Portrait de Leah Davis

Leah Davis

Doctorat - McGill
Superviseur⋅e principal⋅e
AJung Moon
Sujets de recherche
Théorie de l'apprentissage automatique
Site web
GitHub

Publications

Responsible AI measures dataset for ethics evaluation of AI systems
Shalaleh Rismani
Leah Davis
Bonam Mingole
Negar Rostamzadeh
Renee Shelby
AJung Moon
Meaningful governance of any system requires the system to be assessed and monitored effectively. In the domain of Artificial Intelligence (… (voir plus)AI), global efforts have established a set of ethical principles, including fairness, transparency, and privacy upon which AI governance expectations are being built. The computing research community has proposed numerous means of measuring an AI system's normative qualities along these principles. Current reporting of these measures is principle-specific, limited in scope, or otherwise dispersed across publication platforms, hindering the domain's ability to critique its practices. To address this, we introduce the Responsible AI Measures Dataset, consolidating 12,067 data points across 791 evaluation measures covering 11 ethical principles. It is extracted from a corpus of computing literature (n = 257) published between 2011 and 2023. The dataset includes detailed descriptions of each measure, AI system characteristics, and publication metadata. An accompanying, interactive visualization tool supports usability and interpretation of the dataset. The Responsible AI Measures Dataset enables practitioners to explore existing assessment approaches and critically analyze how the computing domain measures normative concepts.
2025-12-19
Scientific Data (publié)
doi.org
Measuring What Matters: Connecting AI Ethics Evaluations to System Attributes, Hazards, and Harms
Shalaleh Rismani
Renee Shelby
Leah Davis
Negar Rostamzadeh
AJung Moon
Over the past decade, an ecosystem of measures has emerged to evaluate the social and ethical implications of AI systems, largely shaped by … (voir plus)high-level ethics principles. These measures are developed and used in fragmented ways, without adequate attention to how they are situated in AI systems. In this paper, we examine how existing measures used in the computing literature map to AI system components, attributes, hazards, and harms. Our analysis draws on a scoping review resulting in nearly 800 measures corresponding to 11 AI ethics principles. We find that most measures focus on four principles – fairness, transparency, privacy, and trust – and primarily assess model or output system components. Few measures account for interactions across system elements, and only a narrow set of hazards is typically considered for each harm type. Many measures are disconnected from where harm is experienced and lack guidance for setting meaningful thresholds. These patterns reveal how current evaluation practices remain fragmented, measuring in pieces rather than capturing how harms emerge across systems. Framing measures with respect to system attributes, hazards, and harms can strengthen regulatory oversight, support actionable practices in industry, and ground future research in systems-level understanding.
2025-10-14
Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society (publié)
doi.org
arxiv.org