12 citations · 18 across the 5 of their papers we have counts for
6 papers
Position: Stop Evaluating AI with Human Tests, Develop Principled, AI-specific Tests instead
Tom Sühr, Florian E. Dorner, Olawale Salaudeen +2
Large Language Models (LLMs) have achieved remarkable results on a range of standardized tests originally designed to assess human cognitive and psychological traits, such as intel…
A Dynamic Model of Performative Human-ML Collaboration: Theory and Empirical Evidence
Tom Sühr, Samira Samadi, Chiara Farronato
Machine learning (ML) models are increasingly used in various applications, from recommendation systems in e-commerce to diagnosis prediction in healthcare. In this paper, we prese…
Challenging the Validity of Personality Tests for Large Language Models
Tom Sühr, Florian E. Dorner, Samira Samadi +1
With large language models (LLMs) like GPT-4 appearing to behave increasingly human-like in text-based interactions, it has become popular to attempt to evaluate personality traits…
A Note on the Significance Adjustment for FA*IR with Two Protected Groups
Meike Zehlike, Tom Sühr, Carlos Castillo
In this report we provide an improvement of the significance adjustment from the FA*IR algorithm of Zehlike et al., which did not work for very short rankings in combination with a…
Does Fair Ranking Improve Minority Outcomes? Understanding the Interplay of Human and Algorithmic Biases in Online Hiring
Tom Sühr, Sophie Hilgard, Himabindu Lakkaraju
Ranking algorithms are being widely employed in various online hiring platforms including LinkedIn, TaskRabbit, and Fiverr. Prior research has demonstrated that ranking algorithms…
FairSearch: A Tool For Fairness in Ranked Search Results
Meike Zehlike, Tom Sühr, Carlos Castillo +1
Ranked search results and recommendations have become the main mechanism by which we find content, products, places, and people online. With hiring, selecting, purchasing, and dati…