UC Santa Barbara

This research seeks to understand how skin tone bias in image recognition algorithms impacts users’ adoption and usage of image recognition technology. We employed a diffusion of innovations framework to explore perceptions of compatibility, complexity, observability, relative advantage, trialability, and reinvention to determine their influence on participants' utilization of image recognition algorithms. Despite having more susceptibility to algorithmic bias, individuals with darker skin tones perceived these algorithms as having greater levels of compatibility and relative advantage, being more observable, and less complex and thus used them more extensively compared to those with lighter skin tones. Individuals with darker skin tones also displayed higher levels of reinvention behaviors, suggesting a potential adaptive response to counteract algorithmic biases.