"丹麦有些不正常":调查 Deepfake 角色认知及其对以人为本的人工智能的影响

Ilkka Kaate , Joni Salminen , João M. Santos , Soon-Gyo Jung , Hind Almerekhi , Bernard J. Jansen
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引用次数: 0

摘要

尽管由于其社会风险,"深度伪造 "往往具有负面含义,但它却具有改善人机交互(HCI)、以人为本的人工智能(AI)和用户体验(UX)的潜力。为了研究 "深度伪造 "对角色用户体验的影响,我们进行了一项实验研究,有46名用户使用 "深度伪造 "角色和人类角色来完成一项设计任务。我们收集了思考录音、观察笔记和调查数据。我们的混合方法分析结果表明,如果用户观察到了deepfake角色中的瑕疵,这些瑕疵就会对角色的用户体验和任务执行产生不利影响;然而,并不是所有用户都能识别出瑕疵。我们对调查数据的定量分析显示,在以下几个方面存在差异:(a)用户如何感知deepfake角色;(b)用户如何发现deepfake角色的漏洞;(c)deepfake角色的漏洞如何影响信息理解;以及(d)deepfake角色的漏洞如何影响任务完成。漏洞对真实性、角色感知和任务感知变量的影响最大,但对行为变量的影响较小。研究结果表明,实施 deepfake 角色的企业需要先解决感知方面的难题,然后才能充分发挥 deepfake 技术在角色创建方面的潜力。
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“There Is something Rotten in Denmark”: Investigating the Deepfake persona perceptions and their Implications for human-centered AI

Although they often have a negative connotation due to their social risks, deepfakes have the potential to improve HCI, human-centered AI, and user experience (UX). To investigate the impact of deepfakes on persona UX, we conducted an experimental study with 46 users who used a deepfake persona and a human persona to carry out a design task. We collected think-aloud, observant notes, and survey data. The results of our mixed-method analysis indicate that if users observe glitches in the deepfake personas, these glitches have a detrimental effect on the persona UX and task performance; however, not all users identify glitches. Our quantitative analysis of survey data shows that there are differences in how (a) users perceive deepfakes, (b) users detect deepfake glitches, (c) deepfake glitches affect information comprehension, and (d) deepfake glitches affect task completion. Glitches have the most significant impact on authenticity, persona perception, and task perception variables but less impact on behavioral variables. The results imply that organizations implementing deepfake personas need to address perceptual challenges before the full potential of deepfake technology can be realized for persona creation.

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