Attitudes about artificial intelligence and machine learning are recent victims of endemic misunderstanding; given our increasing reliance on these technologies, the need for widespread understanding and confidence in their use is paramount. To this end, our work seeks to increase understanding in these typically inaccessible topics through interactive visualizations, thereby garnering curiosity in the hopes of kickstarting a cycle of understanding leading to further pursuit of knowledge. We hope this will cyclically shift global attitudes away from the intimidation of the unknown currently plaguing ML. This work explores best practices for supporting curiosity in new technologies, to inspire attitudinal paradigm-shifts. Over three, distinct visualizations of machine learning data, we created prototypes with carefully selected, highly-transparent datasets, to examine the success factors of engagement required for more informed attitudes on ML less dictated by the fear of the unknown. By employing interactive visualizations, we can captivate the interest of teenagers and individuals from diverse fields, encouraging them to explore the fascinating world of machine learning.

翻译：关于人工智能和机器学习的认知态度近期普遍遭受误解；鉴于我们日益依赖这些技术，广泛理解并对其使用建立信心至关重要。为此，本研究通过交互式可视化手段，致力于增强对这类通常难以理解主题的认知，从而激发好奇心，以期启动"理解-求知"的良性循环。我们期望这一循环能逐步扭转当前笼罩机器学习的"因未知而恐惧"的全球性态度。本项研究探索了激发新技术好奇心的最佳实践，旨在推动认知态度的范式转变。通过基于机器学习数据的三组差异化的可视化原型设计，我们选取了经审慎筛选的高度透明数据集，分析促成公众形成更理性认知（而非被对未知的恐惧所主导）的关键参与要素。通过运用交互式可视化技术，我们能够吸引青少年及跨领域人群的兴趣，鼓励他们探索机器学习这一迷人领域。