Equity Bias: An Ethical Framework for AI Design
Equity Bias is a philosophical and practical framework for building smarter, more equitable AI systems. Grounded in hermeneutic philosophy and epistemic injustice theory, it treats bias not as an error to eliminate but as a reflection of whose knowledge is encoded into systems. While traditional approaches aim to reduce or remove bias, Equity Bias instead makes bias transparent and contestable. In doing so, it broadens whose perspectives shape AI and provides a lens for understanding AI systems as interpretive agents. The framework introduces a three-phase AI Life Cycle methodology: 'Equity Archaeology' (mapping knowledge and assumptions), 'Co-Creating Meaning' (participatory design), and 'Ongoing Accountability' (continuous evaluation). Equity Bias guides developers, researchers, and policymakers towards AI that is ethically accountable and capable of addressing complex real-world challenges.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Advancing Transformative Education: Generative AI as a Catalyst for Equity and Innovation
Generative AI is transforming education by enabling personalized learning, enhancing administrative efficiency, and fostering creative engagement. This paper explores the opportunities and challenges these tools bring to…
Connecting Fairness in Machine Learning with Public Health Equity
Machine learning (ML) has become a critical tool in public health, offering the potential to improve population health, diagnosis, treatment selection, and health system efficiency. However, biases in data and model desi…
FairnessFair Multivariate Adaptive Regression Splines for Ensuring Equity and Transparency
Predictive analytics is widely used in various domains, including education, to inform decision-making and improve outcomes. However, many predictive models are proprietary and inaccessible for evaluation or modification…
Decision MakingFairnessfeature selectionregressionArtificial Intelligence for Inclusive Engineering Education: Advancing Equality, Diversity, and Ethical Leadership
AI technology development has transformed the field of engineering education with its adaptivity-driven, data-based, and ethical-led learning platforms that promote equity, diversity, and inclusivity. But with so much pr…
Co-Producing AI: Toward an Augmented, Participatory Lifecycle
Despite efforts to mitigate the inherent risks and biases of artificial intelligence (AI) algorithms, these algorithms can disproportionately impact culturally marginalized groups. A range of approaches has been proposed…