Paper Citation Record · LEDGER

Towards Provably Fair Machine Learning: Bayesian Approaches For Consistent and Transparent Predictions

As of 11 August 2026, this Paper Citation Record lists 24 of 24 outbound references and 0 inbound Pith citation observations for arXiv:2606.12615.

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

pith.paper-citation-record.v1
2606.12615 v1

Coverage vector

measured 24 of 24 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-06-27T09:55:35.961288Z

measured 24 of 24 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+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

24 of 24 outbound references displayed

verified exact11
verified fuzzy0
unresolved9
parse uncertain0
malformed identifier1
metadata mismatch3

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 6098d182-1f68-43ec-961f-bd75acdeb673 · outbound

This paper cites Machine Bias.

Towards Provably Fair Machine Learning: Bayesian Approaches For Consistent and Transparent Predictions Machine Bias

Reference 1

Resolution: unresolved; no resolver link, observed 2026-06-27T09:55:35.961288Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8c63096f-f1c1-4d02-af33-57effe568278 · outbound

This paper cites Multiaccuracy: Black-Box Post-Processing for Fairness in Classification.

Towards Provably Fair Machine Learning: Bayesian Approaches For Consistent and Transparent Predictions Multiaccuracy: Black-Box Post-Processing for Fairness in Classification

Reference 2

Resolution: verified exact; local_arxiv, observed 2026-07-03T10:37:56.838722Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation ccfe9aa9-5ef5-43bf-a631-4f397bf9f5ff · outbound

This paper cites Efficient Task-Specific Data Valuation for Nearest Neighbor Algorithms.

Towards Provably Fair Machine Learning: Bayesian Approaches For Consistent and Transparent Predictions Efficient Task-Specific Data Valuation for Nearest Neighbor Algorithms

Reference 3

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T10:37:56.835444Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 4ae55e1e-c40c-4616-a982-4f28405b9f94 · outbound

This paper cites On Optimum Recognition Error and Reject Tradeoff.

Towards Provably Fair Machine Learning: Bayesian Approaches For Consistent and Transparent Predictions On Optimum Recognition Error and Reject Tradeoff

Reference 4

Resolution: malformed identifier; arxiv_id, observed 2026-07-03T10:37:56.841551Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 904c888d-6b57-4fff-b7e3-2344e75369e6 · outbound

This paper cites Amazon scraps secret AI recruiting tool that showed bias against women.

Towards Provably Fair Machine Learning: Bayesian Approaches For Consistent and Transparent Predictions Amazon scraps secret AI recruiting tool that showed bias against women

Reference 5

Resolution: unresolved; no resolver link, observed 2026-06-27T09:55:35.961288Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7ef57865-36d7-48c3-8f67-5b7a004ee397 · outbound

This paper cites HappyMap: A Generalized Multi-calibration Method.

Towards Provably Fair Machine Learning: Bayesian Approaches For Consistent and Transparent Predictions HappyMap: A Generalized Multi-calibration Method

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-07-03T10:37:56.844424Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 2061a67c-c82b-4e30-8701-fa3cfdfae336 · outbound

This paper cites Bayesian Fairness.

Towards Provably Fair Machine Learning: Bayesian Approaches For Consistent and Transparent Predictions Bayesian Fairness

Reference 7

Resolution: verified exact; doi, observed 2026-06-27T10:00:49.087627Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 00875d34-aea6-402c-856a-2996e4d03504 · outbound

This paper cites On the foundations of noise-free selective classification.

Towards Provably Fair Machine Learning: Bayesian Approaches For Consistent and Transparent Predictions On the foundations of noise-free selective classification

Reference 8

Resolution: unresolved; no resolver link, observed 2026-06-27T09:55:35.961288Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation aa605d81-3597-40f4-b903-e010394393f1 · outbound

This paper cites https://eur-lex.europa.eu/eli/reg/2024/1689/oj.

Towards Provably Fair Machine Learning: Bayesian Approaches For Consistent and Transparent Predictions https://eur-lex.europa.eu/eli/reg/2024/1689/oj

Reference 9

Resolution: unresolved; no resolver link, observed 2026-06-27T09:55:35.961288Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 46481d5d-4c6e-4c3f-b505-f94b2906cf30 · outbound

This paper cites Bayesian Modelling of Intersectional Fairness: The Variance of Bias.

Towards Provably Fair Machine Learning: Bayesian Approaches For Consistent and Transparent Predictions Bayesian Modelling of Intersectional Fairness: The Variance of Bias

Reference 10

Resolution: verified exact; doi, observed 2026-06-27T10:00:49.097168Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation b52a6dbf-3fa2-4a3f-8e79-69d03f9cff04 · outbound

This paper cites Multicalibration: Calibration for the (computationally-identifiable) masses.

Towards Provably Fair Machine Learning: Bayesian Approaches For Consistent and Transparent Predictions Multicalibration: Calibration for the (computationally-identifiable) masses

Reference 11

Resolution: unresolved; no resolver link, observed 2026-06-27T09:55:35.961288Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2ef68d2d-1261-40b6-b215-07800e43f719 · outbound

This paper cites Canadian Journal of Statistics , author =.

Towards Provably Fair Machine Learning: Bayesian Approaches For Consistent and Transparent Predictions Canadian Journal of Statistics , author =

Reference 12

Resolution: metadata mismatch; doi, observed 2026-06-27T10:00:49.100054Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 1e375c1c-ea5b-4746-ada2-2b564c7332fa · outbound

This paper cites An upper bound for the box dimension of the hyperbolic dynamics via unstable pressure.

Towards Provably Fair Machine Learning: Bayesian Approaches For Consistent and Transparent Predictions An upper bound for the box dimension of the hyperbolic dynamics via unstable pressure

Reference 13

Resolution: verified exact; arxiv_id, observed 2026-07-03T10:37:56.847287Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 64c2062f-4a1f-4d3a-88cd-c3729ae508ef · outbound

This paper cites B ayes Factors.

Towards Provably Fair Machine Learning: Bayesian Approaches For Consistent and Transparent Predictions B ayes Factors

Reference 14

Resolution: verified exact; arxiv_id, observed 2026-06-27T10:00:49.091413Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 07a743d5-aaa2-433b-96a5-e889fff43dac · outbound

This paper cites Direct Exfoliation of Nanoribbons from Bulk van der Waals Crystals.

Towards Provably Fair Machine Learning: Bayesian Approaches For Consistent and Transparent Predictions Direct Exfoliation of Nanoribbons from Bulk van der Waals Crystals

Reference 15

Resolution: verified exact; arxiv_id, observed 2026-07-03T10:37:56.845202Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 35f8da03-9746-45f0-b2c1-073860125ea7 · outbound

This paper cites ACM Comput.

Towards Provably Fair Machine Learning: Bayesian Approaches For Consistent and Transparent Predictions ACM Comput

Reference 16

Resolution: metadata mismatch; doi, observed 2026-06-27T10:00:49.094271Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 7d097a05-8e93-41b2-9bbd-cd1ca89d74b2 · outbound

This paper cites Euclidean Affine Functions and Applications to Calendar Algorithms.

Towards Provably Fair Machine Learning: Bayesian Approaches For Consistent and Transparent Predictions Euclidean Affine Functions and Applications to Calendar Algorithms

Reference 17

Resolution: verified exact; arxiv_id, observed 2026-07-03T10:37:56.832486Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 838f8a33-a494-4c0b-98c4-56e9d0768948 · outbound

This paper cites The $(k,l)$-Euler theorem and the combinatorics of $(k,l)$-sequences.

Towards Provably Fair Machine Learning: Bayesian Approaches For Consistent and Transparent Predictions The $(k,l)$-Euler theorem and the combinatorics of $(k,l)$-sequences

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-07-03T10:37:56.827445Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 24adfe39-3928-4405-85eb-855420e8ca73 · outbound

This paper cites Agnostic Pointwise-Competitive Selective Classification.

Towards Provably Fair Machine Learning: Bayesian Approaches For Consistent and Transparent Predictions Agnostic Pointwise-Competitive Selective Classification

Reference 19

Resolution: verified exact; doi, observed 2026-06-27T10:00:49.103326Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 80dde5f0-5ba9-4621-b143-e4a4e3127833 · outbound

This paper cites Fair Classifiers that Abstain without Harm.

Towards Provably Fair Machine Learning: Bayesian Approaches For Consistent and Transparent Predictions Fair Classifiers that Abstain without Harm

Reference 20

Resolution: unresolved; no resolver link, observed 2026-06-27T09:55:35.961288Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6173e951-de0a-4703-b852-6c8532d4c98a · outbound

This paper cites Dynamics of certain Euler-Bernoulli rods and rings from a minimal coupling quantum isomorphism.

Towards Provably Fair Machine Learning: Bayesian Approaches For Consistent and Transparent Predictions Dynamics of certain Euler-Bernoulli rods and rings from a minimal coupling quantum isomorphism

Reference 22

Resolution: verified exact; arxiv_id, observed 2026-07-03T10:37:56.824496Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 313cc613-29d5-4808-b20a-42b91dc5cf28 · outbound

This paper cites This lets us pick between pool members using the richer 𝑣 node evidence, which cannot be expressed as a linear MIP objective.

Towards Provably Fair Machine Learning: Bayesian Approaches For Consistent and Transparent Predictions This lets us pick between pool members using the richer 𝑣 node evidence, which cannot be expressed as a linear MIP objective

Reference 23

Resolution: unresolved; no resolver link, observed 2026-06-27T09:55:35.961288Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 036d0e24-9f41-4eb5-aba4-9f471748aadd · outbound

This paper cites Proportional Multi-Calibrator (PMC) The PMC post-processor is applied to a logistic regression base learner (LogisticRegression, 𝐶= 1.0,max_iter= 1000).

Towards Provably Fair Machine Learning: Bayesian Approaches For Consistent and Transparent Predictions Proportional Multi-Calibrator (PMC) The PMC post-processor is applied to a logistic regression base learner (LogisticRegression, 𝐶= 1.0,max_iter= 1000)

Reference 24

Resolution: unresolved; no resolver link, observed 2026-06-27T09:55:35.961288Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f39b5467-141d-46a3-99fe-cc6fe4ff9c1a · outbound

This paper cites cherry-picked.

Towards Provably Fair Machine Learning: Bayesian Approaches For Consistent and Transparent Predictions cherry-picked

Reference 25

Resolution: unresolved; no resolver link, observed 2026-06-27T09:55:35.961288Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.