Democratising AI: Multiple Meanings, Goals, and Methods
- URL: http://arxiv.org/abs/2303.12642v3
- Date: Mon, 7 Aug 2023 14:29:03 GMT
- Title: Democratising AI: Multiple Meanings, Goals, and Methods
- Authors: Elizabeth Seger, Aviv Ovadya, Ben Garfinkel, Divya Siddarth, Allan
Dafoe
- Abstract summary: Numerous parties are calling for the democratisation of AI, but the phrase is used to refer to a variety of goals, the pursuit of which sometimes conflict.
This paper identifies four kinds of AI democratisation that are commonly discussed.
Main takeaway is that AI democratisation is a multifarious and sometimes conflicting concept.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Numerous parties are calling for the democratisation of AI, but the phrase is
used to refer to a variety of goals, the pursuit of which sometimes conflict.
This paper identifies four kinds of AI democratisation that are commonly
discussed: (1) the democratisation of AI use, (2) the democratisation of AI
development, (3) the democratisation of AI profits, and (4) the democratisation
of AI governance. Numerous goals and methods of achieving each form of
democratisation are discussed. The main takeaway from this paper is that AI
democratisation is a multifarious and sometimes conflicting concept that should
not be conflated with improving AI accessibility. If we want to move beyond
ambiguous commitments to democratising AI, to productive discussions of
concrete policies and trade-offs, then we need to recognise the principal role
of the democratisation of AI governance in navigating tradeoffs and risks
across decisions around use, development, and profits.
Related papers
- Why Companies "Democratise" Artificial Intelligence: The Case of Open Source Software Donations [0.0]
Companies claim to "democratise" artificial intelligence (AI) when they donate AI open source software (OSS) to non-profit foundations or release AI models.
This study employs a mixed-methods approach to investigate commercial incentives for 43 AI OSS donations to the Linux Foundation.
It contributes a taxonomy of both individual and organisational social, economic, and technological incentives for AI democratisation.
arXiv Detail & Related papers (2024-09-26T14:23:44Z) - From Experts to the Public: Governing Multimodal Language Models in Politically Sensitive Video Analysis [48.14390493099495]
This paper examines the governance of large language models (MM-LLMs) through individual and collective deliberation.
We conducted a two-step study: first, interviews with 10 journalists established a baseline understanding of expert video interpretation; second, 114 individuals from the general public engaged in deliberation using Inclusive.AI.
arXiv Detail & Related papers (2024-09-15T03:17:38Z) - How will advanced AI systems impact democracy? [16.944248678780614]
We discuss the impacts that generative artificial intelligence may have on democratic processes.
We ask how AI might be used to destabilise or support democratic mechanisms like elections.
Finally, we discuss whether AI will strengthen or weaken democratic principles.
arXiv Detail & Related papers (2024-08-27T12:05:59Z) - Deceptive uses of Artificial Intelligence in elections strengthen support for AI ban [44.99833362998488]
We propose a framework for assessing AI's impact on elections.
We group AI-enabled campaigning uses into three categories -- campaign operations, voter outreach, and deception.
We provide the first systematic evidence from a preregistered representative survey.
arXiv Detail & Related papers (2024-08-08T12:58:20Z) - Public Constitutional AI [0.0]
We are increasingly subjected to the power of AI authorities.
How can we ensure AI systems have the legitimacy necessary for effective governance?
This essay argues that to secure AI legitimacy, we need methods that engage the public in designing and constraining AI systems.
arXiv Detail & Related papers (2024-06-24T15:00:01Z) - 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) - Ten Hard Problems in Artificial Intelligence We Must Get Right [72.99597122935903]
We explore the AI2050 "hard problems" that block the promise of AI and cause AI risks.
For each problem, we outline the area, identify significant recent work, and suggest ways forward.
arXiv Detail & Related papers (2024-02-06T23:16:41Z) - Towards Democratizing AI: A Comparative Analysis of AI as a Service
Platforms and the Open Space for Machine Learning Approach [1.5500145658862499]
We compare several popular AI-as-a-Service platforms and identify the key requirements for a platform that can achieve true democratization of AI.
Our analysis highlights the need for self-hosting options, high scalability, and openness.
Our approach is more comprehensive and effective in meeting the requirements of democratizing AI than existing AI-as-a-Service platforms.
arXiv Detail & Related papers (2023-11-08T08:02:59Z) - 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) - 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)
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.