Bridging literature on whiteness and racialization with theories on habitus, I argue that white antiracist organizations act as incubators for white people to learn the “high culture” of the racial justice field, which is domin...
Racial Formation, Implicit Understanding, and Problems with Implicit Association TestsAlexis ShotwellI. In t r o d u c tI o nIn this paper, I argue that the concept of ‘implicit understanding’ includes af-fective, bodily, presuppositional, and socially-situated forms of understanding. I claim...
This restart comes with a projection of the subspace using a rational function. In this paper, it is shown how the restart can be worked out in practice. In a second part, it is shown when the filtering of the subspace basis can fail and how this failure can be handled by deflating a...
53] with adversarial designs that generate latent features or synthetic data from the original training data [54,55]. The most common implementations are variational autoencoders (VAEs) [56,57] based models or generative adversarial networks (GANs) [58] based models. VAE models require distributi...
ChatGPT has also been discovered to have some issues when it comes to racial and gender biases associated with the chatbot. Implicit bias built into technology is far from a new concept; however, UC Berkeley psychology and neuroscience professor Steven Piantadosishared on Twitterin early December ...
Based on research findings on association between problems in memory subtypes and psychiatric disorders, there are explicit memory biases towards anxious or traumatic stimulus in panic disorder and posttraumatic stress disorder, while there is an implicit memory bias in generalized anxiety disorder. In ...
Token prediction as implicit classification to identify LLM-generated text. Yutian Chen, Hao Kang, Vivian Zhai, Liangze Li, Rita Singh, and Bhiksha Raj. arXiv preprint arXiv:2311.08723 (2023) [link] TOPFORMER: Topology-Aware Authorship Attribution of Deepfake Texts with Diverse Writing Styles....
And there have been severe problems with gender bias in the Amazon AI curriculum selection [2]. In fact, machine learning models are so widely deployed in our society that, without fairness protection, we may find the impacts of discrimination to be catastrophic [3–8]. Fortunately, studies ...
with disabilities, storytelling in teaching, real-world application, mentorship, stereotype threat mitigation, and technology use. Additionally, it covers lifelong learning, student leadership, diverse assessment methods, emotional intelligence, digital literacy, collaborative learning, implicit bias awareness, ...
sketched out, with five lines of enquiry noted as being particularly promising: expanding the concept of social identity; predicting comparison choice in intergroup settings; incorporating affect into the theory; managing social identities in multicultural settings; and integrating implicit and explicit ...