On Integrating the Data-Science and Machine-Learning Pipelines for Responsible AI-Reference-Cited by-同舟云学术

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On Integrating the Data-Science and Machine-Learning Pipelines for Responsible AI

Published:2024-06-09 Issue: Volume:41 Page:50-53
ISSN:
Container-title:Proceedings of the Conference on Governance, Understanding and Integration of Data for Effective and Responsible AI
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Author:

Esmaelizadeh Armin¹^ORCID,Rorseth Joel²^ORCID,Yu Andy²^ORCID,Godfrey Parke³^ORCID,Golab Lukasz²^ORCID,Srivastava Divesh⁴^ORCID,Szlichta Jaroslaw³^ORCID,Taghva Kazem¹^ORCID

Affiliation:

1. UNLV, USA

2. University of Waterloo, Canada

3. York University, Canada

4. AT&T Chief Data Office, USA

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3665601.3669849

Reference10 articles.

1. Ziawasch Abedjan, Lukasz Golab, Felix Naumann, and Thorsten Papenbrock. 2018. Data Profiling. Morgan & Claypool Publishers.

2. [2] Rakesh Agrawal and Ramakrishnan Srikant. 1994. Fast algorithms for mining association rules in large databases. In VLDB Conf., 487–499.

3. Exploring data using patterns: A survey

4. Armin Esmaeilzadeh, Lukasz Golab, and Kazem Taghva. 2023. InfoMoD: Information-theoretic Model Diagnostics. In SSDBM Conf., 19:1–19:4.

5. Interpretable and informative explanations of outcomes

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