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pymetrics
audit-ai
Python2018–2018
pymetrics / audit-ai

audit-ai

detect demographic differences in the output of machine learning models or other assessments

2Distinct people
2Mentions
0Explicit recommenders
321GitHub stars
REPOSITORY AT A GLANCE

What to know before exploring

What it is

detect demographic differences in the output of machine learning models or other assessments

GitHub

Where it is used

No documented product use has been verified yet.

What people are saying

No recommendation phrase was found in the indexed comments.

No classified warning is attached to this page. Read the discussions before treating silence as approval.

Also in these discussions

No other repository is repeatedly linked in the same discussions.

This week on Hacker News

Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 5:04:57 PM UTC.

No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.

Mentions over time

HN mentions by week2 dated mentions

Observed mentions: 2018-06-03 09:34:34 UTC to 2018-10-10 15:25:26 UTC. Weeks start Monday, UTC.

012018-05-28 00:00:00 UTC to 2018-06-04 00:00:00 UTC (exclusive): 1 mentions; 1 submissions, 0 commentsMay 282018-06-04 00:00:00 UTC to 2018-06-11 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2018-06-11 00:00:00 UTC to 2018-06-18 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2018-06-18 00:00:00 UTC to 2018-06-25 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2018-06-25 00:00:00 UTC to 2018-07-02 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2018-07-02 00:00:00 UTC to 2018-07-09 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2018-07-09 00:00:00 UTC to 2018-07-16 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2018-07-16 00:00:00 UTC to 2018-07-23 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2018-07-23 00:00:00 UTC to 2018-07-30 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2018-07-30 00:00:00 UTC to 2018-08-06 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 commentsJul 302018-08-06 00:00:00 UTC to 2018-08-13 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2018-08-13 00:00:00 UTC to 2018-08-20 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2018-08-20 00:00:00 UTC to 2018-08-27 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2018-08-27 00:00:00 UTC to 2018-09-03 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2018-09-03 00:00:00 UTC to 2018-09-10 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2018-09-10 00:00:00 UTC to 2018-09-17 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2018-09-17 00:00:00 UTC to 2018-09-24 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2018-09-24 00:00:00 UTC to 2018-10-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2018-10-01 00:00:00 UTC to 2018-10-08 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2018-10-08 00:00:00 UTC to 2018-10-15 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 commentsOct 8

Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.

View mention counts and date ranges
UTC ranges include their start and exclude their end. Select a period to filter the source evidence.
PeriodStart (UTC)End (exclusive, UTC)MentionsSubmissionsComments
May 282018-05-28 00:00:00 UTC2018-06-04 00:00:00 UTC110
Jun 42018-06-04 00:00:00 UTC2018-06-11 00:00:00 UTC000
Jun 112018-06-11 00:00:00 UTC2018-06-18 00:00:00 UTC000
Jun 182018-06-18 00:00:00 UTC2018-06-25 00:00:00 UTC000
Jun 252018-06-25 00:00:00 UTC2018-07-02 00:00:00 UTC000
Jul 22018-07-02 00:00:00 UTC2018-07-09 00:00:00 UTC000
Jul 92018-07-09 00:00:00 UTC2018-07-16 00:00:00 UTC000
Jul 162018-07-16 00:00:00 UTC2018-07-23 00:00:00 UTC000
Jul 232018-07-23 00:00:00 UTC2018-07-30 00:00:00 UTC000
Jul 302018-07-30 00:00:00 UTC2018-08-06 00:00:00 UTC000
Aug 62018-08-06 00:00:00 UTC2018-08-13 00:00:00 UTC000
Aug 132018-08-13 00:00:00 UTC2018-08-20 00:00:00 UTC000
Aug 202018-08-20 00:00:00 UTC2018-08-27 00:00:00 UTC000
Aug 272018-08-27 00:00:00 UTC2018-09-03 00:00:00 UTC000
Sep 32018-09-03 00:00:00 UTC2018-09-10 00:00:00 UTC000
Sep 102018-09-10 00:00:00 UTC2018-09-17 00:00:00 UTC000
Sep 172018-09-17 00:00:00 UTC2018-09-24 00:00:00 UTC000
Sep 242018-09-24 00:00:00 UTC2018-10-01 00:00:00 UTC000
Oct 12018-10-01 00:00:00 UTC2018-10-08 00:00:00 UTC000
Oct 82018-10-08 00:00:00 UTC2018-10-15 00:00:00 UTC101

What Hacker News says

2 sources
This is not very surprising - Machine Learning algorithms trained on biased datasets tend to pick up the hidden biases in the training data. It’s important that we be transparent about the training data that we are using, and are looking for hidden biases in it, otherwise we are building biased systems. Fortunately, there are open source tools out there that help audit machine learning models for bias, such as Audit AI, released by pymetrics - https://github.com/pymetrics/audit-ai
In Amazon scraps secret AI recruiting tool that showed bias against women

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