Fairness perceptions of AI use by tax administration (doi:10.34934/DVN/1NHGXH)

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Part 2: Study Description
Part 3: Data Files Description
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Document Description

Citation

Title:

Fairness perceptions of AI use by tax administration

Identification Number:

doi:10.34934/DVN/1NHGXH

Distributor:

Social Sciences and Digital Humanities Archive – SODHA

Date of Distribution:

2024-03-07

Version:

1

Bibliographic Citation:

Decuypere, Anouk; Anne Van de Vijver, 2024, "Fairness perceptions of AI use by tax administration", https://doi.org/10.34934/DVN/1NHGXH, Social Sciences and Digital Humanities Archive – SODHA, V1, UNF:6:bX3joxuUwAPJ8WTD68gU6g== [fileUNF]

Study Description

Citation

Title:

Fairness perceptions of AI use by tax administration

Subtitle:

Students, professionals and a representative Flemish sample

Identification Number:

doi:10.34934/DVN/1NHGXH

Authoring Entity:

Decuypere, Anouk (Universiteit Antwerpen)

Anne Van de Vijver (Universiteit Antwerpen)

Software used in Production:

SPSS

Software used in Production:

Excel

Grant Number:

/

Grant Number:

G043723N

Distributor:

Social Sciences and Digital Humanities Archive – SODHA

Access Authority:

Decuypere, Anouk

Depositor:

Decuypere, Anouk

Date of Deposit:

2024-03-05

Holdings Information:

https://doi.org/10.34934/DVN/1NHGXH

Study Scope

Keywords:

Social Sciences

Topic Classification:

Fairnes

Abstract:

We tested whether the proportion of AI versus auditors in fraud selection matters for fairness, and whether there is an impact of transparency (explanations). We found that a higher proportion of AI was more procedurally fair, mostly through bias suppression and consistency, and that the attitude toward AI and trust in the administration explained most variance. Transparency (explanations) had no impact. We also found two small negative interaction effects concerning trust and procedural fairness: with high trust in the tax administration, fairness increased less (as AI increased). Conversely, with low trust, fairness increased more (as AI increased). Dataset 1 was used for the pilot (with students and professionals) Dataset 2 was a representative dataset for the Flemish population.

Time Period:

2022-2023

Unit of Analysis:

Individuals

Universe:

Sample 1: Students and tax professionals (separated in the file) Sample 2: Representative sample of the Flemish population

Kind of Data:

Survey data

Methodology and Processing

Time Method:

Cross-sectional (survey experiment)

Data Collector:

Second sample was collected in collaboration with Bilendi

Sampling Procedure:

Sample 1: Convenience sample Sample 2: Convenience sample drawn from their database with 100,000 participants

Mode of Data Collection:

Online survey

Type of Research Instrument:

Structured survey experiment

Sources Statement

Weighting:

There was weighing in sample 2 to be an accurate representation of the Flemish population, the weighting variable is indicated as such

Cleaning Operations:

Manipulation and attention check, non-complying participants were removed

Notes:

More information can be found in the publication which will be linked later.

Response Rate:

Sample 1: 19% Sample 2: 28%

Data Access

Notes:

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Other Study Description Materials

File Description--f4697

File: Sample 1 SODHA.tab

  • Number of cases: 232

  • No. of variables per record: 85

  • Type of File: text/tab-separated-values

Notes:

UNF:6:sHvdJohBiB3cDh0UzSIXCw==

File Description--f4698

File: Sample 2.tab

  • Number of cases: 2366

  • No. of variables per record: 98

  • Type of File: text/tab-separated-values

Notes:

UNF:6:4wDd4zl/OxBFFKe5bazIJQ==

Other Study-Related Materials

Label:

Sample 1 SODHA.csv

Text:

Students and professionals in Flanders

Notes:

text/csv

Other Study-Related Materials

Label:

Sample 2 SODHA.csv

Text:

A representative sample of Flanders

Notes:

text/csv