Data Preparation for Fairness-Performance Trade-Offs: A Practitioner-Friendly Alternative? | Paper Citation Record

Observation 9ecdb17e-b4d5-4545-b4b0-50641ef5c8b3 · outbound

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

Data Preparation for Fairness-Performance Trade-Offs: A Practitioner-Friendly Alternative? A survey on bias and fairness in machine learning,

Reference 1

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Observation 3904fff6-f16f-4932-8e19-41642c376120 · outbound

This paper cites Bias and unfairness in machine learning models: a systematic review on datasets, tools, fairness metrics, and identification and mitigation methods,.

Data Preparation for Fairness-Performance Trade-Offs: A Practitioner-Friendly Alternative? Bias and unfairness in machine learning models: a systematic review on datasets, tools, fairness metrics, and identification and mitigation methods,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:01:36.416453Z

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Observation f5782fc7-223f-429d-a260-7bf410d99b2b · outbound

This paper cites A review on fairness in machine learning,.

Data Preparation for Fairness-Performance Trade-Offs: A Practitioner-Friendly Alternative? A review on fairness in machine learning,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:01:36.387133Z

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Observation 8ce2a8ea-90b7-43ab-9dc9-8a01e3528765 · outbound

This paper cites Machine learning, ethics and law,.

Data Preparation for Fairness-Performance Trade-Offs: A Practitioner-Friendly Alternative? Machine learning, ethics and law,

Reference 4

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This paper cites Bias mitigation for machine learning classifiers: A comprehensive survey,.

Data Preparation for Fairness-Performance Trade-Offs: A Practitioner-Friendly Alternative? Bias mitigation for machine learning classifiers: A comprehensive survey,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:01:36.350047Z

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Observation b3ac1b04-83fd-4f82-a119-634f293d645d · outbound

This paper cites Bias in machine learning software: why? how? what to do?.

Data Preparation for Fairness-Performance Trade-Offs: A Practitioner-Friendly Alternative? Bias in machine learning software: why? how? what to do?

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:01:36.327268Z

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Observation 36cbdd09-8236-42b5-8413-4a66c6b4a91f · outbound

This paper cites Training data debugging for the fairness of machine learning software,.

Data Preparation for Fairness-Performance Trade-Offs: A Practitioner-Friendly Alternative? Training data debugging for the fairness of machine learning software,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:01:36.297886Z

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Observation 32ed7949-c79c-49ad-9f61-dbb546e0c5e5 · outbound

This paper cites Fairness testing: testing software for discrimination,.

Data Preparation for Fairness-Performance Trade-Offs: A Practitioner-Friendly Alternative? Fairness testing: testing software for discrimination,

Reference 8

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This paper cites Burkov, Machine learning engineering.

Data Preparation for Fairness-Performance Trade-Offs: A Practitioner-Friendly Alternative? Burkov, Machine learning engineering

Reference 9

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This paper cites Fair preprocessing: Towards understand- ing compositional fairness of data transformers in machine learning pipeline,.

Data Preparation for Fairness-Performance Trade-Offs: A Practitioner-Friendly Alternative? Fair preprocessing: Towards understand- ing compositional fairness of data transformers in machine learning pipeline,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:01:36.240080Z

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This paper cites The impact of data prepa- ration on the fairness of software systems.

Data Preparation for Fairness-Performance Trade-Offs: A Practitioner-Friendly Alternative? The impact of data prepa- ration on the fairness of software systems

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:01:36.221163Z

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This paper cites Data preprocessing techniques for classi- fication without discrimination,.

Data Preparation for Fairness-Performance Trade-Offs: A Practitioner-Friendly Alternative? Data preprocessing techniques for classi- fication without discrimination,

Reference 12

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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This paper cites Certifying and removing disparate impact,.

Data Preparation for Fairness-Performance Trade-Offs: A Practitioner-Friendly Alternative? Certifying and removing disparate impact,

Reference 13

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This paper cites Exploring how machine learning practitioners (try to) use fairness toolkits,.

Data Preparation for Fairness-Performance Trade-Offs: A Practitioner-Friendly Alternative? Exploring how machine learning practitioners (try to) use fairness toolkits,

Reference 14

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Source-reported events for the cited work

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Observation ae834d17-28d3-4011-87d2-510f8f9506c1 · outbound

This paper cites The landscape and gaps in open source fairness toolkits,.

Data Preparation for Fairness-Performance Trade-Offs: A Practitioner-Friendly Alternative? The landscape and gaps in open source fairness toolkits,

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-11T11:01:34.880423Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 759c7d84-1e46-4987-9035-d9aa4982039e · outbound

This paper cites A Catalog of Fairness-Aware Practices in Machine Learning Engineering.

Data Preparation for Fairness-Performance Trade-Offs: A Practitioner-Friendly Alternative? A Catalog of Fairness-Aware Practices in Machine Learning Engineering

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-11T11:01:34.886209Z

Source-reported events for the cited work

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Observation 113c7bc3-4229-4a51-b4fe-c6f88ef42ad9 · outbound

This paper cites Fairness-aware practices from developers’ perspective: A survey,.

Data Preparation for Fairness-Performance Trade-Offs: A Practitioner-Friendly Alternative? Fairness-aware practices from developers’ perspective: A survey,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:01:36.166801Z

Source-reported events for the cited work

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This paper cites Raina and S.

Data Preparation for Fairness-Performance Trade-Offs: A Practitioner-Friendly Alternative? Raina and S

Reference 18

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Observation 423a2d79-e46c-4ead-a64a-8e30286bebc3 · outbound

This paper cites Fairness per- ceptions of algorithmic decision-making: A systematic review of the empirical literature.

Data Preparation for Fairness-Performance Trade-Offs: A Practitioner-Friendly Alternative? Fairness per- ceptions of algorithmic decision-making: A systematic review of the empirical literature

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:01:36.149403Z

Source-reported events for the cited work

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This paper cites Fairness improvement with multiple protected attributes: How far are we?.

Data Preparation for Fairness-Performance Trade-Offs: A Practitioner-Friendly Alternative? Fairness improvement with multiple protected attributes: How far are we?

Reference 20

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 770a8f8d-0c47-47f2-b098-922208264ab5 · outbound

This paper cites Software fairness,.

Data Preparation for Fairness-Performance Trade-Offs: A Practitioner-Friendly Alternative? Software fairness,

Reference 21

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Source-reported events for the cited work

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Observation 6af9de04-ff4a-4598-ba7a-ddcf1e62ecd0 · outbound

This paper cites Ai ethics issues in real world: Evidence from ai incident database,.

Data Preparation for Fairness-Performance Trade-Offs: A Practitioner-Friendly Alternative? Ai ethics issues in real world: Evidence from ai incident database,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:01:36.092903Z

Source-reported events for the cited work

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Observation 61f9acba-537b-4dc4-8e36-e2957c8390ed · outbound

This paper cites Mitigating unwanted biases with adversarial learning,.

Data Preparation for Fairness-Performance Trade-Offs: A Practitioner-Friendly Alternative? Mitigating unwanted biases with adversarial learning,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:01:36.069555Z

Source-reported events for the cited work

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Observation bfc388e5-1884-4495-923d-c4071b2d3d39 · outbound

This paper cites Fairway: a way to build fair ml software,.

Data Preparation for Fairness-Performance Trade-Offs: A Practitioner-Friendly Alternative? Fairway: a way to build fair ml software,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:01:36.048737Z

Source-reported events for the cited work

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Observation cbb14abb-ade6-45a9-8f33-f1149e2aee39 · outbound

This paper cites Automated directed fairness testing,.

Data Preparation for Fairness-Performance Trade-Offs: A Practitioner-Friendly Alternative? Automated directed fairness testing,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:01:36.032110Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 8947cd92-5717-4f81-ac2d-e89efd39daa5 · outbound

This paper cites Black box fairness testing of machine learning models,.

Data Preparation for Fairness-Performance Trade-Offs: A Practitioner-Friendly Alternative? Black box fairness testing of machine learning models,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:01:36.013006Z

Source-reported events for the cited work

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Observation eb4833b3-681b-4c9e-acbf-c2f90ef77c0e · outbound

This paper cites White-box fairness testing through adversarial sampling,.

Data Preparation for Fairness-Performance Trade-Offs: A Practitioner-Friendly Alternative? White-box fairness testing through adversarial sampling,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:01:35.988681Z

Source-reported events for the cited work

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Observation afe4d948-5aff-4048-b74a-50db606e0d1e · outbound

This paper cites Preprocessing matters: Automated pipeline selection for fair classification,.

Data Preparation for Fairness-Performance Trade-Offs: A Practitioner-Friendly Alternative? Preprocessing matters: Automated pipeline selection for fair classification,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:01:35.971705Z

Source-reported events for the cited work

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Observation fba48a0b-ab38-49b4-a310-afd1a0712dd8 · outbound

This paper cites Fair enough: Searching for sufficient measures of fairness,.

Data Preparation for Fairness-Performance Trade-Offs: A Practitioner-Friendly Alternative? Fair enough: Searching for sufficient measures of fairness,

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:01:35.947936Z

Source-reported events for the cited work

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This paper cites A genetic algorithm tutorial,.

Data Preparation for Fairness-Performance Trade-Offs: A Practitioner-Friendly Alternative? A genetic algorithm tutorial,

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Source-reported events for the cited work

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This paper cites Choosing mutation and crossover ratios for genetic algorithms—a review with a new dynamic approach,.

Data Preparation for Fairness-Performance Trade-Offs: A Practitioner-Friendly Alternative? Choosing mutation and crossover ratios for genetic algorithms—a review with a new dynamic approach,

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:01:35.909553Z

Source-reported events for the cited work

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Observation 65146052-84ee-4579-8e6f-939a6d5b9a81 · outbound

This paper cites Fae: A fairness-aware ensemble framework.

Data Preparation for Fairness-Performance Trade-Offs: A Practitioner-Friendly Alternative? Fae: A fairness-aware ensemble framework

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:01:35.874934Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 874ddea6-c3a5-4c54-944d-38a68958e23a · outbound

This paper cites Towards explaining the effects of data preprocess- ing on machine learning.

Data Preparation for Fairness-Performance Trade-Offs: A Practitioner-Friendly Alternative? Towards explaining the effects of data preprocess- ing on machine learning

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:01:35.855635Z

Source-reported events for the cited work

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Observation 34f384b0-0bc6-4151-9ffe-367347edcb5d · outbound

This paper cites Area under the precision-recall curve: Point estimates and confidence intervals,.

Data Preparation for Fairness-Performance Trade-Offs: A Practitioner-Friendly Alternative? Area under the precision-recall curve: Point estimates and confidence intervals,

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Source-reported events for the cited work

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This paper cites A few useful things to know about machine learning,.

Data Preparation for Fairness-Performance Trade-Offs: A Practitioner-Friendly Alternative? A few useful things to know about machine learning,

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Resolution: verified exact; doi, observed 2026-08-11T11:01:35.181809Z

Source-reported events for the cited work

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Observation 4c078f1f-68c6-4d8e-94ed-33d4f99161d1 · outbound

This paper cites The precision-recall plot is more informa- tive than the roc plot when evaluating binary classifiers on imbalanced datasets,.

Data Preparation for Fairness-Performance Trade-Offs: A Practitioner-Friendly Alternative? The precision-recall plot is more informa- tive than the roc plot when evaluating binary classifiers on imbalanced datasets,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:01:35.816854Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 5d3cf975-16f4-4ffc-b334-be3e2f7e0b22 · outbound

This paper cites A reductions approach to fair classification,.

Data Preparation for Fairness-Performance Trade-Offs: A Practitioner-Friendly Alternative? A reductions approach to fair classification,

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:01:35.798172Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 7c7e14fb-7914-45fb-96ab-25b44c50e673 · outbound

This paper cites Equality of opportunity in supervised learning,.

Data Preparation for Fairness-Performance Trade-Offs: A Practitioner-Friendly Alternative? Equality of opportunity in supervised learning,

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:01:35.775959Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 6b9cae7c-d07c-46b9-bdf2-7cb94470492a · outbound

This paper cites An ontology for fairness metrics,.

Data Preparation for Fairness-Performance Trade-Offs: A Practitioner-Friendly Alternative? An ontology for fairness metrics,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:01:35.757056Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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This paper cites Fairness-aware machine learning engineering: how far are we?.

Data Preparation for Fairness-Performance Trade-Offs: A Practitioner-Friendly Alternative? Fairness-aware machine learning engineering: how far are we?

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:01:35.740323Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation f85b5ecf-44a7-4b6f-857c-a446ea52b2ef · outbound

This paper cites Fairmask: Better fairness via model-based rebalancing of protected attributes,.

Data Preparation for Fairness-Performance Trade-Offs: A Practitioner-Friendly Alternative? Fairmask: Better fairness via model-based rebalancing of protected attributes,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:01:35.719350Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 3bbbf472-b47d-4cbd-aab8-8bbed6e70360 · outbound

This paper cites Machine learning and data cleaning: Which serves the other?.

Data Preparation for Fairness-Performance Trade-Offs: A Practitioner-Friendly Alternative? Machine learning and data cleaning: Which serves the other?

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:01:35.702333Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation c475d22d-e3fc-4315-a390-a7d66c0b9210 · outbound

This paper cites When correla- tion clustering meets fairness constraints,.

Data Preparation for Fairness-Performance Trade-Offs: A Practitioner-Friendly Alternative? When correla- tion clustering meets fairness constraints,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:01:35.683841Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 5f077248-0365-49a1-8fb6-c1a6c338aa36 · outbound

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

Data Preparation for Fairness-Performance Trade-Offs: A Practitioner-Friendly Alternative? Fairness in algorithmic decision making: An excursion through the lens of causality

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Resolution: unresolved; no resolver link, observed 2026-08-11T11:01:35.037815Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5944ff94-86d2-4d03-aba9-7a5711264269 · outbound

This paper cites Algorithmic fairness datasets: the story so far,.

Data Preparation for Fairness-Performance Trade-Offs: A Practitioner-Friendly Alternative? Algorithmic fairness datasets: the story so far,

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Resolution: malformed identifier; raw_fallback, observed 2026-08-11T11:01:35.661813Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation cf133577-aab3-463d-9b04-f9238c91dd5b · outbound

This paper cites Ex- amining the impact of bias mitigation algorithms on the sustainability of ml-enabled systems: A benchmark study,.

Data Preparation for Fairness-Performance Trade-Offs: A Practitioner-Friendly Alternative? Ex- amining the impact of bias mitigation algorithms on the sustainability of ml-enabled systems: A benchmark study,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:01:35.640576Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation a4869380-27d5-4cc1-b5e0-a5fcc806d624 · outbound

This paper cites Statlog (German Credit Data),.

Data Preparation for Fairness-Performance Trade-Offs: A Practitioner-Friendly Alternative? Statlog (German Credit Data),

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-11T11:01:35.055558Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c5e9101b-7190-4556-ad28-0d4b164a67a3 · outbound

This paper cites Heart Disease,.

Data Preparation for Fairness-Performance Trade-Offs: A Practitioner-Friendly Alternative? Heart Disease,

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-11T11:01:35.060731Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dae53319-2201-4967-ac8c-f59a34aebe08 · outbound

This paper cites Becker and R.

Data Preparation for Fairness-Performance Trade-Offs: A Practitioner-Friendly Alternative? Becker and R

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Resolution: unresolved; no resolver link, observed 2026-08-11T11:01:35.065806Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4b6f00d9-6bf5-40d9-9b39-effe4a0f0877 · outbound

This paper cites Bias mitigation for machine learning classifiers: A comprehensive survey,.

Data Preparation for Fairness-Performance Trade-Offs: A Practitioner-Friendly Alternative? Bias mitigation for machine learning classifiers: A comprehensive survey,

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-11T11:01:35.071211Z

Source-reported events for the cited work

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Observation 9eed38c6-67f8-44d4-bb92-ab4746422501 · outbound

This paper cites Smote: synthetic minority over-sampling technique,.

Data Preparation for Fairness-Performance Trade-Offs: A Practitioner-Friendly Alternative? Smote: synthetic minority over-sampling technique,

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:01:35.621933Z

Source-reported events for the cited work

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Observation 266093bc-44dc-4d4d-9ef2-592383b844ba · outbound

This paper cites an unresolved cited work.

Data Preparation for Fairness-Performance Trade-Offs: A Practitioner-Friendly Alternative? Unresolved cited work

Reference 52

Resolution: unresolved; raw_fallback, observed 2026-08-11T11:01:35.603162Z

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This paper cites A critique and improvement of the cl common language effect size statistics of mcgraw and wong,.

Data Preparation for Fairness-Performance Trade-Offs: A Practitioner-Friendly Alternative? A critique and improvement of the cl common language effect size statistics of mcgraw and wong,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:01:35.585110Z

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