This repository contains code used to audit the stability of personality predictions made by two algorithmic hiring systems

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Deep Learninghiring-stability-audit
Overview

Stability Audit

This repository contains code used to audit the stability of personality predictions made by two algorithmic hiring systems, Humantic AI and Crystal. This codebase supports the 2021 manuscript entitled "External Stability Auditing to Test the Validity of Personality Prediction in AI Hiring," authored by Alene K. Rhea, Kelsey Markey, Lauren D'Arinzo, Hilke Schellmann, Mona Sloane, Paul Squires, and Julia Stoyanovich.

Code

The Jupyter notebook analysis.ipynb reads in the survey and system output data, and performs all stability analysis. The notebook begins with a demographic summarization, and then estimates stability metrics for each facet experiment as described in the manuscript.

Spearman's rank correlation is used to measure rank-order stability, two-tailed Wilcoxon signed rank testing is used to measure locational stability, and normalized L1 distance is used to measure total change across each facet. Medians of each facet treatment are estimated as well. Results are saved to the results directory, organized by metric and by system (Humantic AI and Crystal). Subgroup analysis is performed for rank-order stability and total change. Highlighting is employed to indicate correlations below 0.95 and 0.90, and Wilcoxon p-values below the Bonferroni and Benjamini-Hochberg corrected thresholds. Scatterplots are produced to compare the outputs from each pair of facet treatments. Boxplots illustrate total change. Boxplots comparing relevant subgroup analysis for each facet are produced as well.

Data

Survey

Anonymized survey results are saved in data/survey.csv. Columns described in the table below.

Column Type Description Values
Participant_ID str Unique ID used to identify participant. "ID2" - "ID101" (missing IDs indicate potential subjects were screened out of participation)
gender str Participant gender, as reported in the survey. Pre-processed to mask rare responses in order to preserve anonymity. ["Male" "Female" "Other Gender"]
race str Participant race, as reported in the survey. Pre-processed to mask rare responses in order to preserve anonymity. Empty entries indicates participants declined to self-identify their race in the survey. ["Asian" "White" "Other Race" NaN]
birth_country str Participant birth country, as reported in the survey. Pre-processed to mask rare responses in order to preserve anonymity. Empty entries indicates participants declined to provide their birth country in the survey. ["China" "India" "USA" "Other Country" NaN]
primary_language str Primary language of participant, as reported in the survey. ["English" "Other Langauge"]
resume bool Boolean flag indicating whether participant provided a resume in the survey. ["True" "False"]
linkedin bool Boolean flag indicating whether participant provided a LinkedIn in the survey. ["True" "False"]
twitter bool Boolean flag indicating whether participant provided a public Twitter handle in the survey. ["True" "False"]
linkedin_in_orig_resume bool Boolean flag indicating whether participant included a reference to their LinkedIn in the resume they submitted. Empty entries indicate participants did not submit a resume. ["True" "False" NaN]
orig_embed_type str Description of the method by which the participant referenced their LinkedIn in their submitted resume. Empty entries indicate participant did not submit a resume containing a reference to LinkedIn. ["Full url hyperlinked" "Full url not hyperlinked" "Text hyperlinked" "Other not hyperlinked" NaN]
orig_file_type str Filetype of the resume submitted by the participant. Empty entries indicate participants did not submit a resume. ["pdf" "docx" "txt" NaN]

Humantic AI and Crystal Output

Output from Humantic AI and Crystal is saved in the data directory. Each run is saved as a CSV and is named with its Run ID. Tables 3 and 4 in the manuscript (reproduced below) provide details of each run. Each file contains one row for each submitted input. Participant_ID provides a unique key, and output_success is a Boolean flag indicating that the system successfully produced output from the given input. Wherever output_success is true, there will be numeric predictions for each trait. Crystal results contain predictions for DiSC traits, and Humantic AI results contain predictions for DiSC traits and Big Five traits.

Run ID System Description Run Dates
HRo1 Humantic AI Original Resume 11/23/2020 - 01/14/2021
HRi1 Humantic AI De-Identified Resume 03/20/2021 - 03/28/2021
HRi2 Humantic AI De-Identified Resume 04/20/2021 - 04/28/2021
HRi3 Humantic AI De-Identified Resume 04/20/2021 - 04/28/2021
HRd1 Humantic AI DOCX Resume 03/20/2021 - 03/28/2021
HRu1 Humantic AI URL-Embedded Resume 04/09/2021 - 04/11/2021
HL1 Humantic AI LinkedIn 11/23/2020 - 01/14/2021
HL2 Humantic AI LinkedIn 08/10/2021 - 08/11/2021
HT1 Humantic AI Twitter 11/23/2020 - 01/14/2021
HT2 Humantic AI Twitter 08/10/2021 - 08/11/2021
CRr1 Crystal Raw Text Resume 03/31/2021 - 04/02/2021
CRr2 Crystal Raw Text Resume 05/01/2021 - 05/03/2021
CRr3 Crystal Raw Text Resume 05/01/2021 - 05/03/2021
CRp1 Crystal PDF Resume 11/23/2020 - 01/14/2021
CL1 Crystal LinkedIn 11/23/2020 - 01/14/2021
CL2 Crystal LinkedIn 09/13/2020 - 09/16/2021
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Data, Responsibly
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