Publications (7)
Cracking the Code: Evaluating Zero-Shot Prompting Methods for Providing Programming Feedback
Niklas Ippisch, Anna-Carolina Haensch, Jan Simson +3
Despite the growing use of large language models (LLMs) for providing feedback, limited research has explored how to achieve high-quality feedback. This case study introduces an ev…
"Taking Stock at FAccT": Using Participatory Design to Co-Create a Vision for the Fairness, Accountability and Transparency Community
Shiran Dudy, Jan Simson, Yanan Long
As a relatively new forum, ACM FAccT has become a key space for activists and scholars to critically examine emerging AI and ML technologies. It brings together academics, civil so…
Evaluating Intellectual Property Guardrails of Generative Image Models: A Technical Report
Austin T. Hoag, Apostolos Modas, Yunhao Ba +9
Generative image models are capable of producing images that bear a strong resemblance to, or replicate, recognizable intellectual property (IP). In this technical report, we prese…
One Model Many Scores: Using Multiverse Analysis to Prevent Fairness Hacking and Evaluate the Influence of Model Design Decisions
Jan Simson, Florian Pfisterer, Christoph Kern
A vast number of systems across the world use algorithmic decision making (ADM) to (partially) automate decisions that have previously been made by humans. The downstream effects o…
Decoupling Data and Tooling in Interactive Visualization
Jan Simson
Interactive data visualization is a major part of modern exploratory data analysis, with web-based technologies enabling a rich ecosystem of both specialized and general tools. How…
Lazy Data Practices Harm Fairness Research
Jan Simson, Alessandro Fabris, Christoph Kern
Data practices shape research and practice on fairness in machine learning (fair ML). Critical data studies offer important reflections and critiques for the responsible advancemen…