Unmasking AI
by Joy Buolamwini
Key Concepts
Coded Gaze
AI systems often fail to accurately recognize or represent marginalized groups due to biased training data.
Algorithmic Bias
AI models inherit and amplify human biases present in their training data, leading to unfair outcomes.
Exclusionary AI
Systems designed without diverse input can exclude or harm specific populations, exacerbating existing inequalities.
Algorithmic Justice
The movement to ensure fairness, accountability, and transparency in the development and deployment of AI.
AI Auditing
The necessity of rigorously testing AI systems for fairness and accuracy across diverse demographic groups.
Action Items
Prioritize diverse, representative data in all AI training sets.
Implement regular, independent bias audits for all AI models before deployment.
Advocate for ethical AI regulations and accountability frameworks in policy.
Foster interdisciplinary teams in AI development, including ethicists and social scientists.
Design AI with human-centered values and societal impact as core considerations.
Core Thesis
AI systems, if unchecked, perpetuate and amplify societal biases, demanding a proactive, ethical approach to their design and deployment.
Mindset Shift
Shifts perception of AI from a neutral technological tool to a powerful social force requiring ethical scrutiny and human oversight.