Write a blog post exploring cultural perspectives and potential sensitivities around emotion recognition AI in different Middle Eastern contexts.
OpenEdition schlägt Ihnen vor, diesen Beitrag wie folgt zu zitieren:
Victoria Mummelthei (28. Mai 2024). ai and the Middle East, assignment complementing chapter “Affect” (deadline May 31). Keine Disziplin – No Discipline. Abgerufen am 9. März 2026 von https://doi.org/10.58079/13jqx


Emotion recognition artificial intelligence in the Middle East poses serious challenges beyond a technological development. AI can enhance public interactions by responding intuitively to emotional cues, providing personalized services. However, the implementation of this type of technology also raises ethical and cultural concerns. The potential for misinterpretation of emotions in various cultures and their misuse for surveillance in politically sensitive environments poses the risk of invasions of privacy and increased social control. For example, in countries with strict social norms and surveillance practices, such as Iran or Saudi Arabia, it is clear that emotion recognition technology can be used to monitor and control the population more effectively. The ability to analyze emotional states can be used to identify dissatisfaction or dissent, raising important human rights concerns. This is particularly sensitive in contexts where political freedoms are limited and there is a history of government surveillance.
Using AI for emotion recognition in the Middle East raises a number of cultural perspectives and sensitivities that need to be carefully considered.
In this chapter Kate Crawford, focuses on the field of affective computing, which aims to detect and interpret human emotions through technology. Throughout the chapter she highlights how training machine learning models on photographs of people making specific emotional expressions can result in flawed systems, based on those facial expressions or prior identification collected earlier. Obviously, I think that to achieve this kind of progress requires a lot of accuracy as well as security within the process, because it can be a great risk to the population. In Saudi Arabia, they are considering implementing this in the health sector to improve the performance of their services. Facial biometrics would be used for patient authentication, improvement of medical services, and diagnoses through recognition of those emotions that are often externalised. However, as I mentioned before, this requires a large database, care in handling that information and a fair amount of precision during the process.
Firstly, one of the pros is the improvement in allowing certain devices to respond to these emotional states, making interactions more intuitive. But at the same time, I would argue that emotion detection systems can be very inaccurate, and would often misinterpret expressions, especially when dealing with different cultures and diverse social contexts.
After all, AI emotion recognition in the Middle East must be approached with a deep understanding of cultural sensitivities and ethical implications. It is essential that developers and policymakers work in collaboration with ordinary people to ensure that these technologies are implemented in a fair and equitable manner, respecting cultural values and norms, and protecting citizens rights and privacy.
In the chapter “Affect,” Kate Crawford explores the intersection of artificial intelligence with efforts to read human intentions through facial expressions. She does that through investigating “micro-expressions” concept, developed by Paul Ekman. Micro-expressions are believed to be universal, uncontrollable facial movements that occur in a short time voluntarily and revealing ‘real emotions’.
Crawford discusses the problematic nature of systems designed to understand human intentions through facial expressions. She points out the criticisms such cultural differences challenge such a holistic understanding, while others believe that facial expressions do not necessarily convey a person’s true feelings. Despite the controversy surrounding Ekman’s theory, it is noted that it is still widely used.
The chapter indirectly points out who benefits from these technologies: those in power and corporations. It doesn’t seem dystopian anymore that governments, under the guise of security, might add the reading of micro-expressions to their arsenal, which has existed until today through surveillance, tracking, and recording individuals’ actions. It’s also not difficult to predict that companies, whose primary purpose is to make a profit, would use data collected from micro-expressions technology to sell more tailored products to individuals. Like the other chapters in the book, this chapter concludes by reiterating how technological advancements, marketed as ‘progress’, can actually be easily manipulated.
The problem discussed in the chapter is not new; it has always been a topic of debate among scientists and is fundamentally epistemological. Words are mere labels that hint at what we think about emotions. The issue is that people not only express their emotions differently, but they also experience feelings we describe with the same words in vastly different ways. For instance, someone could feel desperate while searching for a suitable laptop for work, just as another person might feel desperate looking for a lost child. We use the same words to describe different emotional experiences. The problem with computer algorithms is that they are trained and evaluated based on this ambiguous labeling. A possible solution could involve feature engineering developed through big data analysis, identifying features that are not labeled with specific words in human languages but are still effective in making predictions. However, when we try to put these findings into language to analyze their validity, we face the same problem of bias in our perception and vocabulary. The issue lies not in the machines or their use in specific areas, but in human perception and labeling during both the training and evaluation processes.
One Size Does Not Fit All
In this chapter, Margaret Mead offers a strong critique of Paul Ekman’s theory of the universalization of emotions. Contrary to Ekman, who argues that emotions and their expressions are universally biologically based, Mead emphasizes the importance of cultural factors in shaping emotional experiences. Accepting Ekman’s model leads to the view that emotions are innate and naturally occurring, but this perspective can be too simplistic. As Maria Gendron and Lisa Feldman Barrett claim, “facial expressions are not footprints,” highlighting that emotions cannot be fully understood without considering cultural context. Consequently, standardizing facial expressions, especially in AI training, can create significant issues.
One notable example is the Screening of Passengers by Observation Techniques (SPOT) program, which reveals the potential dangers and sensitivities of emotion recognition AI. Designed to detect potential terrorists post-9/11 using behavioral and emotional signs, SPOT has been criticized for reproducing racial profiling and systemic racism. This program underscores the risks of relying on so-called scientific tools that may not account for cultural nuances.
Furthermore, AI is increasingly used in job interviews, where it can make biased decisions based on how it was trained. If AI systems are primarily trained on Western datasets, they may react differently to identical facial expressions depending on a person’s race, potentially categorizing black faces as angrier and white faces as more suitable for jobs. This bias can lead to discriminatory hiring practices, reinforcing existing prejudices and inequalities.
In summary, Mead’s critique, supported by Gendron and Barrett’s insights, highlights the need to consider cultural factors in emotion research. The use of AI in sensitive areas like security and hiring must be approached with caution to avoid reinforcing biases and preserving discrimination. It is essential to ensure that AI training datasets are diverse and representative to eliminate these risks.
In his chapter Affection, Crawford provides a perspective on the historical development of emotion detection through artificial intelligence (AI). In 1967, Paul Ekman went on a research trip to Papua New Guinea, hypothesizing that people’s emotional states lead to similar expressions. Based on Ekman’s flashcards, the theory was that humans had universal ways of expressing affect. Ekman’s theory is the cornerstone of studying emotion perception in artificial intelligence. While some experiments support Ekman’s hypothesis, it is essential to acknowledge that adopting an approach that validates “all people experience all emotions at the same level” immensely disturbs people’s agency. As Sara Ahmed argues, effects are unique, subjective, and socially constructed. Mead’s argument that the effects are cultural, contrary to Ekman’s, also adopts Sara Ahmed’s position. It is also impossible for AI to avoid racial profiling. One of the most striking examples that not all facial expressions can be confined to a universal scale may be the research highlighting racial biases in the perception of emotions and threats conducted by the American Psychological Association. In the experiment, people tend to perceive Black men as larger, more threatening, and more aggressive than similarly sized White men, even when their sizes are the same and facial expressions are neutral. To put it in the context of the Middle East, it is impossible for emotion recognition technologies not to recognize a Middle Eastern person as more aggressive and dangerous than a white person because of the racial profiling and bias imposed by society. So, no technology that feeds on people and cultures can be freed from racial profiling. In addition to all these, the “uniqueness of emotions,” as defined by Ahmed in the context of Middle Eastern studies, would be worth mentioning. Simplifying the emotions experienced by all people leads to ignoring the uniqueness of human experience. It is almost impossible to talk about the same emotions experienced by a white person and a Middle Eastern person because their experiences and cultural backgrounds are different. Assuming that the feelings of the colonizer and the colonized will be the same results in a reductionist understanding of all hierarchies and power relations; therefore, it is pretty tricky. To equate the free emotions created by people who have not lived a free life with the emotions created in a free environment indeed leads to ignoring the experiences of oppressed societies. In this respect, I think it is a very restrictive and ignorant understanding. In this context, employing a critical perspective on Ekman’s theory would benefit scholarly inquiries centered on the essence of culture, humanity, and society.