According to the chapter, what are the main risks of algorithmic bias in Emotion AI, and what recommendations do researchers like Tatiparti et al. (2025) propose to mitigate these risks?

The main risks are cultural and demographic bias, because models trained mostly on Western facial datasets misinterpret expressions from non-Western, female, or neurodivergent people; and algorithmic opacity, which makes Emotion AI a black box and increases the risk of unfair or abusive decisions in hiring, performance evaluation, and well-being monitoring. Tatiparti et al. (2025) recommend periodic bias auditing, training on more objective and diverse data, and a human-in-the-loop process in which AI is only a suggestive tool, not the final authority.

Section 5.2 identifies two core risks of algorithmic bias in Emotion AI. First, emotion recognition models are often trained on Western facial datasets, so they perform differently and often incorrectly on non-Western populations. For example, an expression of relaxation in one culture may be read as boredom or aggression by the same model. Women and neurodivergent people may also be misunderstood because the model cannot yet distinguish subtle expression differences. This becomes particularly serious when such outputs feed into recruitment, performance evaluation, or emotional well-being monitoring, where a context-free label such as disengaged or angry can cause unreasonable consequences. Second, algorithmic opacity compounds the problem: users cannot see how Emotion AI reaches its conclusions, which increases the risk of abuse and undermines employees' ability to challenge decisions. The same section also notes emotional misinterpretation as a technical limitation, because culture, gender, personality, and neurological variation all influence how people express and perceive emotions, and narrowly configured models neglect these differences. To mitigate these risks, Tatiparti et al. (2025) propose periodic bias auditing, objective training information, and a human-in-the-loop procedure where AI acts as a suggestive tool rather than a final authority. The chapter also adds that emotionally inferred conclusions should be interpretable and auditable, as Mohammad (2022) argues, so managers and workers can understand how conclusions were reached.