Human-Centric Machine Learning
One primary goal of Machine Learning (ML) development is to create computational systems capable of learning from real-world data and making informed predictions. While ML applications demonstrate benefits, unexpected algorithmic behaviours, traceable to various aspects of ML system design, have led to a decrease in public trust in such systems due to their perceived unreliability. Moreover, algorithmic tools in public policing have shown biases linked to race, gender, and culture, thereby reinforcing existing social inequalities. This research attempts to mitigate these systemic biases and explore more possibilities to support high-stakes fields (e.g., policy-making) by embedding human-centric design into ML system.
Keywords
Human-centric design, machine learning, Artificial Intelligence
Staff
[Loraine Clarke]{lec24}, [Jason Jacques]{jtj2}