In the rapidly advancing world of artificial intelligence (AI), systems are increasingly deployed to recognize human faces and interpret emotional expressions. However, a critical issue has emerged: these AI technologies can manifest profound biases, particularly racial biases, that skew their performance and decision-making. Recent research conducted at Pennsylvania State University has brought to light how AI models trained on skewed datasets can learn biased correlations, such as associating happiness disproportionately with white faces. This raises urgent questions about the fairness and reliability of AI systems, especially as they become more integrated into everyday life.
The study, published in the journal Media Psychology, reveals that many AI models exhibit a troubling tendency: they misclassify emotions of individuals from minority racial groups, notably Black individuals, while performing relatively well for white subjects. This phenomenon arises because the training datasets behind these AI systems contain uneven representation of racial groups and their emotional expressions. For instance, datasets may have an overabundance of happy white faces, fostering an unintentional but damaging association of positive emotion predominantly with white race, while sad or negative emotions become linked with Black individuals.
S. Shyam Sundar, Evan Pugh University Professor and director of the Center for Socially Responsible Artificial Intelligence at Penn State, explains that AI “seems to have learned that race is an important criterion for determining whether a face is happy or sad” — not because developers intended this, but because the underlying data fed into the algorithms reflected existing societal imbalances. This unintended learning of racial biases within AI models echoes a wider concern in machine learning: the danger of “unanticipated correlations” when training data does not adequately represent diversity.
To explore whether ordinary users — laypersons — can recognize such biases embedded in AI systems, researchers designed a series of experiments. They constructed 12 versions of a prototype facial expression recognition AI, intentionally introducing skewed training data variations. Then, with a diverse pool of 769 participants from different racial backgrounds, the study tested whether individuals could detect biases in how the AI performed. Intriguingly, the overwhelming majority of participants failed to identify the racial bias, particularly if they belonged to the majority white group or were not affected by the misclassified emotions.
Lead author Cheng “Chris” Chen, an assistant professor at Oregon State University with a background in mass communications from Penn State’s Donald P. Bellisario College, highlights that although users tended to trust AI systems as neutral arbiters, this misplaced trust falters when discrepancies became evident. Black participants, especially when faced with biased AI outputs that misclassified their emotional expressions, were more inclined to note racial disparities. However, this recognition was often limited to specific emotions, notably negative affect such as sadness in Black faces, which were overrepresented in the biased training data.
The experiments were carefully designed with different scenarios. The first scenario presented participants with AI training data showing an uneven racial distribution in emotional categories—happy faces overwhelmingly white, sad faces predominantly Black. The second scenario stripped away minority representation altogether, showing only white faces across emotional categories. The third experiment combined these scenarios, featuring five distinct conditions from racially biased happy or sad faces to balanced, unbiased datasets. Participants’ perceptions of the AI’s fairness across racial groups were then assessed.
Results consistently demonstrated a troubling blindness to bias. Most participants did not perceive any unfairness or racial preference in the AI’s treatment of facial expressions when simply viewing the training data. It was only when the AI’s performance displayed overt bias — often by failing to accurately classify emotions in minority groups — that perceptions shifted. Black participants were more sensitive to these cues, suggesting that personal experience with misrepresentation sharpens awareness of bias, whereas others may overlook it entirely.
Sundar emphasizes the psychological dimension of these findings. The study underscores a wider societal issue: people tend to assume AI systems are objective and neutral, despite evidence to the contrary. This misplaced trust magnifies the potential harm caused by algorithmic bias, as users and developers may not scrutinize the underlying data or question how AI learns. Such oversight risks perpetuating racial stereotypes and unequal treatment, compounding social inequalities.
Chen further notes that the tendency to ignore training data biases and instead focus solely on AI’s apparent performance outcomes is problematic. When users observe racially biased results, they tend to form impressions based on these visible outcomes rather than understanding the root causes embedded in skewed datasets. This misperception highlights an urgent need for improved AI literacy — broadening public and expert understanding of how data shapes AI behavior and the societal implications of biased training.
Looking forward, the research team plans to pursue new ways of communicating bias in AI to a wide range of stakeholders, including users, developers, and policymakers. Better transparency and education about the origins and impacts of algorithmic bias are essential for fostering more equitable AI systems. The researchers view advancing media and AI literacy as a crucial defense against blind trust and uncritical acceptance of AI outputs, particularly when these technologies increasingly influence decision-making in consequential domains.
This study serves as a timely reminder that AI, despite its promise to enhance human capabilities, is only as fair and accurate as the data it learns from. If racial biases embedded in training datasets remain unrecognized or unchallenged, AI systems risk reinforcing and even amplifying existing prejudices. The path forward requires vigilance, interdisciplinary collaboration, and a commitment to equity that transcends technical innovation alone.
In essence, the research spotlights the human factor in AI bias: not only how machines learn from data, but how people perceive and react to AI behavior. Only by addressing both elements can societies hope to build AI that truly serves everyone — transcending the limitations imposed by skewed historical data and deep-seated social inequities.
Subject of Research: Racial bias in AI training data and laypersons’ ability to detect such bias
Article Title: Racial Bias in AI Training Data: Do Laypersons Notice?
News Publication Date: 16-Sep-2025
Web References: http://dx.doi.org/10.1080/15213269.2025.2558036
Keywords: Artificial intelligence, Generative AI, Psychological science, Behavioral psychology, Human social behavior, Group behavior, Communications, Mass media, Social groups, Social issues, Racial discrimination, Social discrimination
