23 citations · 34 across the 9 of their papers we have counts for
9 papers
Measuring What Matters: Connecting AI Ethics Evaluations to System Attributes, Hazards, and Harms
Shalaleh Rismani, Renee Shelby, Leah Davis +2
Over the past decade, an ecosystem of measures has emerged to evaluate the social and ethical implications of AI systems, largely shaped by high-level ethics principles. These meas…
Opening the Scope of Openness in AI
Tamara Paris, AJung Moon, Jin Guo
The concept of openness in AI has so far been heavily inspired by the definition and community practice of open source software. This positions openness in AI as having positive co…
From Silos to Systems: Process-Oriented Hazard Analysis for AI Systems
Shalaleh Rismani, Roel Dobbe, AJung Moon
To effectively address potential harms from Artificial Intelligence (AI) systems, it is essential to identify and mitigate system-level hazards. Current analysis approaches focus o…
Improving Generalization in Reinforcement Learning Training Regimes for Social Robot Navigation
Adam Sigal, Hsiu-Chin Lin, AJung Moon
In order for autonomous mobile robots to navigate in human spaces, they must abide by our social norms. Reinforcement learning (RL) has emerged as an effective method to train sequ…
Beyond the ML Model: Applying Safety Engineering Frameworks to Text-to-Image Development
Shalaleh Rismani, Renee Shelby, Andrew Smart +3
Identifying potential social and ethical risks in emerging machine learning (ML) models and their applications remains challenging. In this work, we applied two well-established sa…
From plane crashes to algorithmic harm: applicability of safety engineering frameworks for responsible ML
Shalaleh Rismani, Renee Shelby, Andrew Smart +4
Inappropriate design and deployment of machine learning (ML) systems leads to negative downstream social and ethical impact -- described here as social and ethical risks -- for use…
Sociotechnical Harms of Algorithmic Systems: Scoping a Taxonomy for Harm Reduction
Renee Shelby, Shalaleh Rismani, Kathryn Henne +8
Understanding the landscape of potential harms from algorithmic systems enables practitioners to better anticipate consequences of the systems they build. It also supports the pros…
What does it mean to be a responsible AI practitioner: An ontology of roles and skills
Shalaleh Rismani, AJung Moon
With the growing need to regulate AI systems across a wide variety of application domains, a new set of occupations has emerged in the industry. The so-called responsible AI practi…
Driver perceptions of advanced driver assistance systems and safety
Sophie Le Page, Jason Millar, Kelly Bronson +2
Advanced driver assistance systems (ADAS) are often used in the automotive industry to highlight innovative improvements in vehicle safety. However, today it is unclear whether cer…