Filling gaps in trustworthy development of AI
- URL: http://arxiv.org/abs/2112.07773v1
- Date: Tue, 14 Dec 2021 22:45:28 GMT
- Title: Filling gaps in trustworthy development of AI
- Authors: Shahar Avin, Haydn Belfield, Miles Brundage, Gretchen Krueger, Jasmine
Wang, Adrian Weller, Markus Anderljung, Igor Krawczuk, David Krueger,
Jonathan Lebensold, Tegan Maharaj, Noa Zilberman
- Abstract summary: Growing awareness of potential risks from AI systems has spurred action to address those risks.
But the principles often leave a gap between the "what" and the "how" of trustworthy AI development.
There is thus an urgent need for concrete methods that both enable AI developers to prevent harm and allow them to demonstrate their trustworthiness.
- Score: 20.354549569362035
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The range of application of artificial intelligence (AI) is vast, as is the
potential for harm. Growing awareness of potential risks from AI systems has
spurred action to address those risks, while eroding confidence in AI systems
and the organizations that develop them. A 2019 study found over 80
organizations that published and adopted "AI ethics principles'', and more have
joined since. But the principles often leave a gap between the "what" and the
"how" of trustworthy AI development. Such gaps have enabled questionable or
ethically dubious behavior, which casts doubts on the trustworthiness of
specific organizations, and the field more broadly. There is thus an urgent
need for concrete methods that both enable AI developers to prevent harm and
allow them to demonstrate their trustworthiness through verifiable behavior.
Below, we explore mechanisms (drawn from arXiv:2004.07213) for creating an
ecosystem where AI developers can earn trust - if they are trustworthy. Better
assessment of developer trustworthiness could inform user choice, employee
actions, investment decisions, legal recourse, and emerging governance regimes.
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) - Managing extreme AI risks amid rapid progress [171.05448842016125]
We describe risks that include large-scale social harms, malicious uses, and irreversible loss of human control over autonomous AI systems.
There is a lack of consensus about how exactly such risks arise, and how to manage them.
Present governance initiatives lack the mechanisms and institutions to prevent misuse and recklessness, and barely address autonomous systems.
arXiv Detail & Related papers (2023-10-26T17:59:06Z) - Never trust, always verify : a roadmap for Trustworthy AI? [12.031113181911627]
We examine trust in the context of AI-based systems to understand what it means for an AI system to be trustworthy.
We suggest a trust (resp. zero-trust) model for AI and suggest a set of properties that should be satisfied to ensure the trustworthiness of AI systems.
arXiv Detail & Related papers (2022-06-23T21:13:10Z) - Designing for Responsible Trust in AI Systems: A Communication
Perspective [56.80107647520364]
We draw from communication theories and literature on trust in technologies to develop a conceptual model called MATCH.
We highlight transparency and interaction as AI systems' affordances that present a wide range of trustworthiness cues to users.
We propose a checklist of requirements to help technology creators identify appropriate cues to use.
arXiv Detail & Related papers (2022-04-29T00:14:33Z) - 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) - Trustworthy AI: From Principles to Practices [44.67324097900778]
Many current AI systems were found vulnerable to imperceptible attacks, biased against underrepresented groups, lacking in user privacy protection, etc.
In this review, we strive to provide AI practitioners a comprehensive guide towards building trustworthy AI systems.
To unify the current fragmented approaches towards trustworthy AI, we propose a systematic approach that considers the entire lifecycle of AI systems.
arXiv Detail & Related papers (2021-10-04T03:20:39Z) - 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) - Know Your Model (KYM): Increasing Trust in AI and Machine Learning [4.93786553432578]
We analyze each element of trustworthiness and provide a set of 20 guidelines that can be leveraged to ensure optimal AI functionality.
The guidelines help ensure that trustworthiness is provable and can be demonstrated, they are implementation agnostic, and they can be applied to any AI system in any sector.
arXiv Detail & Related papers (2021-05-31T14:08:22Z) - The Sanction of Authority: Promoting Public Trust in AI [4.729969944853141]
We argue that public distrust of AI originates from the under-development of a regulatory ecosystem that would guarantee the trustworthiness of the AIs that pervade society.
We elaborate the pivotal role of externally auditable AI documentation within this model and the work to be done to ensure it is effective.
arXiv Detail & Related papers (2021-01-22T22:01:30Z) - 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.