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  • E-ISSN:

    2454-9584

    P-ISSN

    2454-8111

    Impact Factor 2021

    5.610

    Impact Factor 2022

    6.247

  • E-ISSN:

    2454-9584

    P-ISSN

    2454-8111

    Impact Factor 2021

    5.610

    Impact Factor 2022

    6.247

  • E-ISSN:

    2454-9584

    P-ISSN

    2454-8111

    Impact Factor 2021

    5.610

    Impact Factor 2022

    6.247

INTERNATIONAL JOURNAL OF INVENTIONS IN ENGINEERING & SCIENCE TECHNOLOGY

International Peer Reviewed (Refereed), Open Access Research Journal
(By Aryavart International University, India)

Paper Details

Leveraging AI for Data Provenance: Enhancing Tracking and Verification of Data Lineage in FATE Assessment

Swathi Chundru

Motivity Labs Pvt Ltd, Hyderabad, Telangana, India

87 - 104 Vol. 7, Jan-Dec, 2021
Receiving Date: 2021-03-30;    Acceptance Date: 2021-06-05;    Publication Date: 2021-06-29
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Abstract

A record of the sources and processing of data, known as data provenance, holds new possibilities in the ever-growing role that artificial intelligence (AI)-based systems play in assisting human decision-making. Fairness, accountability, transparency, and explainability are the four key virtues that responsible AI builds upon to prevent the terrible consequences that might arise from biased AI systems. This work describes current biases and explores potential applications of data provenance to alleviate them, in an effort to spark more research on data provenance that facilitates responsible AI. We start by going over biases resulting from the pre-processing and data origins. Next, we talk about the practice as it is now, the difficulties it faces, and the solutions that have been suggested. In order to create responsible AI-based systems, we give an overview of how our recommendations might help establish data provenance and hence eliminate biases arising from the origins and preprocessing of the data. We wrap up by outlining future study directions in our research agenda.

Keywords: Artificial Intelligence; Data lineage; FATE assessment

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