Tackling AI Hyping

Mona Sloane, David Danks, Emanuel Moss
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Abstract

The introduction of a new generation of AI systems has kicked off another wave of AI hype. Now that AI systems have added the ability to produce new content to their predictive capabilities, extreme excitement about their alleged capabilities and opportunities is matched only by long held fears about job loss and machine control.

We typically understand the dynamics of AI hype to be something that happens to us, but in this commentary, we propose to flip the script. We suggest that AI hype is not a social fact, but a widely shared practice. We outline some negative implications of this practice and suggest how these can be mitigated, especially with regards to shifting ways of knowing and learning about AI, in the classroom and beyond. Even though pedagogical efforts (broadly understood) have benefited from AI hyping (there is now more varied AI training than ever), such efforts can also help minimize the impacts of hyping on the public’s credulity toward extravagant claims made about AI’s potential benefits and dangers.

Below, we consider steps that can be taken to address this issue and illustrate pathways for more holistic AI educational approaches that participate to a lesser degree in the practice of AI hyping. We contend that designing better AI futures will require that AI hyping be blunted to enable grounded debates about the ways that AI systems impact people’s lives both now and in the near future.

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应对人工智能炒作
新一代人工智能系统的引入引发了另一波人工智能炒作。如今,人工智能系统已经在其预测能力之外增加了生产新内容的能力,与对其所谓的能力和机会的极度兴奋相匹配的,只有长期以来对失业和机器控制的担忧。我们通常认为人工智能炒作的动态是发生在我们身上的事情,但在这篇评论中,我们打算颠覆这个剧本。我们认为,人工智能炒作不是一种社会事实,而是一种广泛共享的做法。我们概述了这种做法的一些负面影响,并提出了如何减轻这些影响的建议,特别是在课堂内外了解和学习人工智能的方式发生变化方面。尽管教学努力(被广泛理解)受益于人工智能的炒作(现在的人工智能训练比以往任何时候都更加多样化),但这种努力也可以帮助最大限度地减少炒作对公众轻信关于人工智能潜在好处和危险的夸大主张的影响。下面,我们考虑可以采取的步骤来解决这个问题,并说明更全面的人工智能教育方法的途径,这些方法在较小程度上参与人工智能炒作的实践。我们认为,设计更好的人工智能未来将需要对人工智能的炒作进行钝化,以便就人工智能系统在现在和不久的将来影响人们生活的方式进行有根据的辩论。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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