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Explore diverse expert perspectives on transforming AI ethics and governance for a more responsible technological future.
Explore computational argumentation techniques to build fair and explainable AI systems that make transparent, justifiable decisions in complex scenarios.
Discover how to implement participatory and periodic red-teaming strategies for large language models to identify vulnerabilities and improve AI safety through collaborative testing.
Explore state-level AI policy development and discover key stakeholders shaping regulatory frameworks across American legislatures.
Explore how multiple valid solutions and interpretations emerge in machine learning systems, examining the philosophical and practical implications of multiplicity across ML applications.
Explore how transparent AI systems can transform criminal justice reform and improve legal access through innovative technological solutions.
Explore how Black community archives can reshape AI development through ethical frameworks and liberatory data practices for more inclusive technology.
Uncover how system prompts introduce bias in language models and their impact on AI fairness through research-based evaluation practices.
Discover how to address predictive multiplicity challenges in machine learning systems through practical evaluation methods and fairness metrics.
Uncover how global power dynamics shape AI ethics knowledge production, examining North-South inequalities and co-productionist frameworks in epistemic justice research.
Discover a protocol for developing child abuse imagery detection models while minimizing harmful data exposure through proxy task methodologies and risk reduction strategies.
Explore methods for testing discrimination in generative AI systems through systematic evaluation approaches and policy frameworks.
Explore how the Digital Services Act's reporting mechanisms work in practice and their effectiveness for users navigating complex legal requirements.
Explore fairness and inclusion in AI through local knowledge integration for low-resource settings with leading African ML researcher Nyalleng Moorosi.
Explore how generative AI systems suppress identity-related speech through content moderation mechanisms and their implications for marginalized communities.
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