This article is made possible thanks to collaboration between The European Sting and the World Economic Forum.
Authors: Hannah Rosenfeld, World Economic Forum Artificial Intelligence and Machine Learning Specialist, Danni Yu, World Economic Forum Artificial Intelligence and Machine Learning Project Fellow, Boston Consulting Group Consultant, Abhishek Gupta, Senior Responsible AI Leader and Expert Consulting, Boston Group (BCG), Founder and Principal Investigator, Montreal Institute for AI Ethics
- Studies have found that socially marginalized groups are particularly vulnerable to job losses and displacement from automation.
- We must address the fundamental issue of AI education and upskilling for these underrepresented groups.
- Stakeholders in the public and private sectors can work together to ensure a fair future for all.
Recent developments in generative artificial intelligence (AI) have seen a surge in AI adoption across industries ranging from healthcare to marketing. However, there is ample evidence that AI tools can cause discriminatory harm to already marginalized groups through unconscious bias in algorithms and lack of representation in datasets. AI is also having a range of potential impacts on employment and labor, leaving marginalized groups vulnerable to job losses and displacement from automation.
of the World Economic Forum Future Jobs Report 2023 It shows that 44% of core employee skills are expected to change over the next five years as more tasks are completed by machines. A recent study by OpenAI found that about 80% of the U.S. workforce is doing some jobs influenced by GPT (Generative Pre-trained Transformer – a language model that can generate human-like text) when it comes to generative AI. It concludes that the remaining approximately 19% are affected. A significant portion of workers’ jobs will be affected.
Studies also show that the impact of AI on jobs and work is likely to have negative impacts on women, racism, indigenous peoples, and low-income groups. Eric Brynjolfsson, director of the Stanford Digital Economy Lab, says AI-powered automation can increase productivity and wealth, but the benefits come from resources that technology cannot easily replace: those with unique assets, talents and skills. He pointed out that it was disproportionate to the people. Low AI literacy can create a spiral of increasing alienation in communities already disadvantaged.
Ethical AI also means future-ready comprehensive AI education and training
There are several factors at the intersection of well-being and alienation, of which education and knowledge are important prerequisites for improving access to employment and business opportunities.
As the number of tasks automated and enhanced by AI rapidly increases, employment and business opportunities will inevitably evolve, making the ability to use AI technology a key qualification. However, current education and training systems are insufficient to prepare everyone for this transformational journey.
Groups with low resources may be directly or indirectly excluded and marginalized. For example, children in marginalized communities have less access to advanced technology and AI educational opportunities. A wealthier colleague might have her iPad or laptop and start coding games or learning the basics of AI, but they usually aren’t as exposed to the latest technology and curriculum.
Ethical use of AI therefore not only includes understanding the social impact of AI and fair use of data, but also comes with accessible, equitable and diverse education and training opportunities. Alongside rapid advances in AI, education systems must also keep pace with this transformation to ensure a comprehensive and future-proof curriculum.
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Ethical use of AI not only includes understanding the social impact of AI and fair use of data, but also comes with accessible, fair and diverse education and training opportunities. ”
Publicly accessible education and training should be included on national AI agendas
The National AI Strategy has a section on workforce reskilling and preparation, but the focus should be on the public education system. We need to focus on improving public AI literacy by investing in improving the public education system and upskilling public school teachers.
Education authorities should work with schools, teachers and communities to develop practical guidelines and concrete plans to make AI fundamentals and digital literacy part of the core curriculum. Introducing the foundations of AI, digital literacy, critical thinking, and innovation to students early in their education, such as in secondary and primary school, is also beneficial.
Here are some examples of how AI teaching can be integrated into educational activities.
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How does the World Economic Forum ensure the ethical development of artificial intelligence?
The World Economic Forum’s Center for the Fourth Industrial Revolution brings together stakeholders around the world to accelerate the adoption of transparent and inclusive AI, so that technology can be deployed in a safe, ethical and responsible manner. to
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The Importance of Inclusive Educational Experiences
For equitable and inclusive AI, it is imperative that everyone involved in this technology understands it, collaborates effectively, and is aware of the opportunities and risks posed by its use. A comprehensive and customized learning experience is essential to improve knowledge absorption and prevent dropout, no matter what educational path learners follow. This includes considerations for AI teaching and content delivery. for example:
- Experiences need to be designed to ensure accessibility to training for those who face barriers to formal education, such as people with disabilities, low literacy and the elderly.
- Governments and institutions should be aware that there are resource barriers to AI education among disadvantaged groups, such as computer and internet access, child care and financial support.
- It is important to understand the different social and cultural backgrounds of learners, incorporate diverse perspectives in content, and use examples and case studies that learners can relate to. One way she addresses this is by using examples, case studies, and ideas that reflect different experiences and perspectives on gender, race, culture, ethnicity, sexual orientation, religion, age, and more.
We want a richer and more productive future with AI, but we still have a long way to go before everyone has enough knowledge and skills to reap its benefits. As AI advances, it’s time to work on an inclusive, future-ready education system that paves the way for a more equitable future.
