Toward Sociotechnical AI: Mapping Vulnerabilities for Machine Learning in Context

This paper provides an empirical and conceptual account on seeing machine learning models as part of a sociotechnical system to identify relevant vulnerabilities emerging in the context of use. As ML is increasingly adopted in socially sensitive and safety-critical domains, many ML applications end up not delivering on their promises, and...

Contestable AI by Design: Towards a Framework

As the use of AI systems continues to increase, so do concerns over their lack of fairness, legitimacy and accountability. Such harmful automated decision-making can be guarded against by ensuring AI systems are contestable by design: responsive to human intervention throughout the system lifecycle. Contestable AI by design is a small but...