CaTE Data Curation for Trustworthy AI | Paper Citation Record

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This paper cites URL: https://c2pa.org/specifications/ specifications/1.3/guidance/Guidance.html#1.4@specs:C2PA_Specification.adoc.

CaTE Data Curation for Trustworthy AI URL: https://c2pa.org/specifications/ specifications/1.3/guidance/Guidance.html#1.4@specs:C2PA_Specification.adoc

Reference 1

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This paper cites URL: https://dataandtrustalliance.org/.

CaTE Data Curation for Trustworthy AI URL: https://dataandtrustalliance.org/

Reference 2

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This paper cites URL: https://developers.google.com/ looker-studio/connector/semantics.

CaTE Data Curation for Trustworthy AI URL: https://developers.google.com/ looker-studio/connector/semantics

Reference 3

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This paper cites FA*IR: A Fair Top-k Ranking Algorithm.

CaTE Data Curation for Trustworthy AI FA*IR: A Fair Top-k Ranking Algorithm

Reference 4

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CaTE Data Curation for Trustworthy AI Unresolved cited work

Reference 5

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This paper cites URL: https://www.media.mit.edu/projects/data-p rovenance-for-ai/overview/.

CaTE Data Curation for Trustworthy AI URL: https://www.media.mit.edu/projects/data-p rovenance-for-ai/overview/

Reference 6

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This paper cites URL: https://www.w3.org/TR/prov-o/.

CaTE Data Curation for Trustworthy AI URL: https://www.w3.org/TR/prov-o/

Reference 7

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This paper cites URL: https://rdflib.readthedocs.io/en/stable/#.

CaTE Data Curation for Trustworthy AI URL: https://rdflib.readthedocs.io/en/stable/#

Reference 8

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This paper cites metrics and scoring.

CaTE Data Curation for Trustworthy AI metrics and scoring

Reference 9

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Observation 423f537c-cdac-4bb6-83f2-023484ade796 · outbound

This paper cites The Academy of Management Review , 20(3):709–734, 1995.

CaTE Data Curation for Trustworthy AI The Academy of Management Review , 20(3):709–734, 1995

Reference 10

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This paper cites Technical report, United States Department of Defense, 2018.

CaTE Data Curation for Trustworthy AI Technical report, United States Department of Defense, 2018

Reference 11

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This paper cites Auxiliary information: the raking-ratio empirical process.

CaTE Data Curation for Trustworthy AI Auxiliary information: the raking-ratio empirical process

Reference 12

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This paper cites Why resampling outperforms reweighting for correcting sampling bias with stochastic gradients.

CaTE Data Curation for Trustworthy AI Why resampling outperforms reweighting for correcting sampling bias with stochastic gradients

Reference 13

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Observation 505a24f2-2f97-44b8-8e78-f972e5fc8a65 · outbound

This paper cites An approach for classification of highly imbalanced data using weighting and undersampling.

CaTE Data Curation for Trustworthy AI An approach for classification of highly imbalanced data using weighting and undersampling

Reference 14

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This paper cites The Impossibility of Fair LLMs.

CaTE Data Curation for Trustworthy AI The Impossibility of Fair LLMs

Reference 15

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This paper cites O’Connor, and Kevin McGuinness.

CaTE Data Curation for Trustworthy AI O’Connor, and Kevin McGuinness

Reference 16

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This paper cites Data catalog vocabulary (dcat) (w3c recommendation).

CaTE Data Curation for Trustworthy AI Data catalog vocabulary (dcat) (w3c recommendation)

Reference 17

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This paper cites Wefe: The word embeddings fairness eval- uation framework.

CaTE Data Curation for Trustworthy AI Wefe: The word embeddings fairness eval- uation framework

Reference 18

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This paper cites Optimal representative sample weighting.

CaTE Data Curation for Trustworthy AI Optimal representative sample weighting

Reference 19

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This paper cites Practical considerations in raking survey data.

CaTE Data Curation for Trustworthy AI Practical considerations in raking survey data

Reference 20

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This paper cites Semantic data types.

CaTE Data Curation for Trustworthy AI Semantic data types

Reference 21

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This paper cites AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Bias.

CaTE Data Curation for Trustworthy AI AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Bias

Reference 22

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This paper cites Recovering from Biased Data: Can Fairness Constraints Improve Accuracy?.

CaTE Data Curation for Trustworthy AI Recovering from Biased Data: Can Fairness Constraints Improve Accuracy?

Reference 23

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This paper cites Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings.

CaTE Data Curation for Trustworthy AI Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings

Reference 24

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This paper cites Identifying problems with raking estimators.

CaTE Data Curation for Trustworthy AI Identifying problems with raking estimators

Reference 25

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This paper cites What is the effect of importance weighting in deep learning? In International Conference on Machine Learning , 2018.

CaTE Data Curation for Trustworthy AI What is the effect of importance weighting in deep learning? In International Conference on Machine Learning , 2018

Reference 26

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This paper cites Bryson, and Arvind Narayanan.

CaTE Data Curation for Trustworthy AI Bryson, and Arvind Narayanan

Reference 27

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This paper cites A Decision-driven Methodology for Designing Uncertainty-aware AI Self-Assessment.

CaTE Data Curation for Trustworthy AI A Decision-driven Methodology for Designing Uncertainty-aware AI Self-Assessment

Reference 28

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This paper cites Fairness with adaptive weights.

CaTE Data Curation for Trustworthy AI Fairness with adaptive weights

Reference 29

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This paper cites Yu, Qiang Yang, and Xing Xie.

CaTE Data Curation for Trustworthy AI Yu, Qiang Yang, and Xing Xie

Reference 30

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CaTE Data Curation for Trustworthy AI Unresolved cited work

Reference 31

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This paper cites Unleashing the potential of prompt engineering for large language models.

CaTE Data Curation for Trustworthy AI Unleashing the potential of prompt engineering for large language models

Reference 32

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This paper cites OoDAnalyzer: Interactive Analysis of Out-of-Distribution Samples.

CaTE Data Curation for Trustworthy AI OoDAnalyzer: Interactive Analysis of Out-of-Distribution Samples

Reference 33

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CaTE Data Curation for Trustworthy AI Xgboost: A scalable tree boosting system

Reference 34

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This paper cites Semantic Type Detection in Tabular Data via Machine Learning Using Semi-synthetic Data.

CaTE Data Curation for Trustworthy AI Semantic Type Detection in Tabular Data via Machine Learning Using Semi-synthetic Data

Reference 35

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CaTE Data Curation for Trustworthy AI Jordan, Joseph E

Reference 36

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This paper cites Eleven quick tips for data cleaning and feature engineering.

CaTE Data Curation for Trustworthy AI Eleven quick tips for data cleaning and feature engineering

Reference 37

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This paper cites Statistical functions (scipy.stats) — scipy v1.13.0 manual.

CaTE Data Curation for Trustworthy AI Statistical functions (scipy.stats) — scipy v1.13.0 manual

Reference 38

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CaTE Data Curation for Trustworthy AI On a least squares adjustment of a sampled frequency table when the expected marginal totals are known

Reference 39

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CaTE Data Curation for Trustworthy AI Calibration estimators in survey sampling

Reference 40

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CaTE Data Curation for Trustworthy AI Bert: Pre-training of deep bidirectional transformers for language understanding

Reference 41

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CaTE Data Curation for Trustworthy AI Multi- dimensional gender bias classification

Reference 42

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CaTE Data Curation for Trustworthy AI A survey on in-context learning

Reference 43

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This paper cites Breaking the bias: Gender fairness in llms using prompt engineering and in-context learning.

CaTE Data Curation for Trustworthy AI Breaking the bias: Gender fairness in llms using prompt engineering and in-context learning

Reference 44

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This paper cites To SMOTE, or not to SMOTE?.

CaTE Data Curation for Trustworthy AI To SMOTE, or not to SMOTE?

Reference 45

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CaTE Data Curation for Trustworthy AI Unresolved cited work

Reference 46

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CaTE Data Curation for Trustworthy AI Model-agnostic meta-learning for fast adaptation of deep networks

Reference 47

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CaTE Data Curation for Trustworthy AI Classification in the presence of label noise: a survey

Reference 48

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CaTE Data Curation for Trustworthy AI DebIE: A Platform for Implicit and Explicit Debiasing of Word Embedding Spaces

Reference 49

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This paper cites Explainable disparity compensation for efficient fair ranking.

CaTE Data Curation for Trustworthy AI Explainable disparity compensation for efficient fair ranking

Reference 50

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CaTE Data Curation for Trustworthy AI Retrieval-Augmented Generation for Large Language Models: A Survey

Reference 51

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CaTE Data Curation for Trustworthy AI New Oversampling Approaches Based on Polynomial Fitting for Imbalanced Data Sets

Reference 52

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CaTE Data Curation for Trustworthy AI Grammar-constrained decoding for structured NLP tasks without finetuning

Reference 53

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This paper cites A Distributional Framework For Data Valuation.

CaTE Data Curation for Trustworthy AI A Distributional Framework For Data Valuation

Reference 54

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CaTE Data Curation for Trustworthy AI Data Shapley: Equitable Valuation of Data for Machine Learning

Reference 55

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CaTE Data Curation for Trustworthy AI A primer on neural network models for natural language processing

Reference 56

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CaTE Data Curation for Trustworthy AI Neural network methods for natural language processing

Reference 57

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This paper cites Lipstick on a pig: Debiasing methods cover up systematic gender biases in word embeddings but do not remove them.

CaTE Data Curation for Trustworthy AI Lipstick on a pig: Debiasing methods cover up systematic gender biases in word embeddings but do not remove them

Reference 58

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This paper cites Automated data cleaning can hurt fairness in machine learning-based decision making.

CaTE Data Curation for Trustworthy AI Automated data cleaning can hurt fairness in machine learning-based decision making

Reference 59

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CaTE Data Curation for Trustworthy AI Unresolved cited work

Reference 60

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This paper cites Achieving Counterfactual Fairness for Anomaly Detection, volume 13935, page 55–66.

CaTE Data Curation for Trustworthy AI Achieving Counterfactual Fairness for Anomaly Detection, volume 13935, page 55–66

Reference 61

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This paper cites Distributional structure.

CaTE Data Curation for Trustworthy AI Distributional structure

Reference 62

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CaTE Data Curation for Trustworthy AI Discriminative machine learning for maximal representative subsampling

Reference 63

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This paper cites Identification of outliers , volume 11.

CaTE Data Curation for Trustworthy AI Identification of outliers , volume 11

Reference 64

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This paper cites Garcia, and Shutao Li.

CaTE Data Curation for Trustworthy AI Garcia, and Shutao Li

Reference 65

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CaTE Data Curation for Trustworthy AI Deep residual learning for image recogni- tion

Reference 66

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CaTE Data Curation for Trustworthy AI Understanding machine learning practitioners’ data documentation perceptions, needs, challenges, and desiderata

Reference 67

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This paper cites Some reflections on innovation and invention.

CaTE Data Curation for Trustworthy AI Some reflections on innovation and invention

Reference 68

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This paper cites Machine Learning Algorithms, pages 117–259.

CaTE Data Curation for Trustworthy AI Machine Learning Algorithms, pages 117–259

Reference 69

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CaTE Data Curation for Trustworthy AI Hacking with npos: collaborative analytics and broker roles in civic data hackathons

Reference 70

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CaTE Data Curation for Trustworthy AI Parameter-efficient transfer learning for NLP

Reference 71

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This paper cites As biased as you measure: Methodological pitfalls of bias evaluations in speaker verification research.

CaTE Data Curation for Trustworthy AI As biased as you measure: Methodological pitfalls of bias evaluations in speaker verification research

Reference 72

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CaTE Data Curation for Trustworthy AI Unresolved cited work

Reference 73

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CaTE Data Curation for Trustworthy AI Identifying and Correcting Label Bias in Machine Learning, January

Reference 74

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CaTE Data Curation for Trustworthy AI Johnson and Taghi M

Reference 75

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This paper cites SPlit: An Optimal Method for Data Splitting.

CaTE Data Curation for Trustworthy AI SPlit: An Optimal Method for Data Splitting

Reference 76

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CaTE Data Curation for Trustworthy AI Date and time format—iso 8601

Reference 77

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CaTE Data Curation for Trustworthy AI Debiasing pre-trained contextualised embeddings

Reference 78

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CaTE Data Curation for Trustworthy AI Hauptmann

Reference 79

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This paper cites Prompting large language models with audio for general-purpose speech summarization.

CaTE Data Curation for Trustworthy AI Prompting large language models with audio for general-purpose speech summarization

Reference 80

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CaTE Data Curation for Trustworthy AI Preventing catastrophic forgetting in continual learning of new natural language tasks

Reference 81

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CaTE Data Curation for Trustworthy AI Learning fair classifiers with partially annotated group labels

Reference 82

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CaTE Data Curation for Trustworthy AI A Survey of Domain Knowledge Elicitation in Applied Machine Learning

Reference 83

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This paper cites Towards Fair RAG: On the Impact of Fair Ranking in Retrieval-Augmented Generation.

CaTE Data Curation for Trustworthy AI Towards Fair RAG: On the Impact of Fair Ranking in Retrieval-Augmented Generation

Reference 84

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CaTE Data Curation for Trustworthy AI A short survey on importance weighting for machine learning,

Reference 85

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CaTE Data Curation for Trustworthy AI WILDS: A Benchmark of in-the-Wild Distribution Shifts

Reference 86

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CaTE Data Curation for Trustworthy AI Fleischmann, and William A

Reference 87

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Observation d0e14673-4cd9-477a-ab5a-91ca735665a4 · outbound

This paper cites Aggregating human domain knowledge for feature ranking.

CaTE Data Curation for Trustworthy AI Aggregating human domain knowledge for feature ranking

Reference 88

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This paper cites Adaptive Sensitive Reweighting to Mitigate Bias in Fairness-aware Classification.

CaTE Data Curation for Trustworthy AI Adaptive Sensitive Reweighting to Mitigate Bias in Fairness-aware Classification

Reference 89

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CaTE Data Curation for Trustworthy AI Kub´ at and S

Reference 90

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CaTE Data Curation for Trustworthy AI Gummadi, and Gerhard Weikum

Reference 91

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Observation 72ffedaa-7f8f-4c6b-90d8-4b543790ff23 · outbound

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CaTE Data Curation for Trustworthy AI Column Type Annotation using ChatGPT

Reference 92

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This paper cites Biocreative v cdr task corpus: a resource for chemical disease relation extraction.

CaTE Data Curation for Trustworthy AI Biocreative v cdr task corpus: a resource for chemical disease relation extraction

Reference 93

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This paper cites Table-GPT: Table Fine-tuned GPT for Diverse Table Tasks.

CaTE Data Curation for Trustworthy AI Table-GPT: Table Fine-tuned GPT for Diverse Table Tasks

Reference 94

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This paper cites Ecg noise sources and various noise removal techniques: A survey.

CaTE Data Curation for Trustworthy AI Ecg noise sources and various noise removal techniques: A survey

Reference 95

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CaTE Data Curation for Trustworthy AI Model-based synthetic sampling for imbalanced data

Reference 96

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CaTE Data Curation for Trustworthy AI The power of scale for parameter-efficient prompt tuning

Reference 97

Resolution: verified exact; doi, observed 2026-08-05T18:23:36.355798Z

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Observation 2f238297-f8dd-4424-81ea-92a063105623 · outbound

This paper cites 2D-Shapley: A Framework for Fragmented Data Valuation.

CaTE Data Curation for Trustworthy AI 2D-Shapley: A Framework for Fragmented Data Valuation

Reference 98

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Observation 74a4db54-0770-4da9-a2a3-806c27dff065 · outbound

This paper cites Data Authenticity, Consent, & Provenance for AI are all broken: what will it take to fix them?.

CaTE Data Curation for Trustworthy AI Data Authenticity, Consent, & Provenance for AI are all broken: what will it take to fix them?

Reference 99

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CaTE Data Curation for Trustworthy AI De-biasing “bias” measurement

Reference 100

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