Anticipating Impacts: Using Large-Scale Scenario Writing to Explore Diverse Implications of Generative AI in the News Environment

Fuente: arXiv
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Main Authors: Kieslich, Kimon, Diakopoulos, Nicholas, Helberger, Natali
Format: Preprint
Published: 2023
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author Kieslich, Kimon
Diakopoulos, Nicholas
Helberger, Natali
author_facet Kieslich, Kimon
Diakopoulos, Nicholas
Helberger, Natali
contents The tremendous rise of generative AI has reached every part of society - including the news environment. There are many concerns about the individual and societal impact of the increasing use of generative AI, including issues such as disinformation and misinformation, discrimination, and the promotion of social tensions. However, research on anticipating the impact of generative AI is still in its infancy and mostly limited to the views of technology developers and/or researchers. In this paper, we aim to broaden the perspective and capture the expectations of three stakeholder groups (news consumers; technology developers; content creators) about the potential negative impacts of generative AI, as well as mitigation strategies to address these. Methodologically, we apply scenario writing and use participatory foresight in the context of a survey (n=119) to delve into cognitively diverse imaginations of the future. We qualitatively analyze the scenarios using thematic analysis to systematically map potential impacts of generative AI on the news environment, potential mitigation strategies, and the role of stakeholders in causing and mitigating these impacts. In addition, we measure respondents' opinions on a specific mitigation strategy, namely transparency obligations as suggested in Article 52 of the draft EU AI Act. We compare the results across different stakeholder groups and elaborate on the (non-) presence of different expected impacts across these groups. We conclude by discussing the usefulness of scenario-writing and participatory foresight as a toolbox for generative AI impact assessment.
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spellingShingle Anticipating Impacts: Using Large-Scale Scenario Writing to Explore Diverse Implications of Generative AI in the News Environment
Kieslich, Kimon
Diakopoulos, Nicholas
Helberger, Natali
Computers and Society
Human-Computer Interaction
The tremendous rise of generative AI has reached every part of society - including the news environment. There are many concerns about the individual and societal impact of the increasing use of generative AI, including issues such as disinformation and misinformation, discrimination, and the promotion of social tensions. However, research on anticipating the impact of generative AI is still in its infancy and mostly limited to the views of technology developers and/or researchers. In this paper, we aim to broaden the perspective and capture the expectations of three stakeholder groups (news consumers; technology developers; content creators) about the potential negative impacts of generative AI, as well as mitigation strategies to address these. Methodologically, we apply scenario writing and use participatory foresight in the context of a survey (n=119) to delve into cognitively diverse imaginations of the future. We qualitatively analyze the scenarios using thematic analysis to systematically map potential impacts of generative AI on the news environment, potential mitigation strategies, and the role of stakeholders in causing and mitigating these impacts. In addition, we measure respondents' opinions on a specific mitigation strategy, namely transparency obligations as suggested in Article 52 of the draft EU AI Act. We compare the results across different stakeholder groups and elaborate on the (non-) presence of different expected impacts across these groups. We conclude by discussing the usefulness of scenario-writing and participatory foresight as a toolbox for generative AI impact assessment.
title Anticipating Impacts: Using Large-Scale Scenario Writing to Explore Diverse Implications of Generative AI in the News Environment
topic Computers and Society
Human-Computer Interaction
url https://arxiv.org/abs/2310.06361