Navigating Fairness: Practitioners' Understanding, Challenges, and Strategies in AI/ML Development
- URL: http://arxiv.org/abs/2403.15481v1
- Date: Thu, 21 Mar 2024 03:44:59 GMT
- Title: Navigating Fairness: Practitioners' Understanding, Challenges, and Strategies in AI/ML Development
- Authors: Aastha Pant, Rashina Hoda, Chakkrit Tantithamthavorn, Burak Turhan,
- Abstract summary: There is a lack of empirical studies focused on understanding the views and experiences of AI practitioners in developing a fair AI/ML.
We conducted semi-structured interviews with 22 AI practitioners to investigate their understanding of what a 'fair AI/ML' is.
We developed a framework showcasing the relationship between AI practitioners' understanding of 'fair AI/ML' and their challenges in its development.
- Score: 11.846525587357489
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: The rise in the use of AI/ML applications across industries has sparked more discussions about the fairness of AI/ML in recent times. While prior research on the fairness of AI/ML exists, there is a lack of empirical studies focused on understanding the views and experiences of AI practitioners in developing a fair AI/ML. Understanding AI practitioners' views and experiences on the fairness of AI/ML is important because they are directly involved in its development and deployment and their insights can offer valuable real-world perspectives on the challenges associated with ensuring fairness in AI/ML. We conducted semi-structured interviews with 22 AI practitioners to investigate their understanding of what a 'fair AI/ML' is, the challenges they face in developing a fair AI/ML, the consequences of developing an unfair AI/ML, and the strategies they employ to ensure AI/ML fairness. We developed a framework showcasing the relationship between AI practitioners' understanding of 'fair AI/ML' and (i) their challenges in its development, (ii) the consequences of developing an unfair AI/ML, and (iii) strategies used to ensure AI/ML fairness. Additionally, we also identify areas for further investigation and offer recommendations to aid AI practitioners and AI companies in navigating fairness.
Related papers
- What do AI/ML practitioners think about AI/ML bias? [11.846525587357489]
Our studies have revealed a discrepancy between practitioners' understanding of 'AI/ML bias' and the definitions of tech companies and researchers.
These efforts could yield a significant return on investment by aiding AI/ML practitioners in developing unbiased AI/ML systems.
arXiv Detail & Related papers (2024-07-11T23:43:25Z) - The Impossibility of Fair LLMs [59.424918263776284]
The need for fair AI is increasingly clear in the era of large language models (LLMs)
We review the technical frameworks that machine learning researchers have used to evaluate fairness.
We develop guidelines for the more realistic goal of achieving fairness in particular use cases.
arXiv Detail & Related papers (2024-05-28T04:36:15Z) - Particip-AI: A Democratic Surveying Framework for Anticipating Future AI Use Cases, Harms and Benefits [54.648819983899614]
Particip-AI is a framework to gather current and future AI use cases and their harms and benefits from non-expert public.
We gather responses from 295 demographically diverse participants.
arXiv Detail & Related papers (2024-03-21T19:12:37Z) - Testing autonomous vehicles and AI: perspectives and challenges from cybersecurity, transparency, robustness and fairness [53.91018508439669]
The study explores the complexities of integrating Artificial Intelligence into Autonomous Vehicles (AVs)
It examines the challenges introduced by AI components and the impact on testing procedures.
The paper identifies significant challenges and suggests future directions for research and development of AI in AV technology.
arXiv Detail & Related papers (2024-02-21T08:29:42Z) - POLARIS: A framework to guide the development of Trustworthy AI systems [3.02243271391691]
There is a significant gap between high-level AI ethics principles and low-level concrete practices for AI professionals.
We develop a novel holistic framework for Trustworthy AI - designed to bridge the gap between theory and practice.
Our goal is to empower AI professionals to confidently navigate the ethical dimensions of Trustworthy AI.
arXiv Detail & Related papers (2024-02-08T01:05:16Z) - Ethics in the Age of AI: An Analysis of AI Practitioners' Awareness and
Challenges [11.656193349991609]
We conducted a survey aimed at understanding AI practitioners' awareness of AI ethics and their challenges in incorporating ethics.
Based on 100 AI practitioners' responses, our findings indicate that majority of AI practitioners had a reasonable familiarity with the concept of AI ethics.
Formal education/training was considered somewhat helpful in preparing practitioners to incorporate AI ethics.
arXiv Detail & Related papers (2023-07-14T02:50:46Z) - Inherent Limitations of AI Fairness [16.588468396705366]
The study of AI fairness has rapidly developed into a rich field of research with links to computer science, social science, law, and philosophy.
Many technical solutions for measuring and achieving AI fairness have been proposed, yet their approach has been criticized in recent years for being misleading, unrealistic and harmful.
arXiv Detail & Related papers (2022-12-13T11:23:24Z) - Fairness in Agreement With European Values: An Interdisciplinary
Perspective on AI Regulation [61.77881142275982]
This interdisciplinary position paper considers various concerns surrounding fairness and discrimination in AI, and discusses how AI regulations address them.
We first look at AI and fairness through the lenses of law, (AI) industry, sociotechnology, and (moral) philosophy, and present various perspectives.
We identify and propose the roles AI Regulation should take to make the endeavor of the AI Act a success in terms of AI fairness concerns.
arXiv Detail & Related papers (2022-06-08T12:32:08Z) - Stakeholder Participation in AI: Beyond "Add Diverse Stakeholders and
Stir" [76.44130385507894]
This paper aims to ground what we dub a 'participatory turn' in AI design by synthesizing existing literature on participation and through empirical analysis of its current practices.
Based on our literature synthesis and empirical research, this paper presents a conceptual framework for analyzing participatory approaches to AI design.
arXiv Detail & Related papers (2021-11-01T17:57:04Z) - Building Bridges: Generative Artworks to Explore AI Ethics [56.058588908294446]
In recent years, there has been an increased emphasis on understanding and mitigating adverse impacts of artificial intelligence (AI) technologies on society.
A significant challenge in the design of ethical AI systems is that there are multiple stakeholders in the AI pipeline, each with their own set of constraints and interests.
This position paper outlines some potential ways in which generative artworks can play this role by serving as accessible and powerful educational tools.
arXiv Detail & Related papers (2021-06-25T22:31:55Z) - Advancing the Research and Development of Assured Artificial
Intelligence and Machine Learning Capabilities [2.688723831634804]
An adversarial AI (A2I) and adversarial ML (AML) attack seeks to deceive and manipulate AI/ML models.
It is imperative that AI/ML models can defend against these attacks.
The A2I Working Group (A2IWG) seeks to advance the research and development of assured AI/ML capabilities.
arXiv Detail & Related papers (2020-09-24T20:12:14Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.