The social dilemma in AI development and why we have to solve it

• URL: http://arxiv.org/abs/2107.12977v2
• Date: Wed, 28 Jul 2021 15:44:49 GMT
• Title: The social dilemma in AI development and why we have to solve it
• Authors: Inga Str\"umke, Marija Slavkovik, Vince I. Madai
• Abstract summary: We argue that AI developers face a social dilemma in AI development ethics, preventing the widespread adaptation of ethical best practices. We argue that AI development must be professionalised to overcome the social dilemma, and discuss how medicine can be used as a template in this process.
• Score: 2.707154152696381
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: While the demand for ethical artificial intelligence (AI) systems increases, the number of unethical uses of AI accelerates, even though there is no shortage of ethical guidelines. We argue that a main underlying cause for this is that AI developers face a social dilemma in AI development ethics, preventing the widespread adaptation of ethical best practices. We define the social dilemma for AI development and describe why the current crisis in AI development ethics cannot be solved without relieving AI developers of their social dilemma. We argue that AI development must be professionalised to overcome the social dilemma, and discuss how medicine can be used as a template in this process.

Related papers

• 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)
• 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)
• 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)
• 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)
• 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)
• A Deployment Model to Extend Ethically Aligned AI Implementation Method ECCOLA [5.28595286827031]
  This study aims to extend ECCOLA with a deployment model to drive the adoption of ECCOLA. The model includes simple metrics to facilitate the communication of ethical gaps or outcomes of ethical AI development.
  arXiv  Detail & Related papers  (2021-10-12T12:22:34Z)
• 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)
• Socially Responsible AI Algorithms: Issues, Purposes, and Challenges [31.382000425295885]
  Technologists and AI researchers have a responsibility to develop trustworthy AI systems. To build long-lasting trust between AI and human beings, we argue that the key is to think beyond algorithmic fairness.
  arXiv  Detail & Related papers  (2021-01-01T17:34:42Z)
• Modelos din\^amicos aplicados \`a aprendizagem de valores em intelig\^encia artificial [0.0]
  Several researchers in the area have developed a robust, beneficial, and safe concept of AI for the preservation of humanity and the environment. It is utmost importance that artificial intelligent agents have their values aligned with human values. Perhaps this difficulty comes from the way we are addressing the problem of expressing values using cognitive methods.
  arXiv  Detail & Related papers  (2020-07-30T00:56:11Z)
• Dynamic Cognition Applied to Value Learning in Artificial Intelligence [0.0]
  Several researchers in the area are trying to develop a robust, beneficial, and safe concept of artificial intelligence. It is of utmost importance that artificial intelligent agents have their values aligned with human values. A possible approach to this problem would be to use theoretical models such as SED.
  arXiv  Detail & Related papers  (2020-05-12T03:58:52Z)

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.