Socio-contextual barriers in AI adoption: Examining awareness and perceptions in the technology acceptance model

Engineering notes

Key topics: autonomous driving, perception. See the paper for implementation details and experimental results.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

From healthcare to education, commerce to the workplace, today AI offers significant gains. Yet the realisation of these gains may not be equally distributed. Previous research into digital exclusion demonstrates how some societal groups are less able to leverage the benefits of technology, and already data is revealing socially stratified differences in people's perceptions and attitudes towards AI. Relatedly, due to the often-opaque integration of AI into everyday technologies, public awareness of AI is low, and evidence would suggest this too is a factor likely to be impacted by issues of digital exclusion. Given that we know perception drives technology adoption but do not yet know the impact of awareness in this trajectory, it is imperative we empirically measure these relationships from the perspective of societal inequities. Using data collected via two public surveys ( N = 305, 303), this paper extends the Technology Acceptance Model using a novel measure of awareness to better understand pathways to AI adoption. Our estimates are consistent with: (1) AI adoption pathways being largely similar to other technologies; (2) a lack of AI awareness impeding pathways to AI adoption; and (3) socio-contextual factors driving differences in awareness of AI. Ensuring all people in society can and do successfully adopt AI technologies requires modelling its unique properties and examining how these properties themselves may be subject to societal inequities.

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