Distinguishing Between Equity and Equality
Distinguishing between equity and equality in the context of an AI system is necessary to understand the goals, use cases, and approach of this guide.
Equity
Equity refers to the principle of providing fair and personalized treatment to individuals, considering their specific circumstances and needs. It involves designing AI systems that recognize and address disparities among different groups, ensuring everyone has access to the necessary resources and opportunities to achieve similar outcomes.
Equality
Equality requires a uniform distribution of resources or opportunities without regard for their unique situations or challenges. It focuses on ensuring that AI systems apply the same rules and standards to everyone, offering an identical level of opportunity and support across all user groups.
Focusing solely on equality may inadvertently perpetuate existing biases and disparities. Therefore, this guide focuses on equitable AI to allow for a nuanced approach that considers the unique challenges faced by various user groups, ensuring that AI systems promote fairness and inclusivity. By emphasizing equity, this guide aims to support EdTech development teams in mitigating historical biases in training data, algorithms, and decision-making processes toward creating AI systems that serve diverse populations.
Targeted Universalism
An important concept for addressing equity versus equality in defining goals and objectives is targeted universalism. Targeted universalism is an approach that aims to achieve universal goals by tailoring specific interventions to meet the unique needs and circumstances of different groups or individuals. It is grounded in the recognition that, as a result of systemic barriers and societal factors, unique solutions may be required so that equitable outcomes can be reached for marginalized groups.