Experiment

Some image generators produce more problematic stereotypes than others, but all fail at diversity

Automated image generators are often accused of spreading harmful stereotypes, but studies usually only look at MidJourney. Other tools make serious efforts to increase diversity in their output, but effective remedies remain elusive.

Explainer

How and why algorithms discriminate

Automated decision-making systems contain hidden discriminatory prejudices. We’ll explain the causes, possible consequences, and the reasons why existing laws do not provide sufficient protection against algorithmic discrimination.

What is algorithmic discrimination?

Discrimination by algorithms and Artificial Intelligence (AI): we give an overview of the topic.

Stance

If the UN wants to help humanity, it should not fall for AI hype

How should the international governance of AI look like? This is the thorny question the UN Secretary General’s AI Advisory Body tries to address in its first interim report. We have highlighted some concerning aspects of the report in a recent consultation process.

Election year 2024

10 Questions about AI and elections

2024 is an important election year. While citizens all over the world get ready to cast their ballot, many people worry about AI: Are chatbots and fake images a threat to democracy? Can we still trust what we see online? We explain why the hype around AI and elections is somewhat overblown and which real risks we need to watch out for instead.

Atlas of Automation

To detect benefit fraud, measure work performance, predict a person's creditworthiness or show us personalised content online - algorithms and so-called "artificial intelligence" are shaping our everyday lives today. Where, by whom and for what purpose these algorithmic systems are used, however, is largely a black box. With the Atlas of Automation, AlgorithmWatch CH now sheds light on the darkness.

FINDHR

Fair algorithms in personnel selection?

AlgorithmWatch CH is part of the Horizon Europe project “FINDHR”. In this interdisciplinary research project, we address software-related discriminatory effects within recruiting processes by developing methods, tools, and trainings that are designed to avoid discrimination.