Ethics and Bias

In this guide: The AI writing report File requirements What is qualifying text? How do we detect AI-generated writing? Turnitin's AI writing detection capability is designed to help educators ide...

School districts from Utah to Ohio to Alabama are spending thousands of dollars on these tools, despite research showing the technology is far from reliable.

Putting the AI in Charge

This paper examines pedagogical biases in AI-powered educational tools, focusing specifically on lesson plan generators. We investigate how these tools may implicitly embed outdated educational approaches that limit student agency and classroom dialogue. Through analysis of 90 lesson plans from commercial lesson plan generators, we found that AI-generated content predominantly promotes teacher-centered classrooms with limited opportunities for student choice, goal-setting, and meaningful dialogue. To mitigate this issue, we further experimented with intentional prompt engineering, which showed promise in significantly enhancing these dimensions in AI-generated lesson plans. We offer practical strategies for educators and developers to mitigate harmful pedagogical biases while promoting contemporary educational values. This work contributes to the critical conversation about how AI tools should be designed and used to support, rather than undermine, the future of education that values student agency and productive classroom dialogue.

Digital Democracy overview of bill AB 412: Generative artificial intelligence: training data: copyrighted materials.

Have you ever looked at a piece of writing and thought something might be "off"? It might be hard to pinpoint exactly what it is. There might be too many adjectives or the sentence structure might be ...

The effort it will take to make ChatGPT write an essay paragraph by paragraph is not worth getting caught cheating. Here's what to do instead.

When artificial intelligence models pore over hundreds of gigabytes of training data to learn the nuances of language, they also imbibe the biases woven into the texts.

When asked, ChatGPT declared that its training material—the language we humans use every day—was to blame for potential bias in stories it generated

The rapid adoption of generative language models has brought about substantial advancements in digital communication, while simultaneously raising concerns regarding the potential misuse of AI-generated content. Although numerous detection methods have been proposed to differentiate between AI and human-generated content, the fairness and robustness of these detectors remain underexplored. In this study, we evaluate the performance of several widely-used GPT detectors using writing samples from native and non-native English writers. Our findings reveal that these detectors consistently misclassify non-native English writing samples as AI-generated, whereas native writing samples are accurately identified. Furthermore, we demonstrate that simple prompting strategies can not only mitigate this bias but also effectively bypass GPT detectors, suggesting that GPT detectors may unintentionally penalize writers with constrained linguistic expressions. Our results call for a broader conversation about the ethical implications of deploying ChatGPT content detectors and caution against their use in evaluative or educational settings, particularly when they may inadvertently penalize or exclude non-native English speakers from the global discourse. The published version of this study can be accessed at: www.cell.com/patterns/fulltext/S2666-3899(23)00130-7

How can AI/ML systems be used in a responsible and ethical way that deserves the trust of users and society?

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