Understandable AI

The functioning of AI increasingly surpasses our understanding of it. As AI plays more and more of a role in the way our systems operate, how can we design the user experience in a way to match this? How can the user make sense of what the AI is providing them, and how it operates?

MSc Elective Course: Designing with Advanced AI

Classifying Cycling Routes using Machine Learning Design Principles  

For this elective I developed a cycling route recommendation platform 'BraboBike'. The design clusters the different municipalities by the amount of nature, water, noise and amenities and compares this to the user's past cycling activity and environmental preferences in order to establish which municipalities fit the user.

The design process involved extensive presence of Machine Learning using WEKA, where I took a leading role in data selection, algorithm selection and application, pre-processing of data, and evaluation of clusters. 

MSc Elective Course: Embodying Intelligent Behaviour in Social Context

Recommending Ethical Papers considering Linguistic Ability and Personal Interests

REAiD is a personalized library that creates recommendations for academic ethcs papers. It evaluates reading- and writing proficiency and recommends papers that fit your linguistic ability. 

The elective focused on creating 'Explainable AI' that allow humans to comprehend how it operates by showing the steps and information AI uses. It allowed me a deeper understanding of the link between Math, Data & Computing and User & Society, and use python to code with API's and machine learning algorithms.

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