.80	1,741.93	9.02
XGBoost	Token counts	71,414.42	23.83	1,069.24	7.93
XGBoost	TF-IDF	65,657.99	24.13	1,209.66	7.68
Multilingual BERT	-	82,212.22	29.04	0.00	0.00

eISSN:: 2543-6821
Language:: English

Publication timeframe:: Volume Open
Journal Subjects:: Business and Economics, Political Economics, Economic Policy, Economic Theory, Systems and Structures, Microeconomics, Macroecomics

Journal RSS Feed

Predicting the Amount of Compensation for Harm Awarded by Courts Using Machine-Learning Algorithms

Published Online: May 26, 2024

Page range: 214 - 232

DOI: https://doi.org/10.2478/ceej-2024-0015

Keywords
compensation amount, harm, machine learning, Polish courts, prediction

© 2024 Maciej Świtała, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Figure 1.

Figure 2.

Figure 3.

Figure 4.

Figure 5.

Tokens Characterized by the Highest Differences Between Maximum and Minimum Value of Their Partial Dependence Plots

Error Measures Obtained on a Test Set With Different Algorithms Applied

Error Measures Obtained on a Test Set With Different Algorithms Applied and Models’ Overestimates Assumed as Not Errors

Predicting the Amount of Compensation for Harm Awarded by Courts Using Machine-Learning Algorithms

Published Online: May 26, 2024

Page range: 214 - 232

DOI: https://doi.org/10.2478/ceej-2024-0015

Keywordscompensation amount, harm, machine learning, Polish courts, prediction

© 2024 Maciej Świtała, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Figure 1.

Figure 2.

Figure 3.

Figure 4.

Figure 5.

Tokens Characterized by the Highest Differences Between Maximum and Minimum Value of Their Partial Dependence Plots

Error Measures Obtained on a Test Set With Different Algorithms Applied

Error Measures Obtained on a Test Set With Different Algorithms Applied and Models’ Overestimates Assumed as Not Errors

Keywords
compensation amount, harm, machine learning, Polish courts, prediction