Regulating Reality: Exploring Synthetic Media Through Multistakeholder AI Governance

• URL: http://arxiv.org/abs/2502.04526v1
• Date: Thu, 06 Feb 2025 21:56:16 GMT
• Title: Regulating Reality: Exploring Synthetic Media Through Multistakeholder AI Governance
• Authors: Claire R. Leibowicz,
• Abstract summary: This paper analyzes 23 in-depth, semi-structured interviews with stakeholders governing synthetic media from across sectors. It reveals key themes affecting synthetic media governance, including how temporal perspectives-spanning past, present, and future. It also reveals the critical role of trust, both among stakeholders and between audiences and interventions.
• Score: 1.450405446885067
• License:
• Abstract: Artificial intelligence's integration into daily life has brought with it a reckoning on the role such technology plays in society and the varied stakeholders who should shape its governance. This is particularly relevant for the governance of AI-generated media, or synthetic media, an emergent visual technology that impacts how people interpret online content and perceive media as records of reality. Studying the stakeholders affecting synthetic media governance is vital to assessing safeguards that help audiences make sense of content in the AI age; yet there is little qualitative research about how key actors from civil society, industry, media, and policy collaborate to conceptualize, develop, and implement such practices. This paper addresses this gap by analyzing 23 in-depth, semi-structured interviews with stakeholders governing synthetic media from across sectors alongside two real-world cases of multistakeholder synthetic media governance. Inductive coding reveals key themes affecting synthetic media governance, including how temporal perspectives-spanning past, present, and future-mediate stakeholder decision-making and rulemaking on synthetic media. Analysis also reveals the critical role of trust, both among stakeholders and between audiences and interventions, as well as the limitations of technical transparency measures like AI labels for supporting effective synthetic media governance. These findings not only inform the evidence-based design of synthetic media policy that serves audiences encountering content, but they also contribute to the literature on multistakeholder AI governance overall through rare insight into real world examples of such processes.

Related papers

• Public sentiments on the fourth industrial revolution: An unsolicited public opinion poll from Twitter [0.0]
  This article explores public perceptions on the Fourth Industrial Revolution (4IR) through an analysis of social media discourse across six European countries. Using sentiment analysis and machine learning techniques, we assess how the public reacts to the integration of technologies such as artificial intelligence, robotics, and blockchain into society. The results highlight a significant polarization of opinions, with a shift from neutral to more definitive stances either embracing or resisting technological impacts.
  arXiv  Detail & Related papers  (2024-11-21T15:39:53Z)
• Artificial Intelligence in Brazilian News: A Mixed-Methods Analysis [0.0]
  This study analyzes 3,560 news articles from Brazilian media published between July 1, 2023, and February 29, 2024, from 13 popular online news outlets. The findings reveal that Brazilian news coverage of AI is dominated by topics related to applications in the workplace and product launches. The analysis also highlights a significant presence of industry-related entities, indicating a strong influence of corporate agendas in the country's news.
  arXiv  Detail & Related papers  (2024-10-22T20:52:51Z)
• 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)
• From Principles to Practices: Lessons Learned from Applying Partnership on AI's (PAI) Synthetic Media Framework to 11 Use Cases [1.2277343096128712]
  2023 was the year the world woke up to generative AI, and 2024 is the year policymakers are responding more firmly. This paper is the first known collection of diverse examples of the implementation of synthetic media governance. It highlights areas synthetic media governance can be applied, augmented, expanded, and refined for use, in practice.
  arXiv  Detail & Related papers  (2024-07-17T21:27:56Z)
• Guiding the Way: A Comprehensive Examination of AI Guidelines in Global Media [0.0]
  This study analyzes 37 AI guidelines for media purposes in 17 countries. Our analysis reveals key thematic areas, such as transparency, accountability, fairness, privacy, and the preservation of journalistic values. Results highlight shared principles and best practices that emerge from these guidelines.
  arXiv  Detail & Related papers  (2024-05-07T22:47:56Z)
• Towards Responsible AI in Banking: Addressing Bias for Fair Decision-Making [69.44075077934914]
  "Responsible AI" emphasizes the critical nature of addressing biases within the development of a corporate culture. This thesis is structured around three fundamental pillars: understanding bias, mitigating bias, and accounting for bias. In line with open-source principles, we have released Bias On Demand and FairView as accessible Python packages.
  arXiv  Detail & Related papers  (2024-01-13T14:07:09Z)
• Responsible AI Considerations in Text Summarization Research: A Review of Current Practices [89.85174013619883]
  We focus on text summarization, a common NLP task largely overlooked by the responsible AI community. We conduct a multi-round qualitative analysis of 333 summarization papers from the ACL Anthology published between 2020-2022. We focus on how, which, and when responsible AI issues are covered, which relevant stakeholders are considered, and mismatches between stated and realized research goals.
  arXiv  Detail & Related papers  (2023-11-18T15:35:36Z)
• The Participatory Turn in AI Design: Theoretical Foundations and the Current State of Practice [64.29355073494125]
  This article aims to ground what we dub the "participatory turn" in AI design by synthesizing existing theoretical literature on participation. We articulate empirical findings concerning the current state of participatory practice in AI design based on an analysis of recently published research and semi-structured interviews with 12 AI researchers and practitioners.
  arXiv  Detail & Related papers  (2023-10-02T05:30:42Z)
• Aggression and "hate speech" in communication of media users: analysis of control capabilities [50.591267188664666]
  Authors studied the possibilities of mutual influence of users in new media. They found a high level of aggression and hate speech when discussing an urgent social problem - measures for COVID-19 fighting. Results can be useful for developing media content in a modern digital environment.
  arXiv  Detail & Related papers  (2022-08-25T15:53:32Z)
• Stakeholder Participation in AI: Beyond "Add Diverse Stakeholders and Stir" [76.44130385507894]
  This paper aims to ground what we dub a 'participatory turn' in AI design by synthesizing existing literature on participation and through empirical analysis of its current practices. Based on our literature synthesis and empirical research, this paper presents a conceptual framework for analyzing participatory approaches to AI design.
  arXiv  Detail & Related papers  (2021-11-01T17:57:04Z)
• The Deepfake Detection Dilemma: A Multistakeholder Exploration of Adversarial Dynamics in Synthetic Media [0.0]
  Synthetic media detection technologies label media as either synthetic or non-synthetic. As detection practices become more accessible, they become more easily circumvented. This work concludes that there is no "best" approach to navigating the detector dilemma.
  arXiv  Detail & Related papers  (2021-02-11T16:44:09Z)

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.