Empowering local communities using artificial intelligence.
Empowering local communities using artificial intelligence.
复制标题
利用人工智能赋能当地社区。
DOI：
10.1016/j.patter.2022.100449
复制
发表时间：
2022-03-11
期刊：
影响因子：
6.5
通讯作者：
Bozzon, Alessandro
中科院分区：
文献类型：
--
作者：
Hsu, Yen-Chia;Huang, Ting-Hao 'kenneth';Verma, Himanshu;Mauri, Andrea;Nourbakhsh, Illah;Bozzon, Alessandro
人工智能（AI）应用可以深刻影响社会。最近，人们对研究科学家如何为一般任务设计人工智能系统产生了广泛的兴趣。然而，以这种方式开发的人工智能系统是否能在不同的地区环境中发挥预期的作用，同时赋予当地人民权力，这仍然是一个悬而未决的问题。科学家如何与当地社区共同创建人工智能系统，以解决区域问题？本文在数据科学、人工智能、公民科学和人机交互的交叉点上，为这个尚未探索的方向提供了新的视角。通过案例研究，我们讨论了与当地人共同设计人工智能系统，使用人工智能收集和解释社区数据以及使人工智能系统适应长期社会变化的挑战。我们还整合了对人工智能研究和公民需求的见解，包括评估人工智能的社会影响，为人工智能开发策划社区数据集，以及建立人工智能管道向外行解释数据模式。共同创建人工智能系统可以使当地社区能够解决区域问题为社会影响设计人工智能是将人工智能研究与当地需求联系起来的关键与当地人一起管理数据可以为他们提供代理并促进人工智能研究使用人工智能解释数据模式可以揭示公众监督的本地问题人工智能（AI）越来越多地用于分析各种实践中的大量数据，例如物体识别。我们特别感兴趣的是使用人工智能驱动的系统，让当地社区参与制定计划或解决方案，以解决紧迫的社会和环境问题。这种地方环境往往涉及多个利益相关者，他们的议程不同，甚至相互矛盾，导致对这些系统的行为和预期结果的期望不匹配。有必要调查人工智能模型和管道是否可以通过共同创建和现场部署在不同的环境中按预期工作。基于与当地人共同创建人工智能驱动系统的案例研究，我们解释了需要更多关注的挑战，并提供了将人工智能研究与公民需求联系起来的可行途径。我们倡导开发新的协作方法和思维方式，以在多利益相关者背景下共同创建人工智能驱动的系统，以解决当地的问题。人工智能系统在实现可持续发展目标方面具有巨大的应用潜力。然而，多个利益相关者之间的利益冲突在与当地人共同设计人工智能系统、收集社区数据以微调人工智能模型以及使人工智能的行为适应长期社会变化时会带来挑战。通过案例研究和文献，本文解释了这些挑战，并强调了可行的途径，使当地社区倡导社会和政策变革，以应对紧迫的区域问题。
Artificial intelligence (AI) applications can profoundly affect society. Recently, there has been extensive interest in studying how scientists design AI systems for general tasks. However, it remains an open question as to whether the AI systems developed in this way can work as expected in different regional contexts while simultaneously empowering local people. How can scientists co-create AI systems with local communities to address regional concerns? This article contributes new perspectives in this underexplored direction at the intersection of data science, AI, citizen science, and human-computer interaction. Through case studies, we discuss challenges in co-designing AI systems with local people, collecting and explaining community data using AI, and adapting AI systems to long-term social change. We also consolidate insights into bridging AI research and citizen needs, including evaluating the social impact of AI, curating community datasets for AI development, and building AI pipelines to explain data patterns to laypeople. Co-creating AI systems can empower local communities to address regional concerns Designing AI for social impact is the key to linking AI research closer to local needs Curating data with local people can yield agency to them and facilitate AI research Explaining data patterns using AI can reveal local issues for public scrutiny Artificial intelligence (AI) is increasingly used to analyze large amounts of data in various practices, such as object recognition. We are specifically interested in using AI-powered systems to engage local communities in developing plans or solutions for pressing societal and environmental concerns. Such local contexts often involve multiple stakeholders with different and even contradictory agendas, resulting in mismatched expectations of the behaviors and desired outcomes of these systems. There is a need to investigate whether AI models and pipelines can work as expected in different contexts through co-creation and field deployment. Based on case studies in co-creating AI-powered systems with local people, we explain challenges that require more attention and provide viable paths to bridge AI research with citizen needs. We advocate for developing new collaboration approaches and mindsets that are needed to co-create AI-powered systems in multi-stakeholder contexts to address local concerns. AI systems have great potential to be applied in achieving Sustainable Development Goals. However, conflicts of interest among multiple stakeholders result in challenges when co-designing AI systems with local people, collecting community data to fine-tune the AI models, and adapting the behavior of AI to long-term social change. Through case studies and the literature, this article explains these challenges and highlights viable paths toward empowering local communities to advocate for social and policy changes in response to pressing regional issues.
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