The Complexities of AI for Social Good: An Explorative Study on Adversarial Machine Learning to Facilitate Deliberative Decision-Making in Elections.

IF 3.4 2区 哲学 Q1 ENGINEERING, MULTIDISCIPLINARY
Syafira Fitri Auliya, Olya Kudina, Aaron Yi Ding, Ibo Van de Poel
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Abstract

The proliferation and pervasive use of artificial intelligence (AI) pose significant challenges to our democracies. In particular, AI leverages microtargeting political campaigns by constructing detailed user profiles and inferring people's individual susceptibilities from their data. This capability enables highly targeted political messaging that can substantially influence voting decisions, potentially undermining citizens' ability to make deliberative decisions in elections. While existing research has proposed interventions to mitigate the negative impacts of political micro-targeting, many of these interventions may become less effective as AI continually yields more powerful and subliminal forms of micro-targeting. Assuming that technologies can play a role in mitigating these negative impacts of AI, we conducted an explorative study to identify the design principles of technological tools that could facilitate citizens' deliberative decision-making in elections in the continuously levelaged AI-based political campaigns. Using Indonesia's 2024 elections as a case study, we interviewed twenty citizens and four political actors to gain critical insights into how such technologies might be developed. Initially, we anticipated that privacy-enhancing AI, used to counterattack profiling AI used by political actors, might suffice to facilitate election deliberation. However, our findings reveal a more complex reality: addressing societal issues through technology is inherently challenging; no single solution can serve as a silver bullet. Instead, facilitating election deliberation requires integrating privacy with other conditions, including self-reflection, education, access to diverse information, critical thinking, and openness to others. These design principles might serve as concrete, actionable design principles to guide the development of technologies to enhance election deliberation.

社会公益人工智能的复杂性:对抗性机器学习促进选举审议决策的探索性研究。
人工智能(AI)的扩散和普遍使用对我们的民主国家构成了重大挑战。特别是,人工智能通过构建详细的用户档案和从他们的数据推断人们的个人敏感性来利用微目标政治活动。这种能力使高度有针对性的政治信息能够大大影响投票决定,潜在地破坏公民在选举中作出审议决定的能力。虽然现有研究提出了一些干预措施,以减轻政治微目标的负面影响,但随着人工智能不断产生更强大、更潜意识的微目标形式,其中许多干预措施可能会变得不那么有效。假设技术可以在减轻人工智能的这些负面影响方面发挥作用,我们进行了一项探索性研究,以确定技术工具的设计原则,这些技术工具可以在基于人工智能的政治运动中促进公民在选举中进行协商决策。以印度尼西亚2024年的选举为例,我们采访了20名公民和4名政治参与者,以获得有关如何开发此类技术的关键见解。最初,我们预计用于反击政治行为者使用的分析人工智能的隐私增强人工智能可能足以促进选举审议。然而,我们的研究结果揭示了一个更复杂的现实:通过技术解决社会问题本身就具有挑战性;没有任何单一的解决方案可以成为灵丹妙药。相反,促进选举审议需要将隐私与其他条件结合起来,包括自我反思、教育、获取多样化信息、批判性思维和对他人开放。这些设计原则可以作为具体的、可操作的设计原则来指导技术的发展,以加强对选举的审议。
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来源期刊
Science and Engineering Ethics
Science and Engineering Ethics 综合性期刊-工程:综合
CiteScore
10.70
自引率
5.40%
发文量
54
审稿时长
>12 weeks
期刊介绍: Science and Engineering Ethics is an international multidisciplinary journal dedicated to exploring ethical issues associated with science and engineering, covering professional education, research and practice as well as the effects of technological innovations and research findings on society. While the focus of this journal is on science and engineering, contributions from a broad range of disciplines, including social sciences and humanities, are welcomed. Areas of interest include, but are not limited to, ethics of new and emerging technologies, research ethics, computer ethics, energy ethics, animals and human subjects ethics, ethics education in science and engineering, ethics in design, biomedical ethics, values in technology and innovation. We welcome contributions that deal with these issues from an international perspective, particularly from countries that are underrepresented in these discussions.
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