Interested in #XAI? Do not miss the Explainability in ML workshop at @unituebingen
It will take place March 28-29 in Tübingen (Germany) and will feature talks from prominent researchers in the field.
Check the programme here https://eml-unitue.de/eml-workshop, and register in advance: few spots remaining!
#MachineLearning #ML #ExplainableML #Workshop #UniversityTuebingen
Ultimately, we hope it contributes to bridging the gap between #ExplainableML, #XAI, and #ORMS. To facilitate future work and experimental reproducibility, all our algorithms and scripts are provided in #opensource at https://github.com/alexforel/Explainable-CSO.
Many thanks to my amazing co-authors Alexandre Forel and Axel Parmentier, as well as Polytechnique Montréal and SCALE AI for the financial support through the SCALE-AI Chair in Data-Driven Supply Chains!
Uncertainty about the inner workings of #machinelearning models holds back the application of ML-enabled systems in real estate markets. How do ML models arrive at their estimates?
How can practitioners guarantee that ML systems do not run afoul of the law?
Wayne Wan and I show how ML systems can be externally tested with dedicated system tests (as commonly done in software development).
#newpaperalert #appliedML #realestate #proptech #explainableML
https://onlinelibrary.wiley.com/doi/abs/10.1111/1540-6229.12416
ml4science
Thibaut Vidal
Thies Lindenthal