Artificial Tikkun Olam: AI Can Be Our Best Friend in Building an Open Human-Computer Society

• URL: http://arxiv.org/abs/2010.12015v1
• Date: Tue, 20 Oct 2020 23:29:45 GMT
• Title: Artificial Tikkun Olam: AI Can Be Our Best Friend in Building an Open Human-Computer Society
• Authors: Simon Kasif
• Abstract summary: We review a long-standing wisdom that a widespread practical deployment of any technology may produce adverse side effects misusing the knowhow. We describe some of the common and AI specific risks in health industries and other sectors. We propose a simple intelligent system quotient that may correspond to their adverse societal impact.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Technological advances of virtually every kind pose risks to society including fairness and bias. We review a long-standing wisdom that a widespread practical deployment of any technology may produce adverse side effects misusing the knowhow. This includes AI but AI systems are not solely responsible for societal risks. We describe some of the common and AI specific risks in health industries and other sectors and propose both broad and specific solutions. Each technology requires very specialized and informed tracking, monitoring and creative solutions. We postulate that AI systems are uniquely poised to produce conceptual and methodological solutions to both fairness and bias in automated decision-making systems. We propose a simple intelligent system quotient that may correspond to their adverse societal impact and outline a multi-tier architecture for producing solutions of increasing complexity to these risks. We also propose that universities may consider forming interdisciplinary Study of Future Technology Centers to investigate and predict the fuller range of risks posed by technology and seek both common and AI specific solutions using computational, technical, conceptual and ethical analysis

Related papers

• 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)
• Risks and Opportunities of Open-Source Generative AI [64.86989162783648]
  Applications of Generative AI (Gen AI) are expected to revolutionize a number of different areas, ranging from science & medicine to education. The potential for these seismic changes has triggered a lively debate about the potential risks of the technology, and resulted in calls for tighter regulation. This regulation is likely to put at risk the budding field of open-source generative AI.
  arXiv  Detail & Related papers  (2024-05-14T13:37:36Z)
• 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)
• AI Deception: A Survey of Examples, Risks, and Potential Solutions [20.84424818447696]
  This paper argues that a range of current AI systems have learned how to deceive humans. We define deception as the systematic inducement of false beliefs in the pursuit of some outcome other than the truth.
  arXiv  Detail & Related papers  (2023-08-28T17:59:35Z)
• An Overview of Catastrophic AI Risks [38.84933208563934]
  This paper provides an overview of the main sources of catastrophic AI risks, which we organize into four categories. Malicious use, in which individuals or groups intentionally use AIs to cause harm; AI race, in which competitive environments compel actors to deploy unsafe AIs or cede control to AIs. organizational risks, highlighting how human factors and complex systems can increase the chances of catastrophic accidents. rogue AIs, describing the inherent difficulty in controlling agents far more intelligent than humans.
  arXiv  Detail & Related papers  (2023-06-21T03:35:06Z)
• 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)
• Inherent Limitations of AI Fairness [16.588468396705366]
  The study of AI fairness has rapidly developed into a rich field of research with links to computer science, social science, law, and philosophy. Many technical solutions for measuring and achieving AI fairness have been proposed, yet their approach has been criticized in recent years for being misleading, unrealistic and harmful.
  arXiv  Detail & Related papers  (2022-12-13T11:23:24Z)
• A Brief Overview of AI Governance for Responsible Machine Learning Systems [3.222802562733787]
  This position paper seeks to present a brief introduction to AI governance, which is a framework designed to oversee the responsible use of AI. Due to the probabilistic nature of AI, the risks associated with it are far greater than traditional technologies.
  arXiv  Detail & Related papers  (2022-11-21T23:48:51Z)
• 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)
• An interdisciplinary conceptual study of Artificial Intelligence (AI) for helping benefit-risk assessment practices: Towards a comprehensive qualification matrix of AI programs and devices (pre-print 2020) [55.41644538483948]
  This paper proposes a comprehensive analysis of existing concepts coming from different disciplines tackling the notion of intelligence. The aim is to identify shared notions or discrepancies to consider for qualifying AI systems.
  arXiv  Detail & Related papers  (2021-05-07T12:01:31Z)

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.