AI Systems
Research on machine learning transparency, algorithmic bias, and AI safety mechanisms to ensure AI systems respect human autonomy and rights while delivering beneficial outcomes.
Research Overview
The AI Systems research area examines the technical, ethical, and governance dimensions of artificial intelligence and machine learning systems. As AI becomes increasingly integrated into critical domains of human life, understanding how to ensure these systems respect human autonomy, operate transparently, and align with human values becomes essential for responsible development and deployment.
Our research in this area spans technical methods, evaluation frameworks, and governance approaches, with a particular focus on:
- Algorithmic Transparency: Developing methods and metrics for making AI systems more explainable and interpretable, enabling meaningful human oversight and accountability.
- Bias Detection and Mitigation: Investigating approaches for identifying and addressing bias in AI systems, ensuring fair and equitable outcomes across different demographic groups.
- AI Safety: Researching technical approaches to AI alignment, control, and safety, ensuring AI systems behave as intended and avoid harmful outcomes.
- Human-AI Interaction: Exploring how humans interact with AI systems, and developing design principles that respect human agency and autonomy.
Current Projects
Algorithmic Transparency Toolkit
Development of practical tools and methodologies for making AI systems more transparent and explainable, enabling meaningful human oversight and accountability.
AI Bias Audit Framework
Creation of a comprehensive framework for auditing AI systems for bias, with specific methodologies for different domains and applications.
AI Safety Benchmarks
Development of benchmarks and evaluation criteria for assessing the safety and alignment of AI systems, with a focus on high-risk applications.
Human-Centered AI Design Principles
Research on design principles for AI systems that respect human agency and autonomy, with practical guidelines for developers and designers.
Research Methodology
Our research in this area employs a multidisciplinary approach, combining:
- Technical Research: Development and evaluation of algorithms, metrics, and tools for addressing challenges in AI transparency, bias, and safety.
- Empirical Studies: Systematic evaluation of AI systems in real-world contexts, measuring their impacts on different stakeholders and communities.
- Participatory Methods: Collaborative research with diverse stakeholders, including those potentially affected by AI systems, to ensure inclusive perspectives.
- Policy Analysis: Examination of governance approaches for AI, including standards, regulations, and self-regulatory frameworks.
Featured Publication
Algorithmic Transparency: Methods and Metrics for Explainable AI
This comprehensive report provides a systematic overview of methods for making AI systems more transparent and explainable, with practical guidance for implementation across different domains and applications.
Read the report