How artificial intelligence adopts human biases: the case of cosmetic skincare industry

Abstract

The cosmetic skincare industry is a growing market that extends to different regions and customer groups. In addition to scientific advances and technological developments, state-of-the-art digital approaches, including machine learning and other artificial intelligence (AI)-based techniques, are being applied at different stages of the value chain. The objectives of these efforts include optimizing the supply chain, developing high-quality, effective and safe products and personalization at every step of the customer journey. However, the use of digital technologies comes with risks and undesirable effects. These include a lack of transparency and accountability, compromised fairness and a general deficiency in data governance, all of which are critical at every customer touchpoint. This dark side of digital transformation is recognized by both businesses and governments. In this paper, we explain the concept of bias leading to unfairness for beauty technology applications. Based on published data we identified potential sources of AI bias in the cosmetic skincare industry and/or beauty tech. They were classified by the stage of the AI lifecycle: biases related to target setting, to acquisition and annotation, to modeling, to validation and evaluation, and to deployment and monitoring. We aim to create awareness of such phenomena among readers, whether executives, managers, developers or potential end-users.