Charged 2026

Author: Chloe Smith

  • The Mystical World of Gluten Free Labeling – is it Lying?

    Odds are, as a consumer you’ve likely glanced at a food label in your lifetime. Though, for some, more than a quick glimpse is necessary, as the intricate details can be the difference between health or illness. This really applies to any allergy, as ingredients and wording can easily veil allergens. Specifically, this can be a regular practice for those avoiding gluten, as just a microscopic amount can have macroscopic repercussions. 

     

    What is gluten?

    Gluten is a protein found in wheat, primarily present as gliadin and glutenin. Synonymous proteins are present in rye as secalin, barley as hordein, and oats as avenins (Biesiekierski, 2017). Gluten is used as an umbrella term to describe each of the proteins, as each behave and interact in similar ways. Most commonly found in breads, pastas, and other dough based products, gluten acts as a binding agent. This means its presence impacts texture, elasticity, and even taste (Biesiekierski, 2017). These factors cause gluten to be found in less obvious products like seasonings, dressing, and even the remoistenable seal of an envelope. 

     

    To be or to not be free of gluten

    With the possibility of gluten being present in so many different forms, tracking its presence is crucial for those trying to avoid it. As declared by the Food and Drug Administration, for something to be labeled gluten free it must meet specific criteria to be considered not misbranded. The official ruling defines ‘gluten free’ as an item that doesn’t have a gluten containing grain, is not derived from a gluten containing grain, and an item with less than 20 parts per million of gluten (ppm) (Food and Drug Administration, 2013). The parts per million decree is pertinent when a gluten containing grain has been specifically adapted to contain not gluten. It essentially means that for every 1,000,000 grams of a substance, less than 20 of them are able to actually contain gluten. This microscopic amount pales in comparison to a single bread crumb or speck of flour, causing issue to another form of gluten possession, cross contamination. 

     

    Items naturally free of gluten such as meat or rice cannot confidently be deemed gluten free due to cross contamination. Cross contamination occurs when one substance is unknowingly transferred to another, in this case that substance is gluten. Transfer of gluten from a containing substance to supposedly gluten free product can happen at any point in the development process of a product (Damasceno et al., 2024). A single speck of wheat flour would be enough to contaminate a gluten free product. This means shared equipment, such as a conveyor belt holding glutenous food being used on a gluten free product could easily result in the surpassing of the 20 ppm maximum.

     

    External Certifications

    Beyond just an FDA approved gluten free marking on a product, there are many different types of certifications that may each follow their own thresholds for what is considered gluten free. Globally, these 3rd party certification processes vary in their guidelines to be included, with different organizations such as Beyond Celiac’s ‘Gluten-Free Certification Program’, Gluten Intolerance Group’s ‘Certified Gluten Free’ Stamp, or National Celiac Association’s gluten free certification. Each of these listings require different ppm of gluten, with one even having a maximum of 5 ppm, which is significantly lower than the FDA regulation (Dennis, 2018). The certifications can provide more comfort to consumers avoiding gluten, as they require more extensive testing of the products to confirm their gluten ppm. While the FDA decree sets limits for a company to legally consider its product gluten free, it does not necessarily require the manufacturer to test the product to confirm that (Thompson & Simpson, 2014). 

     

    How to Read a Label – A Guide from First Hand Experience

    As someone with Celiac Disease, with its only treatment being a strict gluten free diet, over the past 9 years of my diagnosis I’ve learned a few tricks along the way. Most notably, I would say being a conscious consumer has been the most impactful thing for my safety when selecting food items. If a product does not have a specific certification, meaning it is  just generally labeled gluten free or has no label at all, my first step is to read the ingredients. In my experience, there are a few specific keywords to look out for. In the process of scanning an ingredient list, if I notice words like wheat, barley, malt, or rye, off the bat the product can be considered not gluten free. Additionally, gluten can be hidden in certain ingredients like corn syrups, oats, yeasts, and some seasonings. These items aren’t inherently glutenous, but particular processes may make them unsafe. 

     

    In some cases, a search online is beneficial for me as well. Frequently, even though a label does not guarantee an item to be gluten free, the company will disclose on their website that their products are free of gluten. Oftentimes, the company will not officially disclose anything due to concern for cross contamination. The actual extent to which this is possible though varies by company. This is where the list at the bottom of a product saying ‘may contain wheat’ is notable. For some, ‘may contain’ could mean direct use of shared equipment, but for others it’s a disclaimer that the factory also makes gluten items. Research or contact with the company is sometimes the best option as it clarifies this uncertainty, and allows for a decision by the consumer depending on the extent of the allergy/sensitivity. Lastly, an item cannot just be deemed gluten free if it lacks a note saying it ‘contains wheat’, as that does not ensure it does not contain barley or rye, which are also gluten containing grains. 

     

    References

    Biesiekierski, J. R. (2017, February 28). What is gluten? – biesiekierski – 2017 – Journal of Gastroenterology … Wiley Online Library. http://onlinelibrary.wiley.com/doi/10.1111/jgh.13703/full

    Damasceno, R. P. B., Zandonadi, R. P., Mendes, M., Cunha Junior, L. C., Raposo, A., Teixeira-Lemos, E., Chaves, C., & Farage, P. (2024, April 18). Risk of gluten cross-contamination due to food handling practices: A mini-review. MDPI. https://www.mdpi.com/2072-6643/16/8/1198

    Dennis, M. (2018). Third Party certification of Gluten Free Foods. BIDMC of Boston. https://www.bidmc.org/centers-and-departments/digestive-disease-center/services-and-programs/celiac-center/celiacnow/nutrition-and-the-gluten-free-diet/label-reading/third-party-certification-of-gluten-free-foods

    Food and Drug Administration. (2013, August 5). Food Labeling; Gluten-Free Labeling of Foods. Federal Register – National Archives. https://www.federalregister.gov/documents/2013/08/05/2013-18813/food-labeling-gluten-free-labeling-of-foods

    Thompson, T., & Simpson, S. (2014, October 1). A comparison of gluten levels in labeled gluten-free and certified gluten-free foods sold in the United States. Nature News. https://www.nature.com/articles/ejcn2014211 

  • The Uncanny Valley – An Exploration of How Humans Perceive AI

    Have you ever seen a humanoid design that looks just off? Whether it’s the subtle placement of facial features, an abnormal glint in the eyes, or even just a subconscious lurking feeling that something’s not right, you’ve likely experienced the sensation often described as the uncanny valley.  

     

    What is the uncanny valley?

    The concept of the uncanny valley was first introduced in the early 1970s by a robotics professor, Masahiro Mori. Mori describes the term as an irregular representative of a linear relationship, stating, “as robots appear more humanlike, our sense of their familiarity increases until we come to a valley.” (Mori et al., 1970) This ‘valley’ he refers to is a point in the relationship between human ‘likeness’ and familiarity in which familiarity plummets before increasing again. Mori designed a graphical relationship indicating this phenomenon, which can be viewed in the image below. This dynamic can be even further exacerbated by attentional human-like cues added, such as movement or touch sensation (Mori et al., 1970). 

     

     

    (Original diagram created by Masahiro Mori, who coined the uncanny valley term in the early 70s.) Source: Wikimedia Commons [https://commons.wikimedia.org/w/index.php?curid=2041097]

     

    The actual origin of the uncanny valley is a question sought after and theorized on by many researchers. A few common hypotheses about its origin have been proposed though. Some examples that will be further discussed below are categorization uncertainty and perceptual mismatch. Unfortunately these hypotheses and actual root of the phenomenon is under researched area, making it difficult to tie a definitive cause (Kätsyri et al., 2025). 

     

    Categorical perception deals with one’s predisposition to better perceive something if it distinctly falls into a mental category. This causes things felt to fall into discrete categories to be highlighted in their uniqueness, and those that remain similar to have an understated emphasis on difference (Goldstone & Hendrickson, 2009). The hypothesis is that a cognitive dissonance is created when the uncanny valley is reached in a humanoid design. Essentially, at this stage, there is a difficulty in the mental categorization process, with ambiguity in the design making it hard to determine if the figure falls into the ‘human’ category or the ‘robot/inhuman’ category. This uncertainty is thought to bring the discomforting feeling described by the effect (Kätsyri et al., 2025).

     

    Another hypothesis is rooted in the idea of perceptual mismatch. This theory follows a similar line of reasoning to the categorization uncertainty hypothesis, being that the uncanny valley effect is thought to be brought on by a feeling of discomfort that arrives by the brain’s difficulty to discern something. In this case, it relates to the perceptual confusion brought along by something with high human likeness with a feature that is inhuman or artificial in appearance. The discrepancy between a primarily human perception and an identifying feature that is misaligned with that is thought to create the uncomfortable feeling of uncanniness (Kätsyri et al., 2025). 

     

    Uncanny Valley and AI

    As time has gone on, the general public has witnessed the rapid development of technology. With our world’s rising implementation of artificial intelligence (AI) run programs, generative technologies, and interfaces, the question arises, how does this concept intersect with AI? Can the uncanny valley phenomenon expand beyond visual design?

     

    A recent study explores these perceptions in action and how it affects user interaction and emotional responses. Deepali Kishnani, a student at MIT, employed an experimental structure that examined text interactions between participants and 3 variables – a bot engineered to be ‘human-like’, a bot engineered to be ‘uncanny-like’, and a human for control. After conversing with one of the 3, the participant ranked the system on anthropomorphism, animacy, likeability, perceived intelligence, and safety (Kishnani, 2025). 

     

    Based on the rating and analysis of the results, it was concluded that there is evidence that the uncanny valley is present in text-based interaction, as the ‘uncanny-like’ bot was consistently rated lowest on all dimensions. Findings also revealed that people are having an increasingly more difficult time distinguishing human interaction from AI interactions, as 60% of participants in the human control group incorrectly identified their chat companion as AI (Kishnani, 2025).

     

    With the now confirmed presence of possible uncanny valley in AI chatbot interactions, an additional question of how it influences trust from users arises. Another study explores this concept, seeking how the anthropomorphism of AI systems induces concern into the patron, instigating fear of human-natured negative actions onto the inhuman bot (Kim et al., 2024). This study specifically introduces a concept known as ‘uncanny valley of the mind,’ which focuses more on human-like intentions rather than human-like appearance. They suggest that the uncanny valley of the mind is brought on when a chatbot is perceived to have human-like mentalites (also known as a ‘digital mind’), they will also possess human-like intentions- such as the ability to misuse information (Kim et al., 2024).

     

    To test this phenomenon, researchers created 2 chatbots that could be accessed through facebook messenger, one ‘anthropomorphized’ and one ‘non-anthropomorphized.’ Through a series of interactions, the bot asked for personal information and later synthesized either an ad containing that information or a generic ad without it. Interestingly, when the anthropomorphized bot presented the user with a personalized ad, participants reported increased privacy concerns. This effect was not present in the non-anthropomorphized ads, regardless of it being the personalized or non-personalized ad. With this being said, it was determined that higher anthropomorphism in chatbots decreases consumer trust/likelihood to purchase a product when personal information is included in the pitch (Kim et al., 2024).

     

    What Does this Mean for the Future of AI?

    It seems that as the days go on, more applications of AI are created and present in a multitude of functions. As implementations into features like writing programs, customer service, and ad-personalization grows, opportunity for distrust in these systems increases along with it. The research above proves that the uncanny valley can seep into perceptions of these AI powered systems, so how will producers handle this realization?

     

    In my opinion, there are two ways this industry can progress to mediate this issue from a psychological standpoint. Either producers will make efforts to develop AI systems that are less uncanny and more perceptual humans or consumers will begin to gravitate away from trust. Historically both of these things have occurred in product development and examples regarding the uncanny valley can even be observed. 

     

    A great exemplification of both of these mediations is the design of ‘Sonic the Hedgehog’ in the 2020 film rendition. When the first trailer released, fans of the franchise were horrified by the uncanny design of their beloved main character. Sonic, a very cartoonish character, had been adapted to his life action role by combining realistic CGI texturing and a flurry of human-like features on a very non-human creature. The uproar after the release was instant, with many fans taking to social media to detest the movie and state their refusal to endorse it. (Alexander, 2020) This is an example of the consumers gravitating away from trust. Producers were then forced to change the design to be more appealing and cartoonish, exemplifying the need for producer design in a way that is more appealing to consumers. 

     

    The true outcome of how AI develops can only be told by time. Until then, be a critical consumer. Do you find yourself experiencing a lurking, unsettling, off feeling when chatting with AI systems?

     

    References:

    Alexander, B. (2020, February 17). How “Sonic the Hedgehog” avoided death by Twitter with a dramatic, game-saving redesign. USA Today. https://www.usatoday.com/story/entertainment/movies/2020/02/13/sonic-the-hedgehog-movie-redesign-how-backlash-made-character-better/4730593002/

    Goldstone, R. L., & Hendrickson, A. T. (2009, December). Categorical perception. WIREs Cognitive Science. https://wires.onlinelibrary.wiley.com/doi/10.1002/wcs.26

    Kim, W., Ryoo, Y., & Choi, Y. K. (2024). That uncanny valley of mind: When anthropomorphic ai agents disrupt personalized advertising. International Journal of Advertising, 1–30. https://doi.org/10.1080/02650487.2024.2411669

    Kishnani, D. (2025, March 7). The uncanny valley: An empirical study on human perceptions of AI-generated text and images. The Uncanny Valley: An Empirical Study on Human Perceptions of AI-Generated Text and Images. https://dspace.mit.edu/handle/1721.1/159096

    Kätsyri, J., Förger, K., Mäkäräinen, M., & Takala, T. (2025, November 1). A review of empirical evidence on different uncanny valley hypotheses: Support for perceptual mismatch as one road to the valley of eeriness. Frontiers. https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2015.00390/full#B19

    Mori, M. (1970). The Uncanny Valley. https://www.almendron.com/tribuna/wp-content/uploads/2018/01/morunc.pdf