current research

visualizing algorithms:
a prototype software to visualize a machine learning algorithm, a decision tree classifier. 

simulated data flows through the algorithm, showing decisions being made in real time.

built procedurally as an interactive tool, so that any classifier of the same type can be loaded and visualized. the ui supports the self-organization of the algorithm structurally, and to aide analysis. the loaded examples present different shapes of classifier with different feature to class ratios.

the prototype can visualize mistakes in prediction, where the algorithm misclassifies data. it can also reverse engineer each data point’s path through the algorithm to visualize at which fork an error was made.

the most popular paths taken through the algorithm’s complex network of decisions are also visualized.

research questions:

can the visualizations of algorithms be used as an a-linguistic tool to reengage with decision-making in prediction systems?

can interaction design, generative design, and critical code studies, combine as an effective method to visualize ethical positions in algorithms, including bias, mistakes, and interpretability?

to consider bias augmentation, what can be learnt by temporarily isolating the meaning in data, to focus on the effect that structure and process play in the generation of bias?

what does it mean to learn, in machine learning systems, and is anthropomorphism a productive analogy?