Abstract
Purpose
The purpose of this study is to raise awareness about the ethical implications of artificial intelligence (AI) in the library and information industry, specifically focusing on bias and discrimination. It aims to highlight the need for proactive measures to mitigate these issues and ensure that AI technology is developed and implemented in an ethical and unbiased manner.
Design/methodology/approach
This viewpoint paper presents a critical analysis of the ethical implications of bias and discrimination in the library and information industry with respect to AI. It explores current practices and challenges in AI implementation and proposes strategies to address bias and discrimination in AI systems.
Findings
The findings of this study reveal that bias and discrimination are significant concerns in AI systems used in the library and information industry. These biases can perpetuate existing inequalities, hinder access to information and reinforce discriminatory practices. This study identifies key strategies such as data collection and representation, algorithmic transparency and inclusive design to address these issues.
Originality/value
This study contributes to the existing literature by examining the specific challenges of bias and discrimination in AI implementation within the library and information industry. It provides valuable insights into the ethical implications of AI technology and offers practical recommendations for professionals to confront and mitigate bias and discrimination in AI systems, ensuring equitable access to information for all users.
Subject
Library and Information Sciences,Information Systems
Reference17 articles.
1. Algorithmic bias in data-driven innovation in the age of AI;International Journal of Information Management,2021
2. Actionable approaches to promote ethical AI in libraries;Proceedings of the Association for Information Science and Technology,2021
3. Ethics and discrimination in artificial intelligence-enabled recruitment practices;Humanities and Social Sciences Communications,2023
4. Lack of transparency and potential bias in artificial intelligence data sets and algorithms: a scoping review;JAMA Dermatology,2021
5. Fairness and bias in artificial intelligence: a brief survey of sources, impacts, and mitigation strategies,2023
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