Why ethical AI requires a future-ready and inclusive education system

The recent development of generative artificial intelligence (AI) has led to a surge in the adoption of AI across a wide range of industries, from healthcare to marketing. But with it, there is ample evidence of discriminatory harm that AI tools can cause to already marginalised groups through unconscious biases in algorithms and lack of representation in data sets. AI also has varied potential impacts on jobs and labour, with marginalised groups being more susceptible to job loss or displacement due to automation.

The World Economic Forum’s Future of Jobs Report 2023 indicates that 44% of workers’ core skills are expected to change in the next five years alongside increased tasks completed by machines. With generative AI, recent research done by OpenAI concluded that approximately 80% of the US workforce would have some share of work affected by GPTs (Generative Pre-trained Transformer - a language model capable of producing human-like text) and about 19% of workers will have a significant share of their work affected.

Research also indicates that the impact of AI on jobs and labour is more likely to have negative consequences for women, racialised, indigenous, and low-income groups. Eric Brynjolfsson, director of the Stanford Digital Economy Lab, noted that while automation by AI can increase productivity and wealth, the benefits disproportionately go to those with resources that are not easily replaced with technology – i.e. unique assets, talents or skills. A spiral of growing marginalisation can occur in the already disadvantaged communities due to their low literacy on AI.

There are several factors at the confluence of well-being and marginality, of which education and knowledge are key prerequisites for improved access to employment and business opportunities.

With the rapidly growing number of tasks being automated and augmented by AI, employment and business opportunities inevitably evolve and make the ability to use AI technologies a vital qualification. However, the current education and training system is inadequate to prepare everyone for this transformational journey.

Groups with fewer resources are likely to be directly or indirectly excluded and marginalised. For example, children in marginalised communities have lower access to advanced technologies and AI education opportunities. When their more affluent peers have iPads and laptops, start coding games and learn about AI basics, they usually do not have this degree of exposure to the latest technologies and curricula.

Therefore, the ethical use of AI not only includes understanding the social implications of AI and harnessing the fair use of data, but also entails education and training opportunities that are accessible, fair, and diverse. In tandem with rapid advances in AI, education systems must keep pace with this transformation to ensure future-ready and inclusive curricula.

While national AI strategies have a section on reskilling and preparing the workforce, they must emphasise public education systems. Improving public AI literacy should be highlighted, with funding dedicated to uplifting the public education system and upskilling public school teachers.

Education authorities should work with schools, teachers and communities to create practical guidelines and concrete plans to make AI basics and digital literacy part of the core curricula. It is also beneficial to introduce the basics of AI, digital literacy, critical thinking and innovation to students early in their education, such as during their secondary or even primary school education.

A few examples of how AI education can be integrated into educational journeys:

To achieve equitable and inclusive AI, it is essential for all those interacting with this technology to understand and collaborate effectively with this and be aware of the opportunities and risks posed by its use. No matter which educational avenue learners go through, an inclusive and tailored learning experience is critical to improving knowledge uptake and preventing dropouts. This includes considerations in the context of AI education and the delivery of the content. For example:

While we hope for a wealthier and more productive future with AI, there is still a long way before everyone is equipped with sufficient knowledge and skills to reap its benefits. As AI develops, it's time to act on inclusive and future-ready education systems that pave the way for a more equitable future.