AI Ethics by Design: Implementing Customizable Guardrails for Responsible AI Development
- URL: http://arxiv.org/abs/2411.14442v1
- Date: Tue, 05 Nov 2024 18:38:30 GMT
- Title: AI Ethics by Design: Implementing Customizable Guardrails for Responsible AI Development
- Authors: Kristina Ĺ ekrst, Jeremy McHugh, Jonathan Rodriguez Cefalu,
- Abstract summary: We propose a structure that integrates rules, policies, and AI assistants to ensure responsible AI behavior.
Our approach accommodates ethical pluralism, offering a flexible and adaptable solution for the evolving landscape of AI governance.
- Score: 0.0
- License:
- Abstract: This paper explores the development of an ethical guardrail framework for AI systems, emphasizing the importance of customizable guardrails that align with diverse user values and underlying ethics. We address the challenges of AI ethics by proposing a structure that integrates rules, policies, and AI assistants to ensure responsible AI behavior, while comparing the proposed framework to the existing state-of-the-art guardrails. By focusing on practical mechanisms for implementing ethical standards, we aim to enhance transparency, user autonomy, and continuous improvement in AI systems. Our approach accommodates ethical pluralism, offering a flexible and adaptable solution for the evolving landscape of AI governance. The paper concludes with strategies for resolving conflicts between ethical directives, underscoring the present and future need for robust, nuanced and context-aware AI systems.
Related papers
- Technology as uncharted territory: Contextual integrity and the notion of AI as new ethical ground [55.2480439325792]
I argue that efforts to promote responsible and ethical AI can inadvertently contribute to and seemingly legitimize this disregard for established contextual norms.
I question the current narrow prioritization in AI ethics of moral innovation over moral preservation.
arXiv Detail & Related papers (2024-12-06T15:36:13Z) - Delegating Responsibilities to Intelligent Autonomous Systems: Challenges and Benefits [1.7205106391379026]
As AI systems operate with autonomy and adaptability, the traditional boundaries of moral responsibility in techno-social systems are being challenged.
This paper explores the evolving discourse on the delegation of responsibilities to intelligent autonomous agents and the ethical implications of such practices.
arXiv Detail & Related papers (2024-11-06T18:40:38Z) - Particip-AI: A Democratic Surveying Framework for Anticipating Future AI Use Cases, Harms and Benefits [54.648819983899614]
General purpose AI seems to have lowered the barriers for the public to use AI and harness its power.
We introduce PARTICIP-AI, a framework for laypeople to speculate and assess AI use cases and their impacts.
arXiv Detail & Related papers (2024-03-21T19:12:37Z) - 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) - Beneficent Intelligence: A Capability Approach to Modeling Benefit,
Assistance, and Associated Moral Failures through AI Systems [12.239090962956043]
The prevailing discourse around AI ethics lacks the language and formalism necessary to capture the diverse ethical concerns that emerge when AI systems interact with individuals.
We present a framework formalizing a network of ethical concepts and entitlements necessary for AI systems to confer meaningful benefit or assistance to stakeholders.
arXiv Detail & Related papers (2023-08-01T22:38:14Z) - RE-centric Recommendations for the Development of Trustworthy(er)
Autonomous Systems [4.268504966623082]
Complying with the EU AI Act (AIA) guidelines while developing and implementing AI systems will soon be mandatory within the EU.
practitioners lack actionable instructions to operationalise ethics during AI systems development.
A literature review of different ethical guidelines revealed inconsistencies in the principles addressed and the terminology used to describe them.
arXiv Detail & Related papers (2023-05-29T11:57:07Z) - 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) - Putting AI Ethics into Practice: The Hourglass Model of Organizational
AI Governance [0.0]
We present an AI governance framework, which targets organizations that develop and use AI systems.
The framework is designed to help organizations deploying AI systems translate ethical AI principles into practice.
arXiv Detail & Related papers (2022-06-01T08:55:27Z) - 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) - 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.