AI for Just Work: Constructing Diverse Imaginations of AI beyond "Replacing Humans"

• URL: http://arxiv.org/abs/2503.08720v1
• Date: Mon, 10 Mar 2025 20:03:55 GMT
• Title: AI for Just Work: Constructing Diverse Imaginations of AI beyond "Replacing Humans"
• Authors: Weina Jin, Nicholas Vincent, Ghassan Hamarneh,
• Abstract summary: The AI community usually focuses on "how" to develop AI techniques, but lacks thorough open discussions on "why" we develop AI.<n>Lacking critical reflections on the general visions and purposes of AI may make the community vulnerable to manipulation.<n>We denote answers to the "why" question the imaginations of AI, which depict our general visions, frames, and mindsets for the prospects of AI.
• Score: 23.559681740648447
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The AI community usually focuses on "how" to develop AI techniques, but lacks thorough open discussions on "why" we develop AI. Lacking critical reflections on the general visions and purposes of AI may make the community vulnerable to manipulation. In this position paper, we explore the "why" question of AI. We denote answers to the "why" question the imaginations of AI, which depict our general visions, frames, and mindsets for the prospects of AI. We identify that the prevailing vision in the AI community is largely a monoculture that emphasizes objectives such as replacing humans and improving productivity. Our critical examination of this mainstream imagination highlights its underpinning and potentially unjust assumptions. We then call to diversify our collective imaginations of AI, embedding ethical assumptions from the outset in the imaginations of AI. To facilitate the community's pursuit of diverse imaginations, we demonstrate one process for constructing a new imagination of "AI for just work," and showcase its application in the medical image synthesis task to make it more ethical. We hope this work will help the AI community to open dialogues with civil society on the visions and purposes of AI, and inspire more technical works and advocacy in pursuit of diverse and ethical imaginations to restore the value of AI for the public good.

Related papers

• Aligning Generalisation Between Humans and Machines [74.120848518198]
  Recent advances in AI have resulted in technology that can support humans in scientific discovery and decision support but may also disrupt democracies and target individuals. The responsible use of AI increasingly shows the need for human-AI teaming. A crucial yet often overlooked aspect of these interactions is the different ways in which humans and machines generalise.
  arXiv  Detail & Related papers  (2024-11-23T18:36:07Z)
• Imagining and building wise machines: The centrality of AI metacognition [78.76893632793497]
  We argue that shortcomings stem from one overarching failure: AI systems lack wisdom. While AI research has focused on task-level strategies, metacognition is underdeveloped in AI systems. We propose that integrating metacognitive capabilities into AI systems is crucial for enhancing their robustness, explainability, cooperation, and safety.
  arXiv  Detail & Related papers  (2024-11-04T18:10:10Z)
• Making AI Intelligible: Philosophical Foundations [0.0]
  'Making AI Intelligible' shows that philosophical work on the metaphysics of meaning can help answer these questions. Author: The questions addressed in the book are not only theoretically interesting, but the answers have pressing practical implications.
  arXiv  Detail & Related papers  (2024-06-12T12:25:04Z)
• Explainable Human-AI Interaction: A Planning Perspective [32.477369282996385]
  AI systems need to be explainable to the humans in the loop. We will discuss how the AI agent can use mental models to either conform to human expectations, or change those expectations through explanatory communication. While the main focus of the book is on cooperative scenarios, we will point out how the same mental models can be used for obfuscation and deception.
  arXiv  Detail & Related papers  (2024-05-19T22:22:21Z)
• A Bibliometric View of AI Ethics Development [4.0998481751764]
  We perform a bibliometric analysis of AI Ethics literature for the last 20 years based on keyword search. We conjecture that the next phase of AI ethics is likely to focus on making AI more machine-like as AI matches or surpasses humans intellectually.
  arXiv  Detail & Related papers  (2024-02-08T16:36:55Z)
• Reflective Artificial Intelligence [2.7412662946127755]
  Many important qualities that a human mind would have previously brought to the activity are utterly absent in AI. One core feature that humans bring to tasks is reflection. Yet this capability is utterly missing from current mainstream AI. In this paper we ask what reflective AI might look like.
  arXiv  Detail & Related papers  (2023-01-25T20:50:26Z)
• Seamful XAI: Operationalizing Seamful Design in Explainable AI [59.89011292395202]
  Mistakes in AI systems are inevitable, arising from both technical limitations and sociotechnical gaps. We propose that seamful design can foster AI explainability by revealing sociotechnical and infrastructural mismatches. We explore this process with 43 AI practitioners and real end-users.
  arXiv  Detail & Related papers  (2022-11-12T21:54:05Z)
• 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)
• Challenges of Artificial Intelligence -- From Machine Learning and Computer Vision to Emotional Intelligence [0.0]
  We believe that AI is a helper, not a ruler of humans. Computer vision has been central to the development of AI. Emotions are central to human intelligence, but little use has been made in AI.
  arXiv  Detail & Related papers  (2022-01-05T06:00:22Z)
• 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)

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.