Why understanding and responding to search engine bias matters to language educators

Computer science research has increasingly documented social group bias in artificial intelligence (AI). Examples include bias against African Americans in software used by courts to determine bail and sentencing decisions (Angwin et al., 2016), facial recognition systems performing better on people with lighter skin (Buolamwini & Gebru, 2018), and a hiring algorithm penalizing graduates of women's colleges (Silberg & Manyika, 2019). Sources for AI bias are complex. They include bias in the initial data used by the AI along with the role of AI algorithms themselves in “amplifying” such initial biases (Ntoutsi et al., 2020). Effects of AI bias are commonly seen in search engine results that an AI application many use on a daily basis (Noble, 2018). Search engine bias represents a problem for language education because language materials creators are encouraged to harvest online content for their classes. Reasons for doing so include fostering linguistic authenticity (e.g., Akintunde & Famogbiyele, 2018) and cultural authenticity (Ahmadi & Derakhshan, 2016), especially through the integration of images or videos not created for language teaching. Multimedia materials from the internet can promote learner motivation by making language classes more vibrant, by aligning classes with students' interests, and by introducing up-to-date content (Zhang, 2013). The potential problem is when the teacher includes materials found through search engines with little awareness of the workings of bias in search engine results. Indeed, search engines are often even more trusted than traditional information sources such as news outlets (Bonart et al., 2020). In light of the foregoing, I conducted a small experiment. Imagining I wished to use images to teach vocabulary associated with interesting activities, I conducted a Google image search using the phrase people having fun. A partial screenshot of the results is reproduced in Figure 1. The results aptly illustrate the problem. Virtually all of those in the 778 resulting images of people were around their 20s (only 19 appeared to be in their 60s or older). While most were white, some were Black, but only eight were Asian. None were people with a visible disability, and many were involved in running, jumping, and swimming. None were fat. Most appeared to be engaged in middle-class pursuits such as vacationing on the beach. Only one was wearing a hijab. If I then selected some of these images to include in a vocabulary sheet, I could easily be reinscribing many forms of social group marginalization such as ableism, ageism, classism, and lookism. While the educator sensitive to biased representation might be able to compensate, this is actually difficult since such misrepresentation can be complex. My cursory analysis does not take into account additional forms of potential bias associated with the manner of representation in images. For example, studies of colorism indicate that when Black people are represented in mass media images there is a tendency to favor those with a more Eurocentric appearance, that is with straighter hair, thinner lips, narrower noses and, especially, lighter skin (Steele, 2016). Such an analysis also does not consider who is central in pictures and who is active vs. passive. Even more subtle issues of visual grammar such as camera angle and camera distance can convey meaning about individuals within the frame (Kress & van Leeuwen, 2006). For example, in one study of educational materials, non-Western people tended to be presented from a distance and from above with the viewer situated as voyeuristic consumer of the exotic other on display (Torres, 2015). I also made no effort to consider which images appeared before others in the results. This is important since search engine users tend to draw upon the first few results returned (Höchstötter & Lewandowski, 2009). Results like these are especially concerning because materials integrating them can represent a hidden curriculum, and such hidden curricula have potent effects upon learners. A hidden curriculum involving multimedia materials can levy particular power because of the strong socialization effects of such materials, especially upon children (Dubow et al., 2007). This is amply demonstrated by mass media promotion of unhealthy low-BMI bodies, sidestepping the accompanying eating disorder—sometimes fatal—that can result (Anderson-Fye, 2018). Limited empirical research in language teaching regarding unfair social group depictions confirms that there is already a problem with bias in social group representation in the materials used. Such research thus suggests a lack of mechanisms to take on the growing problem of AI/search engine bias. For example, despite multicultural claims, many English language teaching (ELT) materials fail to include representation of LGBT people, promoting a heteronormative stance (e.g., Appleby, 2017). Gender role stereotyping continues to be manifest in ELT materials (e.g., Söğüt, 2018). A study of ELT materials created by teachers across Japan revealed that entire social groups such as non-Anglo Americans and Africans were rendered invisible (Brown, 2021). The influence of media in general coupled with the power, ubiquity, and trust placed in ELT indicate that the potential for problematic social group depictions in materials should be strongly considered. Yet, often they are not. A recent analysis of evaluation schemes for language teaching materials found almost no attention to assessing social group representation as a criterion for materials acceptance decisions (Brown, 2023). Search engine bias dictates the need for a vigorous and cogent response from the ELT profession. Drawing upon critical media studies, language teachers can begin by taking two steps to address this problem. First, they can become familiar with forms of problematic representations of social groups in the mass media. These include ableism, ageism, andronormativity, Christonormativity, classism, colorism, heteronormativity, gender bias/sexism, lookism/sizeism, racism, and urbanormativity. While gaining expertise in these areas may seem daunting for the typical language teacher, simply reading up on any one of these issues is a step in the right direction and will provide increased awareness. The second action that can be taken is to become familiar with tools for detection of these issues. These include tools for understanding the manner of depictions, especially visual grammar and social semiotics in the case of visual artifacts and critical discourse analysis in the case of written texts. The act of becoming familiar with the forms of social group misrepresentation listed above will introduce the reader to important detection tools. There are some additional steps to be taken. One is for scholars in language education to make a greater effort to conduct research into social group representations in ELT materials. Another is for those engaged in training teachers to devote attention to this issue. Individuals involved with creating evaluation frameworks to be used for making decisions regarding the adoption of textbooks and other materials should strive to include social group representation as well. Without such changes, our efforts to promote linguistic authenticity via authentic materials may backfire, ironically promoting inauthentic—and marginalizing—social group representation. A final response is to train students themselves in critical media analysis; such a response dovetails aptly with the commitments of many formal curricula to media literacy and critical thinking. The issue of AI bias has engendered angst in popular discourse and among computer scientists. Search engine bias is recognized as one manifestation of this problem with social group representation looming especially large. Yet, ELT professionals routinely integrate materials sourced via search engines into their classes. Given the socialization power of multimedia materials, the workings of the hidden curriculum, and the ubiquity and power of ELT, social group representation is a pressing issue in teaching materials, especially those drawing upon search engine results. Probably most of us have at least a vague notion that search engine results are biased in some way. In order to act effectively, though, a systematic understanding must be developed in the professional educator. There is evidence, though, that such an understanding is currently insufficient within the field of ELT. Some might counter that by making efforts to actively promote fair social group representation, we are inserting ourselves into the political fray, deviating from some imagined mandate to “just teach language.” I hope that this discussion has successfully demonstrated the fallacy of such a stance since doing nothing may easily allow for the promotion of social injustice through the mechanisms discussed here. Charles Allen Brown is a lecturer in the Purdue University Language and Cultural Exchange program. His research focuses particularly upon the hidden curriculum in English language teaching (ELT). These efforts include drawing upon critical media studies techniques to investigate social group representation within ELT materials used in East Asia.