OAFP 5565 - Understanding Human-AI Coevolution and its impact on Society

The rise of socio-technical systems, where humans interact with AI assistants and recommenders, poses risks of unintended consequences. Navigation apps like Google Maps may cause congestion by directing too many drivers to the same route; profiling and targeted ads can reinforce biases and inequality; and generative AI chatbots risk declining quality as synthetic data increasingly drives retraining. These issues stem from Machine Learning (ML) models trained on user behaviour, creating feedback loops that shape future choices and data. This course equips students to analyse and model these loops, designing experiments to assess their societal impact and foster responsible AI development.

Luca PAPPALARDO

Enseignement électif

English

Students will receive papers and slides to study.

● Principles of Data Analysis: Students are expected to know what a statistical distribution is, how to characterise it with basic measures (mean, standard deviation). ● Principles of Machine Learning: Students are expected to know what regression and classification tasks are.

Spring 2024-2025

In small groups, students are required to prepare a comprehensive document detailing experiments—either empirical or simulation-based—designed to evaluate the impact of AI on ecosystems such as social media, retail, or urban mapping. The document should include: Key impact metrics to be measured. Experimental methodologies, outlining how the experiments will be conducted. Insights from existing literature, providing a theoretical foundation for the experiments. Students will present their work during an oral presentation. The evaluation criteria are as follows: Class participation: 10% (active engagement during the class session). Final submission: 90% (quality and depth of the submitted work).

Files on Google Drive.