Current AI systems describing the content of images and videos in natural language often make assumptions about the gender, nationality, or physical appearance of the people in them. In this project, we aim to fix this unwanted behavior by developing more inclusive systems.
In this project, we develop methods for automatically generating explanations for why a given compromise (between disagreeing people) is the best available option.
In this project, we study and develop new generation AI systems for fraud detection which are not only accurate, but also explainable for different stakeholders and adaptive to certain business rules.
We want to investigate how language technology can be developed and designed in a way that avoids language modeling bias (language technology's hard-wired representational preference for certain languages over other ones).
In this project, we design and analyse voting rules for participatory budgeting, a direct democracy initiative where residents jointly decide on how public funds are spent.
Bias across borders: towards a social-political understanding of bias in machine learning
Goal of this project is to provide a clear conceptual framework on the notion of bias in machine learning for AI researchers in the context of algorithmic fairness.
CERTAIN consists of a diverse group of researchers with various backgrounds. What unites us is our interest in developing explainable and responsible AI. Feel free to contact us or learn more about what we do.
Language technologies, like ChatGPT, are developing quickly. But we don’t really know how they work. And they are not always designed to help society. adapted from Nikki Weststeijn‘s post on...
Going beyond a mathematical investigation of bias
A version of this blog post first appeared on https://odvanderwal.nl/2023/positioning-bias. When researchers study how biased language models are, they generally approach this in a mathematical or statistical way. For example,...
