With the recent development in artificial intelligence and machine learning, most notably in neural networks and deep learning, they are used to make important decisions in critical areas of life. However, the complex behaviour of these algorithms impede our ability to understand how decisions for and about us are made. In 2016, Mike Ananny and Kate Crawford wrote a paper, “Seeing without knowing: Limitations of the transparency ideal and its application to algorithmic accountability,” where they highlight the inadequacy of transparency for understanding and governing algorithmic systems. Over the last few years, the field of Explainable AI (XAI) has rapidly developed to include a variety of techniques—ante and post hoc; global and local; numeric, textual, rules-based, visual. The XAI response to the inherent opacity of algorithmic systems, while critical, runs the risk of demonstrating the fears in Ananny and Crawford’s paper. We may see the production of an enormous trove of transparency driven information, without the means to understand it. 

Knowing without Seeing is an attempt to critically question the transparency ideal in the context of AI systems employing opaque algorithms. This research by Amber Sinha will view transparency as an instrumental value designed to achieve accountability of systems by empowering individuals. To do so, the research will centre the question—what is the meaningful level of transparency that is needed to form a conceptual model of the algorithmic system such that we are able to make enough sense of it to hold it to account. Through literature surveys, interviews, case studies and regulatory review, this project will articulate the philosophical contours of this version of algorithmic transparency which privileges understanding over merely accessing information, and speculate how it may be delivered in practice, and assimilated in regulation. 

This website will be home to a series of long-form essays, supported by regular blog posts and resources that will be published through the course of the project.