239 citations
- ETH ZurichCH2 papers
- Institute of Agricultural EngineeringHU2 papers
- Institut National de Recherche pour l'Agriculture, l'Alimentation et l'EnvironnementFR2 papers
- Massachusetts Institute of TechnologyUS2 papers
- Northwestern UniversityUS2 papers
- Otto-von-Guericke-Universität MagdeburgDE2 papers
- University of AmsterdamNL2 papers
- University of BonnDE2 papers
- University of KonstanzDE2 papers
- University of StuttgartDE2 papers
- University of TübingenDE2 papers
- Wageningen University & ResearchNL2 papers
32 papers
Why AI Slop Matters, but Not Like That
Sachita Nishal, Marijn Sax, Kimon Kieslich
This is a response to the paper ''Why Slop Matters''. By offering both immanent and external critique, we argue that the authors' reasoning neglects the socio-technical context of…
Informing AI Policy Assessment using Large-Scale Simulation of Interventions
Julia Barnett, Kimon Kieslich, Natali Helberger +1
As the rapid proliferation of AI systems and harms spurs efforts in AI governance around the world, prioritizing among competing policy options has become increasingly challenging…
AI Governance in Higher Education: A course design exploring regulatory, ethical and practical considerations
Raphaël Weuts, Johannes Bleher, Hannah Bleher +4
As artificial intelligence (AI) systems permeate critical sectors, the need for professionals who can address ethical, legal and governance challenges has become urgent. Current AI…
Estimating Causal Effects with Observational Data: Guidelines for Agricultural and Applied Economists
Arne Henningsen, Guy Low, David Wuepper +4
Most research questions in agricultural and applied economics are of a causal nature, i.e., how one or more variables (e.g., policies, prices, the weather) affect one or more other…
Regression approaches for modelling genotype-environment interaction and making predictions into unseen environments
Maksym Hrachov, Hans-Peter Piepho, Niaz Md. Farhat Rahman +1
In plant breeding and variety testing, there is an increasing interest in making use of environmental information to enhance predictions for new environments. Here, we will review…
It's only fair when I think it's fair: How Gender Bias Alignment Undermines Distributive Fairness in Human-AI Collaboration
Domenique Zipperling, Luca Deck, Julia Lanzl +1
Human-AI collaboration is increasingly relevant in consequential areas where AI recommendations support human discretion. However, human-AI teams' effectiveness, capability, and fa…