Recent publiceerden enkele grootheden in het onderwijsonderzoek een working paper over AI met aanbevelingen die veel verder gaan dan onderwijs. Arran Hamilton, John Hattie en Dylan Wiliam geven 13 aanbevelingen over hoe we de schade van AI in onze samenleving kunnen beperken:

1. We should work on the assumption that we may be only two years away from Artificial General Intelligence (AGI) that is capable of undertaking all complex human tasks to a higher standard than us and at a fraction of the cost. Even if AGI takes several decades to arrive, the incremental annual improvements are still likely to be both transformative and discombobulating.
2. Given these potentially short timelines, we need to quickly establish a global regulatory framework–including an international coordinating body and country-level regulators
3. AI companies should go through an organizational licensing process before being permitted to develop and release systems ‘into the wild’ – much like the business/product licensing required of pharmaceutical, gun, car, and even food manufacturers.
4. End-user applications should go through additional risk-based approvals before being accessible to members of the public, similar to what pharmaceutical companies need to do to get drugs licensed. These processes should be proportionate with the level or risk/harm – with applications involving children, vulnerable or marginalized people being subject to much more intensive scrutiny.
5. Students (particularly children) should not have unfettered access to these systems before risk-based assessments/trials have been completed.
6. Systems used by students should always have “guardrails” in place that enable parents and educational institutions to audit how and where children are using AI in their learning. For example, this could require permission from parents and school prior to being able to access AI systems.
7. Legislation should be enacted to make it illegal for AI systems to impersonate humans or for them to interact with humans without disclosing that they are an AI.
8. Measures to mitigate bias and discrimination in AI systems should be implemented. This could include guidelines for diverse and representative data collection and fairness audits during the LLM development and training process.
9. Stringent regulations around data privacy and consent, especially considering the vast amounts of data used by AI systems. The regulations should define who can access data, under what circumstances, and how it can be used.
10. Require AI systems to provide explanations for their decisions wherever possible, particularly for high-stakes applications like student placement, healthcare, credit scoring, or law enforcement. This would improve trust and allow for better scrutiny and accountability.
11. As many countries are now doing with the Internet systems, distributors should be made responsible for removing untruths, malicious accusations, and libel claims – and within a very short time of being notified.
12. Establish evaluation systems to continuously monitor and assess the safety, performance, and impact of AI applications. The results should be used to update and refine regulations accordingly and could also be used by developers to improve the quality and usefulness of their applications – including for children’s learning.
13. Implement proportionate penalties for any breach of the AI regulations. The focus could be creating a culture of responsibility and accountability within the AI industry and end-users.