Inauthentic inclusion: Exploring how intention to use AI-generated diverse models can backfire

Sean Sands, Vlad Demsar, Carla Ferraro, Colin Campbell, Justin Cohen
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Abstract

Rapid advances in AI technology have important implications for, and effects on, brands and advertisers. Increasingly, brands are creating digital models to showcase clothing and accessories in a similar way to human models, with AI used to customize various body types, ages, sizes, and skin tones. However, little is known about how the underrepresented consumers respond to a brand's intention to use AI-generated models to represent them. We explore this by conducting four studies. We find evidence that a brand's intention to use AI-generated (vs. human) models negatively affects brand attitude (study 1). We further investigate this effect using two different underrepresented consumer groups: LGBTQIA+ consumers (study 2) and consumers with disabilities (study 3). We show the effect to be serially mediated by consumers' perception of greater threat to their self-identity and a lower sense of belonging, subsequently having a negative effect on brand attitude. Finally, we show that the perception of a brand's motivation for representing diverse consumer groups can attenuate these negative effects (study 4). Specifically, when consumers believe a brand is intrinsically motivated to use AI-generated diversity representations, they report a significantly lower social identity threat which in turn is associated with a significantly higher sense of belonging to the brand. Our research findings suggest that a brand's well-meaning intentions to represent diversity can in fact have negative effects on the very consumers whom a brand is trying to attract. While catering to diversity is of critical importance, our results indicate that brand managers should exercise caution when using AI to appeal to diverse groups of potential consumers.
不真实的包容:探索使用人工智能生成的多样化模型的意图如何适得其反
人工智能技术的飞速发展对品牌和广告商有着重要的影响。越来越多的品牌正在创建数字模型,以类似人类模特的方式展示服装和配饰,并利用人工智能定制各种体型、年龄、尺寸和肤色。然而,人们对代表性不足的消费者如何回应品牌使用人工智能生成的模特来代表他们的意图知之甚少。我们通过四项研究对此进行了探讨。我们发现有证据表明,品牌使用人工智能生成模型(与人类模型相比)的意图会对品牌态度产生负面影响(研究 1)。我们通过两个不同的代表性不足的消费者群体进一步研究了这一影响:LGBTQIA+ 消费者(研究 2)和残疾消费者(研究 3)。我们发现,消费者认为自我认同受到了更大的威胁,归属感降低,从而对品牌态度产生负面影响。最后,我们表明,对品牌代表不同消费群体的动机的认知可以减轻这些负面影响(研究 4)。具体来说,当消费者认为一个品牌使用人工智能生成的多元化表征的内在动机时,他们报告的社会认同威胁就会明显降低,这反过来又与明显较高的品牌归属感相关联。我们的研究结果表明,品牌表现多样性的善意意图实际上会对品牌试图吸引的消费者产生负面影响。虽然迎合多样性至关重要,但我们的研究结果表明,品牌管理者在使用人工智能吸引不同的潜在消费者群体时应谨慎行事。
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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