# AI & Ethics ## expand\_more While AI offers promising possibilities for enhancing learning, it also raises significant concerns around academic honesty, bias, privacy, and environmental sustainability. ## **Academic Integrity** One ethical concern with AI in the classroom involves [academic integrity](https://oue.fas.harvard.edu/academic-integrity-harvard-college). AI tools, such as ChatGPT, generate essays, solve problems, and produce summaries rapidly, which could lead to misuse by students seeking shortcuts. Compounding this issue is [the lack of reliable tools](https://edscoop.com/ai-detectors-are-easily-fooled-researchers-find/) to determine whether content is AI-generated. [This presents a challenge to academic integrity](https://www.insidehighered.com/opinion/views/2024/10/22/your-ai-policy-already-obsolete-opinion) as AI can blur the line between legitimate assistance and [academic dishonesty](https://www.insidehighered.com/news/tech-innovation/artificial-intelligence/2024/07/29/students-and-professors-expect-more). ### **Recommendations:** - **Include an AI policy on your syllabus.** Be transparent with students about why you are asking them to complete a particular assignment and explain how using or not using AI tools will affect those goals. - **Require students to disclose their use of AI**, whether for [brainstorming, drafting, or other purposes.](https://www.newyorker.com/culture/annals-of-inquiry/what-kind-of-writer-is-chatgpt?/) - If students are not permitted to use AI, **design assignments that minimize AI’s utility**, such as personalized reflections, oral presentations, or in-class tasks, ensuring that students engage deeply with the material and demonstrate their own understanding. - **Be transparent about your use of AI**, whether in preparing materials, generating content, providing feedback, and any other uses you’ve found beneficial. AI here could be framed as a tool to support learning. ## **Key Debates and Ethical Questions:** ### **Bias and Fairness in AI Systems** AI systems are trained on [vast datasets](https://ig.ft.com/generative-ai/)– predominantly collected from the internet– and therefore [incorporate the biases and stereotypes](https://www.insidehighered.com/blogs/beyond-transfer/toward-ethical-and-equitable-ai-higher-education) embedded in those datasets. AI-generated content reflects dominant cultural norms and can pose the risk of marginalizing or misrepresenting, reinforcing harmful stereotypes and perpetuating inequality. For the same reason, the use of AI might also undercut the learning goals of intellectual vitality. [Intellectual vitality](https://intellectualvitality.college.harvard.edu/our-commitment/) encourages students to question assumptions and resist arriving at premature conclusions— two areas where AI-generated output often falls short. Another consideration is how certain AI tools can disadvantage certain student groups. For instance, AI systems that process language may struggle with non-standard dialects or multilingual speakers, leading to inaccuracies or misunderstandings. [Vigilance in recognizing and addressing AI’s limitations](https://www.sciencedirect.com/science/article/pii/S2667096823000125) is essential in diverse classroom settings. ### **Privacy and Data Security** Using AI in educational settings [raises concerns about privacy and data security](https://edtechmagazine.com/higher/article/2024/06/data-security-best-practices-ai-tools-higher-education). Many AI tools require users to input personal information or academic work into platforms that collect and store data. In some cases, this data may be used for purposes beyond the immediate educational context, such as marketing or [further training of AI models](https://www.wired.com/story/how-to-stop-your-data-from-being-used-to-train-ai/), often without the user's explicit consent. This raises ethical questions regarding the ownership and control over one's intellectual property and private information. To combat this, FAS members are encouraged to use Harvard-approved tools (Harvard’s [AI Sandbox](https://huit.harvard.edu/ai-sandbox) and [ChatGPT Edu](https://it.fas.harvard.edu/openai-chatgpt-edu/) Workspace). These options allow for the upload of confidential materials (specifically, those materials [Level Three](https://policy.security.harvard.edu/level-3#:~:text=Level%203%20On%20Systems,applicable%20Harvard%20data%20protection%20requirements.) and below). ### **Environmental Impact** Training and running large AI models require [substantial computational power,](https://www.newyorker.com/news/daily-comment/the-obscene-energy-demands-of-ai) which in turn consumes significant energy. This energy consumption is not unique to AI; many digital processes, such as video streaming and cloud storage, also demand significant resources. [But as AI use expands ](https://www.technologyreview.com/2023/12/01/1084189/making-an-image-with-generative-ai-uses-as-much-energy-as-charging-your-phone/)in education, [its contribution to carbon emissions and other environmental harms likewise grows](https://time.com/6987773/ai-data-centers-energy-usage-climate-change/). ### **Copyright and Intellectual Property** Generative AI tools rely on massive datasets that include [copyrighted material.](https://issues.org/generative-ai-copyright-law-crawford-schultz/#:~:text=Generative%20AI%20systems%20have%20used,without%20permission%20in%20some%20cases.) This can raise ethical and legal concerns around [ownership, use, and attribution of content produced with AI.](https://www.theatlantic.com/technology/archive/2024/07/perplexity-ai-search-media-partners/679294/)