Universität zu Köln

Werder, Karl ORCID: 0000-0001-8481-1596, Ramesh, Balasubramaniam and Zhang, Rongen (2022). Establishing Data Provenance for Responsible Artificial Intelligence Systems. ACM Transactions on Management Information Systems, 2 (13). ACM. ISSN 2158-6578

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Abstract

Data provenance, a record that describes the origins and processing of data, offers new promises in the increasingly important role of artificial intelligence (AI)-based systems in guiding human decision making. To avoid disastrous outcomes that can result from bias-laden AI systems, responsible AI builds on four important characteristics: fairness, accountability, transparency, and explainability. To stimulate further research on data provenance that enables responsible AI, this study outlines existing biases and discusses possible implementations of data provenance to mitigate them. We first review biases stemming from the data’s origins and pre-processing. We then discuss the current state of practice, the challenges it presents, and corresponding recommendations to address them. We present a summary highlighting how our recommendations can help establish data provenance and thereby mitigate biases stemming from the data’s origins and pre-processing to realize responsible AI-based systems. We conclude with a research agenda suggesting further research avenues.

Item Type:	Journal Article
Creators:	Creators Email ORCID ORCID Put Code Werder, Karl werder@wiso.uni-koeln.de orcid.org/0000-0001-8481-1596 UNSPECIFIED Ramesh, Balasubramaniam UNSPECIFIED UNSPECIFIED UNSPECIFIED Zhang, Rongen UNSPECIFIED UNSPECIFIED UNSPECIFIED
URN:	urn:nbn:de:hbz:38-538682
DOI:	10.1145/3503488
Journal or Publication Title:	ACM Transactions on Management Information Systems
Volume:	2
Number:	13
Date:	2022
Publisher:	ACM
ISSN:	2158-6578
Language:	English
Faculty:	Faculty of Management, Economy and Social Sciences
Divisions:	Weitere Institute, Arbeits- und Forschungsgruppen > Cologne Institute for Information Systems (CIIS) Faculty of Management, Economics and Social Sciences > Business Administration > Information Systems > Professorship for Informations Systems and Operations Research
Subjects:	Data processing Computer science Library and information sciences Management and auxiliary services
Refereed:	Yes
URI:	http://kups.ub.uni-koeln.de/id/eprint/53868