Abstract
Artificial intelligence (AI) technologies inherently reflect and amplify the sociocultural contexts and systemic inequities of their creators, embedding biases related to gender, race, class, sexuality, and ethnicity into algorithmic systems. This study examines how AI systems, the AI industry, and development processes perpetuate these biases while exploring pathways to address them. AI systems often replicate and reinforce structural discrimination through biased training datasets and algorithmic design, disproportionately impacting marginalized groups. Within the AI industry, the underrepresentation of minorities and systemic barriers constrain the diversity of perspectives shaping technology, further entrenching inequities. Additionally, development processes often prioritize dominant cultural narratives and technical efficiency over inclusivity and ethical accountability, exacerbating disparities for underrepresented populations. Aligned with Theme 3: Cultural Studies, this paper focuses on three key areas: 1. Algorithmic Bias and Marginalized Identities: Investigating how AI systems perpetuate discrimination against gender, racial, ethnic, and socioeconomic minorities. 2. Ethical Governance and Cultural Responsibility: Highlighting the need for governance frameworks and inclusive development practices that address systemic discrimination, prioritize diverse perspectives, and align AI technologies with equity and justice. 3. Inclusive and Intersectional AI Design: Proposing actionable frameworks that integrate diverse identities and cultural contexts into the design, governance, and deployment of AI technologies to promote equitable outcomes globally. This study underscores the importance of embedding ethical principles and cultural sensitivity into AI innovation. By linking AI’s technical and cultural dimensions, it advocates for systemic change and collaborative approaches to ensure AI systems contribute to a more inclusive and equitable global society.
Details
Presentation Type
Paper Presentation in a Themed Session
Theme
KEYWORDS
AI, Gender bias, Systemic Inequities, Intersectionality
