Truthful AI: Developing and governing AI that does not lie
- URL: http://arxiv.org/abs/2110.06674v1
- Date: Wed, 13 Oct 2021 12:18:09 GMT
- Title: Truthful AI: Developing and governing AI that does not lie
- Authors: Owain Evans, Owen Cotton-Barratt, Lukas Finnveden, Adam Bales, Avital
Balwit, Peter Wills, Luca Righetti, William Saunders
- Abstract summary: Lying -- the use of verbal falsehoods to deceive -- is harmful.
While lying has traditionally been a human affair, AI systems are becoming increasingly prevalent.
This raises the question of how we should limit the harm caused by AI "lies"
- Score: 0.26385121748044166
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: In many contexts, lying -- the use of verbal falsehoods to deceive -- is
harmful. While lying has traditionally been a human affair, AI systems that
make sophisticated verbal statements are becoming increasingly prevalent. This
raises the question of how we should limit the harm caused by AI "lies" (i.e.
falsehoods that are actively selected for). Human truthfulness is governed by
social norms and by laws (against defamation, perjury, and fraud). Differences
between AI and humans present an opportunity to have more precise standards of
truthfulness for AI, and to have these standards rise over time. This could
provide significant benefits to public epistemics and the economy, and mitigate
risks of worst-case AI futures.
Establishing norms or laws of AI truthfulness will require significant work
to: (1) identify clear truthfulness standards; (2) create institutions that can
judge adherence to those standards; and (3) develop AI systems that are
robustly truthful.
Our initial proposals for these areas include: (1) a standard of avoiding
"negligent falsehoods" (a generalisation of lies that is easier to assess); (2)
institutions to evaluate AI systems before and after real-world deployment; and
(3) explicitly training AI systems to be truthful via curated datasets and
human interaction.
A concerning possibility is that evaluation mechanisms for eventual
truthfulness standards could be captured by political interests, leading to
harmful censorship and propaganda. Avoiding this might take careful attention.
And since the scale of AI speech acts might grow dramatically over the coming
decades, early truthfulness standards might be particularly important because
of the precedents they set.
Related papers
- Deception and Manipulation in Generative AI [0.0]
I argue that AI-generated content should be subject to stricter standards against deception and manipulation.
I propose two measures to guard against AI deception and manipulation.
arXiv Detail & Related papers (2024-01-20T21:54:37Z) - Fairness in AI and Its Long-Term Implications on Society [68.8204255655161]
We take a closer look at AI fairness and analyze how lack of AI fairness can lead to deepening of biases over time.
We discuss how biased models can lead to more negative real-world outcomes for certain groups.
If the issues persist, they could be reinforced by interactions with other risks and have severe implications on society in the form of social unrest.
arXiv Detail & Related papers (2023-04-16T11:22:59Z) - Aligning Artificial Intelligence with Humans through Public Policy [0.0]
This essay outlines research on AI that learn structures in policy data that can be leveraged for downstream tasks.
We believe this represents the "comprehension" phase of AI and policy, but leveraging policy as a key source of human values to align AI requires "understanding" policy.
arXiv Detail & Related papers (2022-06-25T21:31:14Z) - 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) - Proceedings of the Artificial Intelligence for Cyber Security (AICS)
Workshop at AAAI 2022 [55.573187938617636]
The workshop will focus on the application of AI to problems in cyber security.
Cyber systems generate large volumes of data, utilizing this effectively is beyond human capabilities.
arXiv Detail & Related papers (2022-02-28T18:27:41Z) - 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) - From the Ground Truth Up: Doing AI Ethics from Practice to Principles [0.0]
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.
arXiv Detail & Related papers (2022-01-05T15:33:33Z) - 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) - The Role of Social Movements, Coalitions, and Workers in Resisting
Harmful Artificial Intelligence and Contributing to the Development of
Responsible AI [0.0]
Coalitions in all sectors are acting worldwide to resist hamful applications of AI.
There are biased, wrongful, and disturbing assumptions embedded in AI algorithms.
Perhaps one of the greatest contributions of AI will be to make us understand how important human wisdom truly is in life on earth.
arXiv Detail & Related papers (2021-07-11T18:51:29Z) - Could regulating the creators deliver trustworthy AI? [2.588973722689844]
AI is becoming all pervasive and is often deployed in everyday technologies, devices and services without our knowledge.
Fear is compounded by the inability to point to a trustworthy source of AI.
Some consider trustworthy AI to be that which complies with relevant laws.
Others point to the requirement to comply with ethics and standards.
arXiv Detail & Related papers (2020-06-26T01:32:53Z)
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.