From the Ground Truth Up: Doing AI Ethics from Practice to Principles
- URL: http://arxiv.org/abs/2201.01659v1
- Date: Wed, 5 Jan 2022 15:33:33 GMT
- Title: From the Ground Truth Up: Doing AI Ethics from Practice to Principles
- Authors: James Brusseau
- Abstract summary: Recent AI ethics has focused on applying abstract principles downward to practice.
This paper moves in the other direction.
Ethical insights are generated from the lived experiences of AI-designers working on tangible human problems.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Recent AI ethics has focused on applying abstract principles downward to
practice. This paper moves in the other direction. Ethical insights are
generated from the lived experiences of AI-designers working on tangible human
problems, and then cycled upward to influence theoretical debates surrounding
these questions: 1) Should AI as trustworthy be sought through explainability,
or accurate performance? 2) Should AI be considered trustworthy at all, or is
reliability a preferable aim? 3) Should AI ethics be oriented toward
establishing protections for users, or toward catalyzing innovation? Specific
answers are less significant than the larger demonstration that AI ethics is
currently unbalanced toward theoretical principles, and will benefit from
increased exposure to grounded practices and dilemmas.
Related papers
- Engineering Trustworthy AI: A Developer Guide for Empirical Risk Minimization [53.80919781981027]
Key requirements for trustworthy AI can be translated into design choices for the components of empirical risk minimization.
We hope to provide actionable guidance for building AI systems that meet emerging standards for trustworthiness of AI.
arXiv Detail & Related papers (2024-10-25T07:53:32Z) - Beyond principlism: Practical strategies for ethical AI use in research practices [0.0]
The rapid adoption of generative artificial intelligence in scientific research has outpaced the development of ethical guidelines.
Existing approaches offer little practical guidance for addressing ethical challenges of AI in scientific research practices.
I propose a user-centered, realism-inspired approach to bridge the gap between abstract principles and day-to-day research practices.
arXiv Detail & Related papers (2024-01-27T03:53:25Z) - Towards a Feminist Metaethics of AI [0.0]
I argue that these insufficiencies could be mitigated by developing a research agenda for a feminist metaethics of AI.
Applying this perspective to the context of AI, I suggest that a feminist metaethics of AI would examine: (i) the continuity between theory and action in AI ethics; (ii) the real-life effects of AI ethics; (iii) the role and profile of those involved in AI ethics; and (iv) the effects of AI on power relations through methods that pay attention to context, emotions and narrative.
arXiv Detail & Related papers (2023-11-10T13:26:45Z) - 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) - Metaethical Perspectives on 'Benchmarking' AI Ethics [81.65697003067841]
Benchmarks are seen as the cornerstone for measuring technical progress in Artificial Intelligence (AI) research.
An increasingly prominent research area in AI is ethics, which currently has no set of benchmarks nor commonly accepted way for measuring the 'ethicality' of an AI system.
We argue that it makes more sense to talk about 'values' rather than 'ethics' when considering the possible actions of present and future AI systems.
arXiv Detail & Related papers (2022-04-11T14:36:39Z) - Cybertrust: From Explainable to Actionable and Interpretable AI (AI2) [58.981120701284816]
Actionable and Interpretable AI (AI2) will incorporate explicit quantifications and visualizations of user confidence in AI recommendations.
It will allow examining and testing of AI system predictions to establish a basis for trust in the systems' decision making.
arXiv Detail & Related papers (2022-01-26T18:53:09Z) - Expose Uncertainty, Instill Distrust, Avoid Explanations: Towards
Ethical Guidelines for AI [3.0534660670547864]
I argue that the best way to help humans using AI technology is to make them aware of the intrinsic limitations and problems of AI algorithms.
I suggest three ethical guidelines to be used in the presentation of results.
arXiv Detail & Related papers (2021-11-29T14:53:35Z) - Trustworthy AI: A Computational Perspective [54.80482955088197]
We focus on six of the most crucial dimensions in achieving trustworthy AI: (i) Safety & Robustness, (ii) Non-discrimination & Fairness, (iii) Explainability, (iv) Privacy, (v) Accountability & Auditability, and (vi) Environmental Well-Being.
For each dimension, we review the recent related technologies according to a taxonomy and summarize their applications in real-world systems.
arXiv Detail & Related papers (2021-07-12T14:21:46Z) - 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) - Ethics as a service: a pragmatic operationalisation of AI Ethics [1.1083289076967895]
gap exists between theory of AI ethics principles and the practical design of AI systems.
This is the question we seek to address here by exploring why principles and technical translational tools are still needed even if they are limited.
arXiv Detail & Related papers (2021-02-11T21:29:25Z) - AI virtues -- The missing link in putting AI ethics into practice [0.0]
The paper defines four basic AI virtues, namely justice, honesty, responsibility and care.
It defines two second-order AI virtues, prudence and fortitude, that bolster achieving the basic virtues.
arXiv Detail & Related papers (2020-11-25T14:14:47Z)
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.