The Imperative of Fairness in AI for Economic Decision-Making
by Laasya Aki
I previously talked about data bias and how this problem is exacerbated with the rise of AI, deep diving into the example of loan grant approval. In this post I wanted to focus on the fairness of AI in terms of economic tools on a broader scale. As AI continues to play an increasingly pivotal role in economic decision-making, the issue of fairness has garnered significant attention. There are many challenges in ensuring that AI is fair.
Bias is a major concern in AI, as AI systems are built by humans who can inadvertently inject biases into the algorithms. These biases can lead to unfair decisions, such as denying loans to certain groups of people based on characteristics like gender or ethnicity. To address this, there are technical tools available that can help detect and mitigate bias in AI systems. However, achieving fairness in AI goes beyond just technical solutions. It also requires governance structures to identify and address biases in data collection and processing. Education is another important aspect, as developers and decision-makers need to be aware of their biases and how they can impact AI systems. However, one major organization is making changes in order to combat this problem in the context of economic tools.
The World Economic Forum (WEF) has been at the forefront of addressing fairness in AI, notably through its Global Future Council (GFC) on AI for Humanity. Comprising experts from diverse backgrounds and industries, the GFC is dedicated to advancing the understanding and implementation of fair AI practices. One of the primary initiatives of the GFC is the development of guidelines and principles for AI ethics, focusing on transparency, explainability, privacy, robustness, and fairness. These principles are designed to provide a framework for organizations to ensure the fairness and ethicality of their AI systems. They are also focusing on educational initiatives and awareness-building around AI fairness. This includes creating educational materials to enlighten employees about the implications of biased AI and providing methodologies, tools, and practices to address these issues.
AI algorithm biases in economic decision-making tools can arise from various factors. Historical biases present in the data used for training AI algorithms can perpetuate inequalities, and the design and objectives of the algorithms can also introduce biases. However, if more companies and governments dedicate time and resources to this issue, the fairness of AI can be improved.
References:
- https://www.weforum.org/agenda/2021/01/how-to-address-artificial-intelligence-fairness/
- https://www.weforum.org/publications/a-holistic-guide-to-approaching-ai-fairness-education-in-organizations/