Interpretability of ML models and image recognition models specifaclly, is a increasing problem. In this thesis, the design and implementation of Brickroutine: a system that used a trained model, is presented. Using human annotations, semantic interpretations are given to image classification problems. By giving an iterative approach in terms of workflows and technically designing it in a modular, salable way using Docker, the authors aim for bridging the gap between performance and interpretability of AI by combining human and computational intelligence.

The use of certain buyer strategies in the process of property bidding is being discovered in this paper. An Agent-based model is introduced where financial statistics are based on the Dutch housing market. Three types of agents are used in this model that uses cycles of two weeks. Thee hypotheses about the market are initially formulated. In the end, it proved that only the hypothesis "Initial overbidding on the list price results in a higher success rate" could be confirmed.