Participatory approaches to artificial intelligence (AI) and machine learning (ML) are gaining momentum: the increased attention comes partly with the view that participation opens the gateway to an inclusive, equitable, robust, responsible and trustworthy AI.Among other benefits, participatory approaches are essential to understanding and adequately representing the needs, desires and perspectives of historically marginalized communities. However, there currently exists lack of clarity on what meaningful participation entails and what it is expected to do. In this paper we first review participatory approaches as situated in historical contexts as well as participatory methods and practices within the AI and ML pipeline. We then introduce three case studies in participatory AI.Participation holds the potential for beneficial, emancipatory and empowering technology design, development and deployment while also being at risk for concerns such as cooptation and conflation with other activities. We lay out these limitations and concerns and argue that as participatory AI/ML becomes in vogue, a contextual and nuanced understanding of the term as well as consideration of who the primary beneficiaries of participatory activities ought to be constitute crucial factors to realizing the benefits and opportunities that participation brings.
翻译:人工智能(AI)和机器学习(ML)的参与性办法正在获得势头:人们日益注意的部分内容是,参与打开了通向包容、公平、有力、负责和值得信赖的AI的大门。除其他好处外,参与性办法对于理解和充分代表历史上边缘化社区的需求、愿望和观点至关重要。然而,目前尚不清楚有意义的参与意味着什么以及它预期做什么。在本文件中,我们首先审查历史背景中的参与性办法以及大赦国际和ML编审中的参与性方法和做法。然后,我们在参与性AI中进行三项案例研究。参与有可能带来有益的、解放性的和增强能力的技术设计、开发和部署,同时也有可能引起诸如相互配合和与其他活动挂钩等关切问题的风险。我们阐述了这些局限性和关切,并争论说,由于参与性的AI/ML在言论中出现一种背景和细微的理解,以及考虑参与活动的主要受益者应成为实现参与所带来的利益和机会的关键因素。