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

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
Owner
Data, Responsibly
responsible data management: platform and tools
Data, Responsibly
Open source implementation of "A Self-Supervised Descriptor for Image Copy Detection" (SSCD).

A Self-Supervised Descriptor for Image Copy Detection (SSCD) This is the open-source codebase for "A Self-Supervised Descriptor for Image Copy Detecti

Meta Research 68 Jan 04, 2023
YourTTS: Towards Zero-Shot Multi-Speaker TTS and Zero-Shot Voice Conversion for everyone

YourTTS: Towards Zero-Shot Multi-Speaker TTS and Zero-Shot Voice Conversion for everyone In our recent paper we propose the YourTTS model. YourTTS bri

Edresson Casanova 390 Dec 29, 2022
Global Pooling, More than Meets the Eye: Position Information is Encoded Channel-Wise in CNNs, ICCV 2021

Global Pooling, More than Meets the Eye: Position Information is Encoded Channel-Wise in CNNs, ICCV 2021 Global Pooling, More than Meets the Eye: Posi

Md Amirul Islam 32 Apr 24, 2022
tmm_fast is a lightweight package to speed up optical planar multilayer thin-film device computation.

tmm_fast tmm_fast or transfer-matrix-method_fast is a lightweight package to speed up optical planar multilayer thin-film device computation. It is es

26 Dec 11, 2022
Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy" (ICLR 2022 Spotlight)

About Code release for Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy (ICLR 2022 Spotlight)

THUML @ Tsinghua University 221 Dec 31, 2022
An implementation of "MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing" (ICML 2019).

MixHop and N-GCN ⠀ A PyTorch implementation of "MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing" (ICML 2019)

Benedek Rozemberczki 393 Dec 13, 2022
Code for: Imagine by Reasoning: A Reasoning-Based Implicit Semantic Data Augmentation for Long-Tailed Classification

Imagine by Reasoning: A Reasoning-Based Implicit Semantic Data Augmentation for Long-Tailed Classification Prerequisite PyTorch = 1.2.0 Python3 torch

16 Dec 14, 2022
DETReg: Unsupervised Pretraining with Region Priors for Object Detection

DETReg: Unsupervised Pretraining with Region Priors for Object Detection Amir Bar, Xin Wang, Vadim Kantorov, Colorado J Reed, Roei Herzig, Gal Chechik

Amir Bar 283 Dec 27, 2022
In real-world applications of machine learning, reliable and safe systems must consider measures of performance beyond standard test set accuracy

PixMix Introduction In real-world applications of machine learning, reliable and safe systems must consider measures of performance beyond standard te

Andy Zou 79 Dec 30, 2022
This is the latest version of the PULP SDK

PULP-SDK This is the latest version of the PULP SDK, which is under active development. The previous (now legacy) version, which is no longer supporte

78 Dec 07, 2022
Code for "Typilus: Neural Type Hints" PLDI 2020

Typilus A deep learning algorithm for predicting types in Python. Please find a preprint here. This repository contains its implementation (src/) and

47 Nov 08, 2022
Implementation of Bidirectional Recurrent Independent Mechanisms (Learning to Combine Top-Down and Bottom-Up Signals in Recurrent Neural Networks with Attention over Modules)

BRIMs Bidirectional Recurrent Independent Mechanisms Implementation of the paper Learning to Combine Top-Down and Bottom-Up Signals in Recurrent Neura

Sarthak Mittal 26 May 26, 2022
Implementation of "With a Little Help from my Temporal Context: Multimodal Egocentric Action Recognition, BMVC, 2021" in PyTorch

Multimodal Temporal Context Network (MTCN) This repository implements the model proposed in the paper: Evangelos Kazakos, Jaesung Huh, Arsha Nagrani,

Evangelos Kazakos 13 Nov 24, 2022
In this work, we will implement some basic but important algorithm of machine learning step by step.

WoRkS continued English 中文 Français Probability Density Estimation-Non-Parametric Methods(概率密度估计-非参数方法) 1. Kernel / k-Nearest Neighborhood Density Est

liziyu0104 1 Dec 30, 2021
DenseNet Implementation in Keras with ImageNet Pretrained Models

DenseNet-Keras with ImageNet Pretrained Models This is an Keras implementation of DenseNet with ImageNet pretrained weights. The weights are converted

Felix Yu 568 Oct 31, 2022
Perception-aware multi-sensor fusion for 3D LiDAR semantic segmentation (ICCV 2021)

Perception-Aware Multi-Sensor Fusion for 3D LiDAR Semantic Segmentation (ICCV 2021) [中文|EN] 概述 本工作主要探索一种高效的多传感器(激光雷达和摄像头)融合点云语义分割方法。现有的多传感器融合方法主要将点云投影

ICE 126 Dec 30, 2022
Code for Paper: Self-supervised Learning of Motion Capture

Self-supervised Learning of Motion Capture This is code for the paper: Hsiao-Yu Fish Tung, Hsiao-Wei Tung, Ersin Yumer, Katerina Fragkiadaki, Self-sup

Hsiao-Yu Fish Tung 87 Jul 25, 2022
DeepSpeed is a deep learning optimization library that makes distributed training easy, efficient, and effective.

DeepSpeed+Megatron trained the world's most powerful language model: MT-530B DeepSpeed is hiring, come join us! DeepSpeed is a deep learning optimizat

Microsoft 8.4k Dec 28, 2022
A simple PyTorch Implementation of Generative Adversarial Networks, focusing on anime face drawing.

AnimeGAN A simple PyTorch Implementation of Generative Adversarial Networks, focusing on anime face drawing. Randomly Generated Images The images are

Jie Lei 雷杰 1.2k Jan 03, 2023
Unity Propagation in Bayesian Networks Handling Inconsistency via Unity Smoothing

This repository contains the scripts needed to generate the results from the paper Unity Propagation in Bayesian Networks Handling Inconsistency via U

0 Jan 19, 2022