Automatic Authorities: Power and AI
- URL: http://arxiv.org/abs/2404.05990v1
- Date: Tue, 9 Apr 2024 03:48:42 GMT
- Title: Automatic Authorities: Power and AI
- Authors: Seth Lazar,
- Abstract summary: Machine learning and related computational technologies now underpin vital government services.
They determine how we find out about everything from how to vote to where to get vaccinated.
A new wave of products based on Large Language Models (LLMs) will further transform our economic and political lives.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: As rapid advances in Artificial Intelligence and the rise of some of history's most potent corporations meet the diminished neoliberal state, people are increasingly subject to power exercised by means of automated systems. Machine learning and related computational technologies now underpin vital government services. They connect consumers and producers in new algorithmic markets. They determine how we find out about everything from how to vote to where to get vaccinated, and whose speech is amplified, reduced, or restricted. And a new wave of products based on Large Language Models (LLMs) will further transform our economic and political lives. Automatic Authorities are automated computational systems used to exercise power over us by determining what we may know, what we may have, and what our options will be. In response to their rise, scholars working on the societal impacts of AI and related technologies have advocated shifting attention from how to make AI systems beneficial or fair towards a critical analysis of these new power relations. But power is everywhere, and is not necessarily bad. On what basis should we object to new or intensified power relations, and what can be done to justify them? This paper introduces the philosophical materials with which to formulate these questions, and offers preliminary answers. It starts by pinning down the concept of power, focusing on the ability that some agents have to shape others' lives. It then explores how AI enables and intensifies the exercise of power so understood, and sketches three problems with power and three ways to solve those problems. It emphasises, in particular, that justifying power requires more than satisfying substantive justificatory criteria; standards of proper authority and procedural legitimacy must also be met. We need to know not only what power may be used for, but how it may be used, and by whom.
Related papers
- Position Paper: Agent AI Towards a Holistic Intelligence [53.35971598180146]
We emphasize developing Agent AI -- an embodied system that integrates large foundation models into agent actions.
In this paper, we propose a novel large action model to achieve embodied intelligent behavior, the Agent Foundation Model.
arXiv Detail & Related papers (2024-02-28T16:09:56Z) - Computing Power and the Governance of Artificial Intelligence [51.967584623262674]
Governments and companies have started to leverage compute as a means to govern AI.
compute-based policies and technologies have the potential to assist in these areas, but there is significant variation in their readiness for implementation.
naive or poorly scoped approaches to compute governance carry significant risks in areas like privacy, economic impacts, and centralization of power.
arXiv Detail & Related papers (2024-02-13T21:10:21Z) - Trust, Accountability, and Autonomy in Knowledge Graph-based AI for
Self-determination [1.4305544869388402]
Knowledge Graphs (KGs) have emerged as fundamental platforms for powering intelligent decision-making.
The integration of KGs with neuronal learning is currently a topic of active research.
This paper conceptualises the foundational topics and research pillars to support KG-based AI for self-determination.
arXiv Detail & Related papers (2023-10-30T12:51:52Z) - AI Explainability and Governance in Smart Energy Systems: A Review [0.36832029288386137]
Lack of explainability and governability of AI is a major concern for stakeholders.
This paper provides a review of AI explainability and governance in smart energy systems.
arXiv Detail & Related papers (2022-10-24T05:09:13Z) - Intelligent Decision Assistance Versus Automated Decision-Making:
Enhancing Knowledge Work Through Explainable Artificial Intelligence [0.0]
We propose a new class of DSS, namely Intelligent Decision Assistance (IDA)
IDA supports knowledge workers without influencing them through automated decision-making.
Specifically, we propose to use techniques of Explainable AI (XAI) while withholding concrete AI recommendations.
arXiv Detail & Related papers (2021-09-28T15:57:21Z) - 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) - AI Ethics Needs Good Data [0.8701566919381224]
We argue that discourse on AI must transcend the language of 'ethics' and engage with power and political economy.
We offer four 'economys' on which Good Data AI can be built: community, rights, usability and politics.
arXiv Detail & Related papers (2021-02-15T04:16:27Z) - Empowering Things with Intelligence: A Survey of the Progress,
Challenges, and Opportunities in Artificial Intelligence of Things [98.10037444792444]
We show how AI can empower the IoT to make it faster, smarter, greener, and safer.
First, we present progress in AI research for IoT from four perspectives: perceiving, learning, reasoning, and behaving.
Finally, we summarize some promising applications of AIoT that are likely to profoundly reshape our world.
arXiv Detail & Related papers (2020-11-17T13:14:28Z) - The Short Anthropological Guide to the Study of Ethical AI [91.3755431537592]
Short guide serves as both an introduction to AI ethics and social science and anthropological perspectives on the development of AI.
Aims to provide those unfamiliar with the field with an insight into the societal impact of AI systems and how, in turn, these systems can lead us to rethink how our world operates.
arXiv Detail & Related papers (2020-10-07T12:25:03Z) - Reasonable Machines: A Research Manifesto [0.0]
A sound ecosystem of trust requires ways for autonomously justify their actions.
Building on social reasoning models such as moral and legal philosophy.
Enabling normative communication creates trust and opens new dimensions of AI application.
arXiv Detail & Related papers (2020-08-14T08:51:33Z)
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.