Paper Citation Record · LEDGER

Understanding Bias in Machine Learning

As of 17 August 2026, this Paper Citation Record lists 21 of 21 outbound references and 1 inbound Pith citation observation for arXiv:1909.01866.

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

pith.paper-citation-record.v1
1909.01866 v1

Coverage vector

measured 21 of 21 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-14T05:36:58.269335Z

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 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-01T22:34:43.484713Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

21 of 21 outbound references displayed

verified exact0
verified fuzzy17
unresolved4
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 212dbf4a-d0ba-45eb-b92b-13d17e04e501 · outbound

This paper cites Parallel coordinates: a tool for visualizing multi-dimensional geometry.

Understanding Bias in Machine Learning Parallel coordinates: a tool for visualizing multi-dimensional geometry

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:36:58.574955Z

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 834e7548-f10e-4491-9e7e-9cfb87eb7b62 · outbound

This paper cites http://heatmapping.org/.

Understanding Bias in Machine Learning http://heatmapping.org/

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:36:58.558660Z

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 0677a30e-eae5-4425-8f2d-7b3e9ceb1dfa · outbound

This paper cites On pixel-wise explanations for non-linear classiﬁer decisions by layer-wise relevance propagation.

Understanding Bias in Machine Learning On pixel-wise explanations for non-linear classiﬁer decisions by layer-wise relevance propagation

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:36:58.540946Z

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 edd001ce-6f57-495c-80cc-37a8ae3edcba · outbound

This paper cites Gender shades: Intersectional accuracy dispar- ities in commercial gender classiﬁcation.

Understanding Bias in Machine Learning Gender shades: Intersectional accuracy dispar- ities in commercial gender classiﬁcation

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:36:58.525142Z

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 7928eaf7-fa33-46d9-bb9d-83fe9d8d9e71 · outbound

This paper cites Man is to computer programmer as woman is to homemaker? debiasing word embeddings.

Understanding Bias in Machine Learning Man is to computer programmer as woman is to homemaker? debiasing word embeddings

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:36:58.511424Z

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 60fbca1b-e4f2-4387-b567-333b6d01e1da · outbound

This paper cites Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraints.

Understanding Bias in Machine Learning Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraints

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-14T05:36:58.198485Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a247a9fa-96e6-4797-8253-9cab4a1c0066 · outbound

This paper cites Unbiased look at dataset bias.

Understanding Bias in Machine Learning Unbiased look at dataset bias

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:36:58.498912Z

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 0474577c-25ec-444e-a4f7-54850840a095 · outbound

This paper cites Undoing the damage of dataset bias.

Understanding Bias in Machine Learning Undoing the damage of dataset bias

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:36:58.485503Z

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 45b9daf2-8ddd-46d4-a52c-087885a1edf2 · outbound

This paper cites A deeper look at dataset bias.

Understanding Bias in Machine Learning A deeper look at dataset bias

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:36:58.471544Z

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 d8f44ad6-d922-46dd-a958-a86ccb1754d1 · outbound

This paper cites Why should i trust you?: Explaining the predictions of any classiﬁer.

Understanding Bias in Machine Learning Why should i trust you?: Explaining the predictions of any classiﬁer

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:36:58.458319Z

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 ac8ea46d-0c8d-46f3-a0f1-ae911be7b9d8 · outbound

This paper cites Causal interpretations of black-box models.

Understanding Bias in Machine Learning Causal interpretations of black-box models

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:36:58.442712Z

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 b8f42888-6c0b-43b2-aca1-56150d9fc142 · outbound

This paper cites Permutation importance: a corrected feature importance measure.

Understanding Bias in Machine Learning Permutation importance: a corrected feature importance measure

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:36:58.429671Z

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 ce5cae3c-918d-45cf-8d40-d2d5d09e1781 · outbound

This paper cites Visualizing and understanding convolutional networks.

Understanding Bias in Machine Learning Visualizing and understanding convolutional networks

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:36:58.416292Z

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 d207dee9-15d0-4eb4-b695-587109da209f · outbound

This paper cites Visualizing Deep Neural Network Decisions: Prediction Difference Analysis.

Understanding Bias in Machine Learning Visualizing Deep Neural Network Decisions: Prediction Difference Analysis

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-14T05:36:58.235942Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c9cbcc27-4783-4065-9c99-d1343587c5f9 · outbound

This paper cites Deep inside convo- lutional networks: Visualising image classiﬁcation models and saliency maps.

Understanding Bias in Machine Learning Deep inside convo- lutional networks: Visualising image classiﬁcation models and saliency maps

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:36:58.403272Z

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 6c0be015-8101-4f94-a588-3d3dbf37ead3 · outbound

This paper cites Ried- miller.

Understanding Bias in Machine Learning Ried- miller

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:36:58.389582Z

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 f3bbfd7e-f769-4a3c-98e9-c0747efbaf89 · outbound

This paper cites Axiomatic attribution for deep networks.

Understanding Bias in Machine Learning Axiomatic attribution for deep networks

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-14T05:36:58.251094Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 84c4bb6c-3803-4acd-a894-c1a2600dbc3d · outbound

This paper cites SmoothGrad: removing noise by adding noise.

Understanding Bias in Machine Learning SmoothGrad: removing noise by adding noise

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-14T05:36:58.254867Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b7321ddd-2fef-4ce5-9b65-ed8929e81a0b · outbound

This paper cites Grad-cam: Visual explanations from deep networks via gradient-based localization.

Understanding Bias in Machine Learning Grad-cam: Visual explanations from deep networks via gradient-based localization

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:36:58.369741Z

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 1cf91135-27c1-4db6-b467-4fd8e3e07c6c · outbound

This paper cites Learning important features through propagating activation diﬀerences.

Understanding Bias in Machine Learning Learning important features through propagating activation diﬀerences

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:36:58.356625Z

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 a7ffc7cd-74cb-4d29-a072-7acca05eb5c9 · outbound

This paper cites Understanding individual decisions of cnns via contrastive backpropagation.

Understanding Bias in Machine Learning Understanding individual decisions of cnns via contrastive backpropagation

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:36:58.343790Z

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

Observation d8b6ed32-fdd2-425a-904a-afaf9bb5cb8e · inbound

The CRAFT principles for the responsible use of large language models in policymaking cites this paper.

The CRAFT principles for the responsible use of large language models in policymaking Understanding Bias in Machine Learning

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-01T22:34:43.484713Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.