Simone Stumpf

IS Journal 2023 Journal Article

Artificial Intelligence Ethics and Trust: From Principles to Practice

• Fang Chen
• Jianlong Zhou
• Andreas Holzinger
• Kenneth R. Fleischmann
• Simone Stumpf

Despite the proliferation of ethical frameworks of artificial intelligence (AI) from different organizations such as government agencies, large corporations, and academic institutions, it is still a challenge to implement and operationalize ethical and legal frameworks for AI in practice due to its complexities. The implementation and operationalization involve different aspects in original theoretical and practical research on designing, developing, presenting, testing, and evaluating approaches, which are supported by advanced AI techniques and interdisciplinary research, in particular, social science, law, and cognitive science. This editorial provides an overview of the field of operationalization of AI ethics and trust, and highlights a few key topics covered in this special issue, i. e. , the current landscape of AI ethics implementation, trust and trustworthiness in AI, ethical framework for trust calibration, approaches to build morality in AI, implementation of AI ethics with a pattern-oriented engineering approach, and inclusive user studies.

AIJ Journal 2013 Journal Article

End-user feature labeling: Supervised and semi-supervised approaches based on locally-weighted logistic regression

• Shubhomoy Das
• Travis Moore
• Weng-Keen Wong
• Simone Stumpf
• Ian Oberst
• Kevin McIntosh
• Margaret Burnett

AAAI Conference 2011 Conference Paper

End-User Feature Labeling via Locally Weighted Logistic Regression

• Weng-Keen Wong
• Ian Oberst
• Shubhomoy Das
• Travis Moore
• Simone Stumpf
• Kevin McIntosh
• Margaret Burnett

AAAI Conference 2005 System Paper

The TaskTracker System

• Simone Stumpf
• Anton Dragunov
• Jon Herlocker
• Lida Li

Knowledge workers spend the majority of their working hours processing and manipulating information. These users face continual costs as they switch between tasks to retrieve and create information. The TaskTracer project at Oregon State University investigates the possibilities of a desktop software system that will record in detail how knowledge workers complete tasks, and intelligently leverage that information to increase efficiency and productivity. Our approach assigns each observed user interface action to a task for which it is likely being performed. In this demonstration we show how we have applied machine learning in this environment.