We published a review article that aims to present a literature overview on collecting and employing human knowledge to improve and evaluate the understandability of machine learning models through human-in-the-loop approaches.
Despite the increasing limitations for unvaccinated people, in many European countries, there is still a non-negligible fraction of individuals who refuse to get vaccinated against SARS-CoV-2, undermining governmental efforts to eradicate the virus. Within the PERISCOPE project, we studied the role of online social media in influencing individuals’ opinions about getting vaccinated by designing a … Continue reading The VaccinEU dataset of COVID-19 Vaccine Conversations on Twitter in French, German, and Italian
Starting June 2022, our book “Model Driven Software Engineering in Practice” (co-authored with Jordi Cabot and Manuel Wimmer) is now also available via Springer . This means the price is actually lower, and if you are affiliated with an academic institution, you may even have free access to the book through your institutional access. Check it here. Together … Continue reading Model Driven Software Engineering in Practice now published by Springer Nature
The spread of AI and black-box machine learning models makes it necessary to explain their behavior. Consequently, the research field of Explainable AI was born. The main objective of an Explainable AI system is to be understood by a human as the final beneficiary of the model. In our research we just published on Frontiers … Continue reading EXP-Crowd: Gamified Crowdsourcing for AI Explainability