Responsible Data Management

Summary: Positions data management as central to responsible Automated Decision Systems (ADS), spanning socio-legal-technical pipelines and oversight. Calls for research beyond efficiency and accuracy toward explicit values, beliefs, accountability, auditing, and public impact. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID: he8671bd8c24f9d4a
Venue: VLDB
Year: 2020
Pagerank: 0.00011709266
Overall Rank: 1,163 | 92.21%
DOI: 10.14778/3415478.3415570
PDF: Download (CC BY-NC-ND 4.0)

Incoming Non-self Citations Over Time

Authors

1. Julia Stoyanovich (New York University)
2. Bill Howe (University of Washington)
3. H.V. Jagadish (University of Michigan)

BibTeX Citation

@article{stoyanovich_vldb20,
        title = {{Responsible Data Management}},
        author = {Stoyanovich, Julia and Howe, Bill and Jagadish, H.V.},
        journal = {PVLDB},
        series = {{VLDB} '20},
        volume = {13},
        number = {12},
        pages = {3474--3488},
        doi = {10.14778/3415478.3415570},
        url = {https://doi.org/10.14778/3415478.3415570},
        year = {2020}
}

Incoming Citations (Sorted by Pagerank)

Showing 24 of 24 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
1,954	Interpretable Data-Based Explanations for Fairness Debugging	2022	SIGMOD	9.2965173e-05
3,749	Automated Feature Engineering for Algorithmic Fairness	2021	VLDB	7.0348686e-05
4,059	HedgeCut: Maintaining Randomised Trees for Low-Latency Machine Unlearning	2021	SIGMOD	6.8133669e-05
4,628	MLINSPECT: A Data Distribution Debugger for Machine Learning Pipelines	2021	SIGMOD	6.4796471e-05
6,338	Lightweight Inspection of Data Preprocessing in Native Machine Learning Pipelines	2021	CIDR	5.7918978e-05
7,513	Identifying Insufficient Data Coverage for Ordinal Continuous-Valued Attributes	2021	SIGMOD	5.4906303e-05
7,558	Automating and Optimizing Data-Centric What-If Analyses on Native Machine Learning Pipelines	2023	SIGMOD	5.4788791e-05
7,651	mlwhatif: What If You Could Stop Re-Implementing Your Machine Learning Pipeline Analyses Over and Over?	2023	VLDB	5.4596612e-05
9,609	Happiness Maximizing Sets under Group Fairness Constraints	2023	VLDB	5.1376962e-05
9,990	Fair and Actionable Causal Prescription Ruleset	2025	SIGMOD	5.0845476e-05
10,076	Maximizing Fair Content Spread via Edge Suggestion in Social Networks	2022	VLDB	5.0732737e-05
10,218	In-Database Data Imputation	2024	SIGMOD	5.0461625e-05
10,530	Fair-Count-Min: Frequency Estimation under Equal Group-wise Approximation Factor	2026	SIGMOD	4.9605343e-05
10,767	On Fair Epsilon Net and Geometric Hitting Set	2026	VLDB	4.9605343e-05
10,882	Unbiased Binning for Fairness-aware Attribute Representation	2026	VLDB	4.9605343e-05
11,015	Morphing-based Compression for Data-centric ML Pipelines	2026	VLDB	4.9605343e-05
11,055	CoBLOC - Continuous Block Level Fairness on Data Streams	2026	VLDB	4.9605343e-05
11,121	Fault Lines: Benchmarking the Impact of Label Data Quality on ML Robustness and Fairness	2026	VLDB	4.9605343e-05
11,357	Still More Shades of Null: An Evaluation Suite for Responsible Missing Value Imputation	2025	VLDB	4.9605343e-05
11,466	mlidea: Interactively Improving ML Data Preparation Code via “Shadow Pipelines”	2025	VLDB	4.9605343e-05
11,574	FairHash: A Fair and Memory/Time-efficient Hashmap	2024	SIGMOD	4.9605343e-05
11,674	Snapcase – Regain Control over Your Predictions with Low-Latency Machine Unlearning	2024	VLDB	4.9605343e-05
11,868	Screening Native ML Pipelines with “ArgusEyes”	2022	CIDR	4.9605343e-05
12,031	Enforcing Constraints for Machine Learning Systems via Declarative Feature Selection: An Experimental Study	2021	SIGMOD	4.9605343e-05

Outgoing Citations (Sorted by Pagerank)

Showing 4 of 4 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank	Cited Paper	Year	Venue	Pagerank
804	Interventional Fairness : Causal Database Repair for Algorithmic Fairness	2019	SIGMOD	0.00013788292
2,328	Causal Relational Learning	2020	SIGMOD	8.6206869e-05
5,624	On Obtaining Stable Rankings	2019	VLDB	6.0456849e-05
6,709	A Nutritional Label for Rankings	2018	SIGMOD	5.6855354e-05

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