Image filters and racial standardization
reflections on algorithmic racism, augmented reality, and English language teaching
DOI:
https://doi.org/10.46230/lef.v17i3.16068Keywords:
image filters, artificial intelligence, augmented reality, algorithmic racismAbstract
The use of image filters, enhanced by artificial intelligence (AI) technologies, has become a common practice on social media. Built with Augmented Reality (AR) technologies, these filters overlay digital elements onto the physical world in real time (Peddie, 2017) and, while entertaining, reinforce exclusionary aesthetic standards (Noble, 2018). By favoring whitened features, such tools can negatively impact the self-esteem of Black individuals, producing forms of algorithmic racism (Silva, 2020). Motivated by the frequent use of these technologies by Black students, this article seeks to understand how a decolonial education (Canagarajah, 1999; Josiowicz et al., 2025; Landulfo; Matos, 2022) can contribute to the deconstruction of racist stereotypes reproduced by digital tools. To this end, we developed an autoethnographic essay (Chang, 2008; Adams; Jones; Ellis, 2015; Silva, 2022), with a qualitative and descriptive approach (Paiva, 2019), dialoguing with our pedagogical and digital experiences. The didactic sequence constructed fostered a debate on racial identity and image, revealing that Black students feel either made invisible or altered by filters that do not recognize their features. We also identified that the critical use of the English language, combined with sensitive listening and the analysis of visual codes, fosters the reframing of these experiences. We conclude that it is through dialogue, representativity, and critical inquiry that decolonial education can transform the English language classroom into a space of resistance, belonging, and identity reconstruction (Pinheiro, 2023).
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