Paper Citation Record · LEDGER

Mitigating Algorithmic Bias in Multiclass CNN Classifications Using Causal Modeling

As of 18 August 2026, this Paper Citation Record lists 22 of 22 outbound references and 0 inbound Pith citation observations for arXiv:2501.07885.

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

pith.paper-citation-record.v1
2501.07885 v1

Coverage vector

measured 22 of 22 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T20:35:25.728621Z

measured 22 of 22 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+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

22 of 22 outbound references displayed

verified exact2
verified fuzzy11
unresolved7
parse uncertain0
malformed identifier1
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation a6329994-6267-415d-ac1d-3edc3755b00b · outbound

This paper cites A survey on bias and fairness in machine learning,.

Mitigating Algorithmic Bias in Multiclass CNN Classifications Using Causal Modeling A survey on bias and fairness in machine learning,

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-10T20:35:25.628076Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7dede9d2-0f1c-4d84-a363-2a62d6a9a774 · outbound

This paper cites Fairness And Bias in Artificial Intelligence: A Brief Survey of Sources, Impacts, And Mitigation Strategies.

Mitigating Algorithmic Bias in Multiclass CNN Classifications Using Causal Modeling Fairness And Bias in Artificial Intelligence: A Brief Survey of Sources, Impacts, And Mitigation Strategies

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-10T20:35:25.633650Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d07ab902-8fcd-4c2d-a2db-bf75424ae65e · outbound

This paper cites Mahoney, K.

Mitigating Algorithmic Bias in Multiclass CNN Classifications Using Causal Modeling Mahoney, K

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:35:26.387526Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 73f000ce-9e8c-4215-8d84-6687bde93acb · outbound

This paper cites Hardt, E.

Mitigating Algorithmic Bias in Multiclass CNN Classifications Using Causal Modeling Hardt, E

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:35:26.371514Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 60262ad0-4aaa-4cbf-a7e9-44180b5d606e · outbound

This paper cites Detecting and mitigating algorithmic bias in binary classification using causal modeling,.

Mitigating Algorithmic Bias in Multiclass CNN Classifications Using Causal Modeling Detecting and mitigating algorithmic bias in binary classification using causal modeling,

Reference 5

Resolution: malformed identifier; doi_truncated, observed 2026-08-10T20:35:26.170416Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 5e7fda7f-3e67-4e62-9eb7-5e4262b6de8c · outbound

This paper cites Putzel and S.

Mitigating Algorithmic Bias in Multiclass CNN Classifications Using Causal Modeling Putzel and S

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:35:26.355004Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 78b82969-ecf8-45a6-94d5-88bc4ff71f8c · outbound

This paper cites Mitigating Nonlinear Algorithmic Bias in Binary Classification,.

Mitigating Algorithmic Bias in Multiclass CNN Classifications Using Causal Modeling Mitigating Nonlinear Algorithmic Bias in Binary Classification,

Reference 7

Resolution: verified exact; raw_fallback, observed 2026-08-10T20:35:26.081224Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation f2447299-664e-4486-a10c-d83d86f9663d · outbound

This paper cites Data augmentation for discrimination prevention and bias disambiguation,.

Mitigating Algorithmic Bias in Multiclass CNN Classifications Using Causal Modeling Data augmentation for discrimination prevention and bias disambiguation,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:35:26.339981Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation e4b5c564-ed27-4e31-aade-dad9f6ab51b9 · outbound

This paper cites AI Fairness via Domain Adaptation.

Mitigating Algorithmic Bias in Multiclass CNN Classifications Using Causal Modeling AI Fairness via Domain Adaptation

Reference 9

Resolution: verified exact; local_arxiv, observed 2026-08-10T20:35:25.990185Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation c6cd6a36-2b92-4782-aa86-887a8dcb1303 · outbound

This paper cites Classification with fairness constraints: A meta-algorithm with provable guarantees,.

Mitigating Algorithmic Bias in Multiclass CNN Classifications Using Causal Modeling Classification with fairness constraints: A meta-algorithm with provable guarantees,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:35:26.324694Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation e2191966-4b57-4049-9850-993c6ac4551a · outbound

This paper cites Data Decisions and Theoretical Implications when Adversarially Learning Fair Representations.

Mitigating Algorithmic Bias in Multiclass CNN Classifications Using Causal Modeling Data Decisions and Theoretical Implications when Adversarially Learning Fair Representations

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-10T20:35:25.680631Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8f9263cd-56f7-4bee-b783-0bd78fa88849 · outbound

This paper cites an unresolved cited work.

Mitigating Algorithmic Bias in Multiclass CNN Classifications Using Causal Modeling Unresolved cited work

Reference 12

Resolution: unresolved; raw_fallback, observed 2026-08-10T20:35:26.309839Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 123ab1f2-b42c-4897-966c-dba4c85cbc58 · outbound

This paper cites Direct and indirect effects,.

Mitigating Algorithmic Bias in Multiclass CNN Classifications Using Causal Modeling Direct and indirect effects,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:35:26.294262Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 38edef7c-1451-47b4-a9cd-a91fb0c4ef13 · outbound

This paper cites Pearl, Causality.

Mitigating Algorithmic Bias in Multiclass CNN Classifications Using Causal Modeling Pearl, Causality

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-10T20:35:25.695211Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e3843b8c-4a80-4f30-88d8-149e1672f708 · outbound

This paper cites Counterfactual fairness,.

Mitigating Algorithmic Bias in Multiclass CNN Classifications Using Causal Modeling Counterfactual fairness,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:35:26.268268Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 06887638-8181-4044-8350-c65ada131670 · outbound

This paper cites Fairness through causal awareness: Learning causal latent-variable models for biased data,.

Mitigating Algorithmic Bias in Multiclass CNN Classifications Using Causal Modeling Fairness through causal awareness: Learning causal latent-variable models for biased data,

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-10T20:35:25.704067Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 243ea819-b3f9-4f6b-a917-8152a66bf8e4 · outbound

This paper cites Fairness in algorithmic decision making: An excursion through the lens of causality,.

Mitigating Algorithmic Bias in Multiclass CNN Classifications Using Causal Modeling Fairness in algorithmic decision making: An excursion through the lens of causality,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:35:26.253349Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 972e411a-bf62-49aa-960e-c1e62470b925 · outbound

This paper cites Available: https://huggingface.co/datasets/HuggingF aceM4/FairFace.

Mitigating Algorithmic Bias in Multiclass CNN Classifications Using Causal Modeling Available: https://huggingface.co/datasets/HuggingF aceM4/FairFace

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:35:26.236618Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 3b5f6398-743b-46d1-84b6-0142f6704602 · outbound

This paper cites Available: https://github.com/serengil/deepface.

Mitigating Algorithmic Bias in Multiclass CNN Classifications Using Causal Modeling Available: https://github.com/serengil/deepface

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:35:26.219724Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 9b2e4d47-0115-44b6-ad45-9fe0a1627169 · outbound

This paper cites Transforming classifier scores into accurate multiclass probability estimates,.

Mitigating Algorithmic Bias in Multiclass CNN Classifications Using Causal Modeling Transforming classifier scores into accurate multiclass probability estimates,

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-10T20:35:25.723983Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ceaab53c-83a1-42c1-821c-04ceeb448be4 · outbound

This paper cites Zhang and L.

Mitigating Algorithmic Bias in Multiclass CNN Classifications Using Causal Modeling Zhang and L

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:35:26.202116Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation a24906b2-092d-48a3-8b4c-1dbfca8a9be9 · outbound

This paper cites Blackbox Post-Processing for Multiclass Fairness.

Mitigating Algorithmic Bias in Multiclass CNN Classifications Using Causal Modeling Blackbox Post-Processing for Multiclass Fairness

Reference 2022

Resolution: metadata mismatch; local_arxiv, observed 2026-08-10T20:35:26.103145Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Pith citing papers

No inbound Pith citation observations are available.