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

When intelligent interfaces, such as intelligent desktop assistants, email classifiers, and recommender systems, customize themselves to a particular end user, such customizations can decrease productivity and increase frustration due to inaccurate predictions—especially in early stages when training data is limited. The end user can improve the learning algorithm by tediously labeling a substantial amount of additional training data, but this takes time and is too ad hoc to target a particular area of inaccuracy. To solve this problem, we propose new supervised and semi-supervised learning algorithms based on locally-weighted logistic regression for feature labeling by end users, enabling them to point out which features are important for a class, rather than provide new training instances. We first evaluate our algorithms against other feature labeling algorithms under idealized conditions using feature labels generated by an oracle. In addition, another of our contributions is an evaluation of feature labeling algorithms under real-world conditions using feature labels harvested from actual end users in our user study. Our user study is the first statistical user study for feature labeling involving a large number of end users (43 participants), all of whom have no background in machine learning. Our supervised and semi-supervised algorithms were among the best performers when compared to other feature labeling algorithms in the idealized setting and they are also robust to poor quality feature labels provided by ordinary end users in our study. We also perform an analysis to investigate the relative gains of incorporating the different sources of knowledge available in the labeled training set, the feature labels and the unlabeled data. Together, our results strongly suggest that feature labeling by end users is both viable and effective for allowing end users to improve the learning algorithm behind their customized applications.

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.