Paper Citation Record · LEDGER

Evaluating Fairness and Mitigating Bias in Machine Learning: A Novel Technique using Tensor Data and Bayesian Regression

As of 20 August 2026, this Paper Citation Record lists 25 of 25 outbound references and 0 inbound Pith citation observations for arXiv:2506.11627.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2506.11627 v1

Coverage vector

measured 25 of 25 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T04:09:08.974416Z

measured 25 of 25 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

25 of 25 outbound references displayed

verified exact7
verified fuzzy9
unresolved9
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation b56398de-0080-41d8-973e-2bfccec497be · outbound

This paper cites Skin deep: Investigating subjectivity in skin tone annotations for computer vision benchmark datasets.

Evaluating Fairness and Mitigating Bias in Machine Learning: A Novel Technique using Tensor Data and Bayesian Regression Skin deep: Investigating subjectivity in skin tone annotations for computer vision benchmark datasets

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:09:13.056780Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 5ef60819-48f8-4974-ac87-890cc6e9c9af · outbound

This paper cites Fairness with Continuous Optimal Transport.

Evaluating Fairness and Mitigating Bias in Machine Learning: A Novel Technique using Tensor Data and Bayesian Regression Fairness with Continuous Optimal Transport

Reference 4

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 431e8722-6b59-4126-a642-067d57ea3046 · outbound

This paper cites Measurement and fairness.

Evaluating Fairness and Mitigating Bias in Machine Learning: A Novel Technique using Tensor Data and Bayesian Regression Measurement and fairness

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:09:12.643396Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 77f051e7-7ed2-4846-be9e-5728844d18e3 · outbound

This paper cites Estimating and Improving Fairness with Adversarial Learning.

Evaluating Fairness and Mitigating Bias in Machine Learning: A Novel Technique using Tensor Data and Bayesian Regression Estimating and Improving Fairness with Adversarial Learning

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-07T04:09:07.591374Z

Source-reported events for the cited work

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Observation b58a439d-7807-4fe5-ba4e-f4995c3a449d · outbound

This paper cites General fair empirical risk mini- mization.

Evaluating Fairness and Mitigating Bias in Machine Learning: A Novel Technique using Tensor Data and Bayesian Regression General fair empirical risk mini- mization

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:09:11.644346Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 77ad0d6b-f0e1-4fad-87e3-9eeb79f8184b · outbound

This paper cites Towards Fair Face Verification: An In-depth Analysis of Demographic Biases.

Evaluating Fairness and Mitigating Bias in Machine Learning: A Novel Technique using Tensor Data and Bayesian Regression Towards Fair Face Verification: An In-depth Analysis of Demographic Biases

Reference 17

Resolution: verified exact; local_arxiv, observed 2026-08-07T04:09:09.485578Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 882ed445-3d4b-47c9-9964-a4615a627ba3 · outbound

This paper cites Very Deep Convolutional Networks for Large-Scale Image Recognition.

Evaluating Fairness and Mitigating Bias in Machine Learning: A Novel Technique using Tensor Data and Bayesian Regression Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-07T04:09:08.065721Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1c689238-7eb3-4f50-8c40-cc77f043062d · outbound

This paper cites Feature and Label Embedding Spaces Matter in Addressing Image Classifier Bias.

Evaluating Fairness and Mitigating Bias in Machine Learning: A Novel Technique using Tensor Data and Bayesian Regression Feature and Label Embedding Spaces Matter in Addressing Image Classifier Bias

Reference 19

Resolution: verified exact; local_arxiv, observed 2026-08-07T04:09:09.276030Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation e059520c-63e0-474a-8693-f6a10f7c9b14 · outbound

This paper cites The ham10000 dataset, a large col- lection of multi-source dermatoscopic images of common pigmented skin lesions.

Evaluating Fairness and Mitigating Bias in Machine Learning: A Novel Technique using Tensor Data and Bayesian Regression The ham10000 dataset, a large col- lection of multi-source dermatoscopic images of common pigmented skin lesions

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:09:11.429768Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 38be75b2-edc4-4019-945e-7fc5385ebff9 · outbound

This paper cites On the Legal Compatibility of Fairness Definitions.

Evaluating Fairness and Mitigating Bias in Machine Learning: A Novel Technique using Tensor Data and Bayesian Regression On the Legal Compatibility of Fairness Definitions

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-07T04:09:08.498995Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 90cae82c-dec8-418e-80d0-32793bbc6dde · outbound

This paper cites Improving Fairness in Image Classification via Sketching.

Evaluating Fairness and Mitigating Bias in Machine Learning: A Novel Technique using Tensor Data and Bayesian Regression Improving Fairness in Image Classification via Sketching

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-07T04:09:08.615391Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b00a6516-e7a2-48f9-bf67-f09db51ef672 · outbound

This paper cites EdgeMixup: Improving Fairness for Skin Disease Classification and Segmentation.

Evaluating Fairness and Mitigating Bias in Machine Learning: A Novel Technique using Tensor Data and Bayesian Regression EdgeMixup: Improving Fairness for Skin Disease Classification and Segmentation

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-07T04:09:08.752363Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d0f15d66-4e7f-4bd7-8a40-b5b88ffad403 · outbound

This paper cites an unresolved cited work.

Evaluating Fairness and Mitigating Bias in Machine Learning: A Novel Technique using Tensor Data and Bayesian Regression Unresolved cited work

Reference 25

Resolution: unresolved; raw_fallback, observed 2026-08-07T04:09:10.937790Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 9b0efa14-f47e-4d83-9730-e0b70e19aa38 · outbound

This paper cites PatchAlign:Fair and Accurate Skin Disease Image Classification by Alignment with Clinical Labels.

Evaluating Fairness and Mitigating Bias in Machine Learning: A Novel Technique using Tensor Data and Bayesian Regression PatchAlign:Fair and Accurate Skin Disease Image Classification by Alignment with Clinical Labels

Reference 1988

Resolution: verified exact; local_arxiv, observed 2026-08-07T04:09:10.427286Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 8ecc66ed-0ecc-40c9-8d0c-fa86b5fad9c1 · outbound

This paper cites Equality act 2010, June.

Evaluating Fairness and Mitigating Bias in Machine Learning: A Novel Technique using Tensor Data and Bayesian Regression Equality act 2010, June

Reference 1997

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:09:12.161307Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 57f5422e-e653-4f9a-97be-d4d5cd147729 · outbound

This paper cites Estimating Skin Tone and Effects on Classification Performance in Dermatology Datasets.

Evaluating Fairness and Mitigating Bias in Machine Learning: A Novel Technique using Tensor Data and Bayesian Regression Estimating Skin Tone and Effects on Classification Performance in Dermatology Datasets

Reference 2009

Resolution: unresolved; no resolver link, observed 2026-08-07T04:09:06.931640Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3d3a1ecd-0d22-49ca-a4b5-27655c159072 · outbound

This paper cites uk/ukpga/2010/15/part/2/chapter/1.

Evaluating Fairness and Mitigating Bias in Machine Learning: A Novel Technique using Tensor Data and Bayesian Regression uk/ukpga/2010/15/part/2/chapter/1

Reference 2013

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:09:11.902509Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 5b4e353a-6d2c-4e38-b894-987f8cde08a9 · outbound

This paper cites FineFACE: Fair Facial Attribute Classification Leveraging Fine-grained Features.

Evaluating Fairness and Mitigating Bias in Machine Learning: A Novel Technique using Tensor Data and Bayesian Regression FineFACE: Fair Facial Attribute Classification Leveraging Fine-grained Features

Reference 2015

Resolution: verified exact; local_arxiv, observed 2026-08-07T04:09:09.688357Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation dea9b3ee-25f4-4b50-aa91-834b4fede5fe · outbound

This paper cites Prior Training Model Performancce This Table 4 provides the performance results of a generic model with a commonly assessed group fairness.

Evaluating Fairness and Mitigating Bias in Machine Learning: A Novel Technique using Tensor Data and Bayesian Regression Prior Training Model Performancce This Table 4 provides the performance results of a generic model with a commonly assessed group fairness

Reference 2017

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:09:11.188392Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 7f4e723c-027a-4165-a660-32491313caf2 · outbound

This paper cites Analysis of Manual and Automated Skin Tone Assignments for Face Recognition Applications.

Evaluating Fairness and Mitigating Bias in Machine Learning: A Novel Technique using Tensor Data and Bayesian Regression Analysis of Manual and Automated Skin Tone Assignments for Face Recognition Applications

Reference 2019

Resolution: verified exact; local_arxiv, observed 2026-08-07T04:09:09.941181Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 3974b854-2517-4df5-9a0a-078b24d84364 · outbound

This paper cites Debiasing Machine Learning Models by Using Weakly Supervised Learning.

Evaluating Fairness and Mitigating Bias in Machine Learning: A Novel Technique using Tensor Data and Bayesian Regression Debiasing Machine Learning Models by Using Weakly Supervised Learning

Reference 2020

Resolution: verified exact; local_arxiv, observed 2026-08-07T04:09:10.660104Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 69d879d6-364a-4c4f-90a8-1f64db303af6 · outbound

This paper cites A maximal correlation approach to imposing fairness in machine learning.

Evaluating Fairness and Mitigating Bias in Machine Learning: A Novel Technique using Tensor Data and Bayesian Regression A maximal correlation approach to imposing fairness in machine learning

Reference 2021

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:09:12.430869Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 69e0911e-b9e1-4d1b-b3e4-ff6fdbad8861 · outbound

This paper cites On the apparent conflict between individual and group fairness.

Evaluating Fairness and Mitigating Bias in Machine Learning: A Novel Technique using Tensor Data and Bayesian Regression On the apparent conflict between individual and group fairness

Reference 2022

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:09:12.899541Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 5ede0358-6fcf-45b4-817b-560419d28afa · outbound

This paper cites Fairness-Aware Neural R\'eyni Minimization for Continuous Features.

Evaluating Fairness and Mitigating Bias in Machine Learning: A Novel Technique using Tensor Data and Bayesian Regression Fairness-Aware Neural R\'eyni Minimization for Continuous Features

Reference 2023

Resolution: verified exact; local_arxiv, observed 2026-08-07T04:09:10.154308Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 145153e0-9c8e-4485-b6d7-98487535d7dc · outbound

This paper cites The Frontiers of Fairness in Machine Learning.

Evaluating Fairness and Mitigating Bias in Machine Learning: A Novel Technique using Tensor Data and Bayesian Regression The Frontiers of Fairness in Machine Learning

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-07T04:09:06.341621Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.