Responsible AI in the Software Industry: A Practitioner-Centered Perspective
- URL: http://arxiv.org/abs/2412.07620v1
- Date: Tue, 10 Dec 2024 15:57:13 GMT
- Title: Responsible AI in the Software Industry: A Practitioner-Centered Perspective
- Authors: Matheus de Morais Leça, Mariana Bento, Ronnie de Souza Santos,
- Abstract summary: This study explores the practices and challenges faced by software practitioners in aligning with Responsible AI principles.
Our findings reveal that while practitioners frequently address fairness, inclusiveness, and reliability, principles such as transparency and accountability receive comparatively less attention in their practices.
- Score: 0.0
- License:
- Abstract: Responsible AI principles provide ethical guidelines for developing AI systems, yet their practical implementation in software engineering lacks thorough investigation. Therefore, this study explores the practices and challenges faced by software practitioners in aligning with these principles. Through semi-structured interviews with 25 practitioners, we investigated their methods, concerns, and strategies for addressing Responsible AI in software development. Our findings reveal that while practitioners frequently address fairness, inclusiveness, and reliability, principles such as transparency and accountability receive comparatively less attention in their practices. This scenario highlights gaps in current strategies and the need for more comprehensive frameworks to fully operationalize Responsible AI principles in software engineering.
Related papers
- An Empirical Study on Decision-Making Aspects in Responsible Software Engineering for AI [5.564793925574796]
This study investigates the ethical challenges and complexities inherent in responsible software engineering (RSE) for AI.
Personal values, emerging roles, and awareness of AIs societal impact influence responsible decision-making in RSE for AI.
arXiv Detail & Related papers (2025-01-26T22:38:04Z) - Trustworthy AI in practice: an analysis of practitioners' needs and challenges [2.5788518098820337]
A plethora of frameworks and guidelines have appeared to support practitioners in implementing Trustworthy AI applications.
We study the vision AI practitioners have on TAI principles, how they address them, and what they would like to have.
We highlight recommendations to help AI practitioners develop Trustworthy AI applications.
arXiv Detail & Related papers (2024-05-15T13:02:46Z) - 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) - Towards Responsible AI in Banking: Addressing Bias for Fair
Decision-Making [69.44075077934914]
"Responsible AI" emphasizes the critical nature of addressing biases within the development of a corporate culture.
This thesis is structured around three fundamental pillars: understanding bias, mitigating bias, and accounting for bias.
In line with open-source principles, we have released Bias On Demand and FairView as accessible Python packages.
arXiv Detail & Related papers (2024-01-13T14:07:09Z) - Report of the 1st Workshop on Generative AI and Law [78.62063815165968]
This report presents the takeaways of the inaugural Workshop on Generative AI and Law (GenLaw)
A cross-disciplinary group of practitioners and scholars from computer science and law convened to discuss the technical, doctrinal, and policy challenges presented by law for Generative AI.
arXiv Detail & Related papers (2023-11-11T04:13:37Z) - The Participatory Turn in AI Design: Theoretical Foundations and the
Current State of Practice [64.29355073494125]
This article aims to ground what we dub the "participatory turn" in AI design by synthesizing existing theoretical literature on participation.
We articulate empirical findings concerning the current state of participatory practice in AI design based on an analysis of recently published research and semi-structured interviews with 12 AI researchers and practitioners.
arXiv Detail & Related papers (2023-10-02T05:30:42Z) - Towards Implementing Responsible AI [22.514717870367623]
We propose four aspects of AI system design and development, adapting processes used in software engineering.
The salient findings cover four aspects of AI system design and development, adapting processes used in software engineering.
arXiv Detail & Related papers (2022-05-09T14:59:23Z) - Empowered and Embedded: Ethics and Agile Processes [60.63670249088117]
We argue that ethical considerations need to be embedded into the (agile) software development process.
We put emphasis on the possibility to implement ethical deliberations in already existing and well established agile software development processes.
arXiv Detail & Related papers (2021-07-15T11:14:03Z) - An interdisciplinary conceptual study of Artificial Intelligence (AI)
for helping benefit-risk assessment practices: Towards a comprehensive
qualification matrix of AI programs and devices (pre-print 2020) [55.41644538483948]
This paper proposes a comprehensive analysis of existing concepts coming from different disciplines tackling the notion of intelligence.
The aim is to identify shared notions or discrepancies to consider for qualifying AI systems.
arXiv Detail & Related papers (2021-05-07T12:01:31Z) - Principles to Practices for Responsible AI: Closing the Gap [0.1749935196721634]
We argue that an impact assessment framework is a promising approach to close the principles-to-practices gap.
We review a case study of AI's use in forest ecosystem restoration, demonstrating how an impact assessment framework can translate into effective and responsible AI practices.
arXiv Detail & Related papers (2020-06-08T16:04:44Z) - Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable
Claims [59.64274607533249]
AI developers need to make verifiable claims to which they can be held accountable.
This report suggests various steps that different stakeholders can take to improve the verifiability of claims made about AI systems.
We analyze ten mechanisms for this purpose--spanning institutions, software, and hardware--and make recommendations aimed at implementing, exploring, or improving those mechanisms.
arXiv Detail & Related papers (2020-04-15T17:15:35Z)
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.