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Exploring the Ghost in The Machine: Detecting Racial Bias in AI

Recent studies have raised concerns over the potential for racial bias in AI applications used in classrooms, particularly applications like ChatGPT. AI language models, powered by data trained on billions of words, can be susceptible to biases that are embedded in this source material. Teachers and educators looking to integrate AI into the classroom should be keenly aware of this issue.

The Educational Testing Service (ETS), a well-esteemed testing organization, investigated racial bias in AI through a unique experiment: pitching man against machine in grading more than 13,000 student essays. Findings indicate that AI graded Asian American students’ work lower than their non-Asian counterparts. This discrepancy was due to no discernible reason, as the algorithm was not privy to the students’ racial backgrounds.

Experts such as Mo Zhang, one of the ETS researchers, caution users to thoroughly examine AI-generated results before sharing them with students. Given that AI operates like a “giant black box,” its intricate algorithms are not fully understood even by the creators, making it difficult to predict or justify its output.

While this study is not definitive proof of AI bias, it is an essential reminder of the need for continual scrutiny and evaluation to ensure the technology we employ is equitable and fair.

Considering using AI in your classrooms? Always remember, human oversight is key to preventing undue harm from technology. AI is an excellent tool, but should it be left to decide our students' fate unchecked?

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#AIinEducation #ChatGPT #RacialBias #EdTech

AI Bias in Classrooms: Shocking Study! by Steven's Workspace

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