activity
20202023
most cited"There Is Not Enough Information": On the Effects of Explanations on Perceptions of Informational Fairness and Trustworthiness in Automated Decision-Making

62 citations · 128 across the 7 of their papers we have counts for

collaborators

7 papers

cs.HC2023★ 13 cited

AI Reliance and Decision Quality: Fundamentals, Interdependence, and the Effects of Interventions

Jakob Schoeffer, Johannes Jakubik, Michael Voessing +2

In AI-assisted decision-making, a central promise of having a human-in-the-loop is that they should be able to complement the AI system by overriding its wrong recommendations. In…

cs.HC2022★ 62 cited

"There Is Not Enough Information": On the Effects of Explanations on Perceptions of Informational Fairness and Trustworthiness in Automated Decision-Making

Jakob Schoeffer, Niklas Kuehl, Yvette Machowski

Automated decision systems (ADS) are increasingly used for consequential decision-making. These systems often rely on sophisticated yet opaque machine learning models, which do not…

cs.HC2022★ 4 cited

On the Relationship Between Explanations, Fairness Perceptions, and Decisions

Jakob Schoeffer, Maria De-Arteaga, Niklas Kuehl

It is known that recommendations of AI-based systems can be incorrect or unfair. Hence, it is often proposed that a human be the final decision-maker. Prior work has argued that ex…

cs.HC2021★ 26 cited

Appropriate Fairness Perceptions? On the Effectiveness of Explanations in Enabling People to Assess the Fairness of Automated Decision Systems

Jakob Schoeffer, Niklas Kuehl

It is often argued that one goal of explaining automated decision systems (ADS) is to facilitate positive perceptions (e.g., fairness or trustworthiness) of users towards such syst…

cs.AI2021★ 15 cited

A Study on Fairness and Trust Perceptions in Automated Decision Making

Jakob Schoeffer, Yvette Machowski, Niklas Kuehl

Automated decision systems are increasingly used for consequential decision making -- for a variety of reasons. These systems often rely on sophisticated yet opaque models, which d…

cs.LG2021★ 5 cited

A Ranking Approach to Fair Classification

Jakob Schoeffer, Niklas Kuehl, Isabel Valera

Algorithmic decision systems are increasingly used in areas such as hiring, school admission, or loan approval. Typically, these systems rely on labeled data for training a classif…

cs.IR2020★ 3 cited

Needmining: Identifying micro blog data containing customer needs

Niklas Kühl, Jan Scheurenbrand, Gerhard Satzger

The design of new products and services starts with the identification of needs of potential customers or users. Many existing methods like observations, surveys, and experiments d…