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.
Tag: machine learning
EXP-Crowd: Gamified Crowdsourcing for AI Explainability
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
A sneak peek at the European Union Ethics Guidelines for AI
A few days ago, politico.eu published a preview of the document that the European Union will issue as guidance for ethical issues related to artificial intelligence and machine learning. The document was written by the High-level Expert Group on Artificial Intelligence, appointed by the European Commission. This advanced version of the document is available online now … Continue reading A sneak peek at the European Union Ethics Guidelines for AI
IEEE Big Data Conference 2017: take home messages from the keynote speakers
I collected here the list of my write-ups of the first three keynote speeches of the conference: Human in the Loop Machine Learning (Carla E. Brodley, Northeastern Univ.) Enhancing Human Perception via Text Mining and IR (Cheng Zhai, Univ. Illinois) Graph Representation Learning (Jure Leskovec, Stanford and Pinterest)