Chapter 9 References — Responsible AI¶

Curated resources for deeper exploration of the topics in this chapter.

Books¶

O'Neil, Cathy. (2016). Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. Crown. Documents how algorithmic systems can amplify existing inequities — a concrete foundation for understanding algorithmic bias in school AI deployments.
Eubanks, Virginia. (2018). Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor. St. Martin's Press. Examines how automated decision tools systematically disadvantage marginalized communities, with direct relevance to AI-driven student support systems.
Benjamin, Ruha. (2019). Race After Technology: Abolitionist Tools for the New Jim Code. Polity Press. Analyzes racial bias embedded in algorithmic systems and argues for equity-centered design principles essential to responsible AI in schools.

U.S. Department of Education. (2023). "Artificial Intelligence and the Future of Teaching and Learning." ed.gov. https://www.ed.gov/sites/ed/files/documents/ai-report/ai-report.pdf Dedicates substantial sections to responsible AI use, hallucination risks, and academic integrity guidance, making it the primary government reference for this chapter.
NIST. (2023). "AI Risk Management Framework." nist.gov. https://www.nist.gov/system/files/documents/2023/01/26/AI%20RMF%201.0.pdf The U.S. government's authoritative framework for identifying and mitigating AI risks, including fairness and bias categories applicable to education settings.
Future of Privacy Forum. (2023). "Student Privacy and Ed Tech." fpf.org. https://fpf.org/education/ Practical guidance on FERPA, COPPA, and student data privacy as they apply to AI tools in schools.
ACLU. (2022). "Chatbots in the Classroom: AI Academic Integrity Issues." aclu.org. https://www.aclu.org/report/technology-education Examines academic integrity implications of generative AI and the civil liberties dimensions of AI detection tools.
Turnitin. (2023). "AI Writing and Academic Integrity." turnitin.com. https://www.turnitin.com/blog/ai-writing-and-academic-integrity Explains how AI detection tools work and their known limitations, helping administrators set realistic expectations for academic integrity policies.

AI4K12. (2024). Responsible AI Module. https://ai4k12.org/ Free, standards-aligned curriculum for teaching responsible AI concepts in K-12 classrooms, directly supporting this chapter's guidance on building AI literacy alongside AI tools.
Common Sense Media. (2024). AI and Kids: What Parents and Educators Need to Know. https://www.commonsensemedia.org/technology-addiction/artificial-intelligence Accessible guidance on AI risks for children and adolescents, including skill atrophy and over-reliance concerns.
UNESCO. (2023). Recommendation on the Ethics of AI. https://www.unesco.org/en/artificial-intelligence/recommendation-ethics UNESCO's global ethical framework for AI, covering fairness, transparency, and human oversight — the international policy context for responsible AI in schools.
Student Privacy Compass. (2024). FERPA and COPPA Overview. https://studentprivacy.ed.gov/ The U.S. Department of Education's free guidance hub for school compliance with the federal student privacy laws central to this chapter.

Coded Bias. (2020). Coded Bias Documentary. Netflix/Independent. https://www.codedbias.com/ A documentary film about facial recognition bias that provides a compelling, accessible introduction to algorithmic fairness concepts suitable for showing to parent and board audiences.