What does the public think about Artificial Intelligence? — A Criticality Map to Understand bias in the public perception of AI

Published paper here: https://www.frontiersin.org/articles/10.3389/fcomp.2023.1113903/full

Much research has been done on the development of improved algorithms, data generation, labeling for supervised learning, and studying the economic impact of Artificial Intelligence (AI) on organizations and society. Yet there is a lack of research on how individuals perceive the potential impact of AI on their lives, the economy, or the society. However, understanding this individual perspective is crucial, as the adoption and diffusion of new technologies, such as AI and Machine Learning, can be propelled by higher acceptance or significantly delayed by perceived barriers from involved stakeholders. In this article, we present a study in which we captured novice's expectations towards AI.
For a variety of different statements on the impact of AI, we asked participants to rate how likely they considered a development to be and if they felt positive or negative about that development. A result of this study is a criticality matrix that 1) can guide developers and implementation managers of AI technology regarding socially critical aspects, 2) may guide policy making regarding specific areas in need of regulations, 3) inform researchers which areas could be addressed to increase social acceptance, and 4) help to identify relevant points for school and university curricula to inform future generations on AI.

在众多关于提升算法、数据生成、标注以支持监督学习的研究成果以及探讨人工智能（AI）对组织和社会经济影响的研究中，学界虽已取得丰硕成果，但对个人如何看待AI对其生活、经济或社会影响的探讨却相对匮乏。然而，深入理解这一个体视角至关重要，因为新技术的采纳与扩散，如AI与机器学习，往往受到相关利益相关者接受度的高低或感知到的障碍程度而加速或延缓。本文中，我们呈现了一项研究，旨在捕捉初学者对AI的期望。针对AI影响的多种不同陈述，我们要求参与者评估其对发展的可能性及其对此发展的正面或负面感受。本研究的结果是一张关键性矩阵，该矩阵可1）为AI技术的开发者及实施管理者在社交关键性方面提供指导，2）可能为政策制定者在需要监管的具体领域提供指导，3）为研究者指明哪些领域可被解决以提升社会接受度，以及4）有助于识别与学校及大学课程相关的关键点，以便对未来的世代进行AI教育。