10.4: What can be done about it?

I want to end this post – and this series – on a hopeful note. There’s no denying that Generative AI and related technologies have the potential to positively impact the education system. The same could easily be said about any technology, from writing tools to word processors, PCs to smartphones. The issue – as with all of these technologies – is not whether they can improve learning, but how they are used.

If all we use AI for is efficiency, then we’re heading towards EdTech v2.0. Over the past couple of decades we’ve seen wave after wave of technology that promised enormous gains to learning, but delivered very little. We’ve heard the phrase “this will revolutionise education” again and again, and in general the education system – and its flaws – has proven to be extremely robust. We are now starting to see the potential negative impact of AI technologies, including from datafication, predictive profiling, and the potential for generative AI to perpetuate bias.

So, to counteract the entrenchment of existing power structures, and the centralising of wealth in the hands of the already wealthy, seems like a huge challenge and not something that can be tackled by educators.

But Artificial Intelligence isn’t EdTech. It’s not an app or a piece of equipment, or even a single system. It is a complex infrastructure that will ultimately be woven through all of the technologies we already use, and those on the horizon. And importantly, it’s not quite fully established in education yet, which gives us an opportunity for critique. This series of posts aimed to support that critique by engaging students and educators across different curriculum areas in meaningful discussions about AI and the future of education.

Here are five final practical ideas for Teaching AI Ethics:

1. Develop Clear Policy: As a school leader, it’s important to acknowledge the ethical complexities of AI and develop a comprehensive policy that strives to mitigate these issues. Here’s how you can approach it:
   • Start with an open discussion among staff, students, and community to understand their concerns about AI. Use these discussions to identify key issues that your policy should address.
   • Clearly outline the roles and responsibilities of all stakeholders in adhering to the policy.
   • Define what AI tools are acceptable within your institution and under what circumstances. This can include specifics about data collection, use, and storage.
   • Regularly review and update the policy as new AI technologies and ethical challenges arise.
2. Data Privacy Activity: Understanding the nuances of privacy policies can be challenging, even for adults. Here’s a practical way to involve students:
   • Organise a workshop where students dissect the privacy policies of common AI platforms they use. Guide them to understand how their data is collected, used, and protected.
   • As a follow-up activity, students could create ‘ideal’ privacy policies for an AI product, incorporating the most ethically robust components of the policies they’ve studied.
3. Understanding the Human Labour Behind AI: Many AI models rely heavily on human input and labour, often from individuals in developing countries who are paid low wages to do time-consuming and sometimes distressing work. This often-invisible labour is used in training these models and cleaning up their outputs. Here’s a possible approach to incorporate this issue into the curriculum:
   • Initiate a study and discussion on the topic of “free labour” in AI. This should include research on the lives and working conditions of the data annotators whose work powers many AI models, with a specific focus on the exploitative labour practices mentioned in the aforementioned article.
   • In conjunction with this, students could research and discuss the notion of “data colonialism” and how the data of individuals is used (often without their knowledge or consent) to train AI models, benefiting companies but often not the individuals themselves.
   • To make this issue more tangible, students could examine the data they generate in their own lives and discuss how it might be used to train AI models. This could involve looking at their own digital footprints, understanding what kind of data they generate, and discussing how this data could be used in AI training.
   • Lastly, encourage students to come up with ideas for regulations or systems that could make the process more equitable, such as ways to provide transparency about how data is used, or methods to share the revenue generated from this data with the people who produced it.
4. Interrogating AI Platforms: Understanding AI ethics policies can help students and educators make informed decisions about which platforms to use and how to use them ethically.
   • Assign students to research the Responsible AI and Ethical AI policies of various AI platforms and services.
   • Ask them to present a report on their findings, discussing what these policies mean, how they could be improved, and the potential implications for users.
   • Incorporate these findings into a larger discussion on AI ethics in the classroom, allowing students to understand the real-world implications of these policies.
5. AI for Good Project: Deliberately focusing on the potential positive applications of AI can raise important ethical questions about current uses of the technology.
   • Ask students to design an AI solution for a societal problem they care about. This could be a local issue (e.g., reducing traffic in their town) or a global one (e.g., predicting natural disasters).
   • As part of the project, students should also conduct an Ethical Impact Assessment. This assessment should detail potential ethical issues related to data privacy, bias, and societal impact, and propose strategies to mitigate these issues.
   • Through this project, students will not only gain practical experience in AI but also learn about the ethical considerations that are crucial to responsible AI development and use.