Researchers from Carnegie Mellon University and Stevens Institute of Technology, including John Hooker and Derek Leben, have proposed a new method to improve AI fairness. The method, called social welfare optimization, focuses on overall benefits and harms to individuals rather than approval rates across groups. The study emphasizes “alpha fairness,” which balances fairness and efficiency. This approach could lead to better outcomes for disadvantaged groups in situations like job interviews or mortgage approvals. The findings could help AI developers create more equitable models and policymakers to consider social justice in AI development.
A New Perspective on AI Fairness: Social Welfare Optimization
Researchers from Carnegie Mellon University and Stevens Institute of Technology have proposed a novel approach to evaluating the fairness of AI decisions. Their method, detailed in a recent paper, draws on the concept of social welfare optimization. This well-established principle aims to enhance fairness by considering the overall benefits and harms to individuals. The researchers suggest that this method could be used to assess the standard industry tools for AI fairness, which typically focus on approval rates across protected groups.
John Hooker, a professor of operations research at the Tepper School at Carnegie Mellon, co-authored the study. He explains that the AI community strives to ensure equitable treatment for groups that differ in economic level, race, ethnic background, gender, and other categories. The paper, which received the Best Paper Award, was presented at the International Conference on the Integration of Constraint Programming, Artificial Intelligence, and Operations Research (CPAIOR) in Uppsala, Sweden.
The Concept of Alpha Fairness in AI
The study introduces the concept of “alpha fairness,” a method of striking a balance between fairness and maximizing benefits for everyone. Alpha fairness can be adjusted to balance fairness and efficiency more or less, depending on the situation.
Consider a scenario where an AI system decides who gets approved for a mortgage or who gets a job interview. Traditional fairness methods might only ensure that the same percentage of people from different groups get approved. However, the denial of a mortgage could have a significantly more negative impact on someone from a disadvantaged group than on someone from an advantaged group. By employing a social welfare optimization method, AI systems can make decisions that lead to better outcomes for everyone, particularly for those in disadvantaged groups.
Social Welfare Optimization and Group Fairness
The researchers demonstrate how social welfare optimization can be used to compare different assessments for group fairness currently used in AI. By using this method, we can understand the benefits of applying different group fairness tools in different contexts. It also connects these group fairness assessment tools to the broader world of fairness-efficiency standards used in economics and engineering.
Derek Leben, associate teaching professor of business ethics at the Tepper School, and Violet Chen, assistant professor at Stevens Institute of Technology, co-authored the study. Leben suggests that social welfare optimization can provide insights into the intensely debated question of how to achieve group fairness in AI.
Implications for AI Developers and Policymakers
The study has significant implications for both AI system developers and policymakers. Developers can create more equitable and effective AI models by adopting a broader approach to fairness and understanding the limitations of fairness measures. It also underscores the importance of considering social justice in AI development, ensuring that technology promotes equity across diverse groups in society.
The paper is published in CPAIOR 2024 Proceedings. The researchers’ findings suggest that social welfare optimization could be a valuable tool in the ongoing effort to make AI fairer for everyone. By considering the overall benefits and harms to individuals, this method could lead to more equitable outcomes, particularly for those in disadvantaged groups.
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