Due Process, Algorithms, Auditing Algorithms, Algorithmic Bias, Black Box, Transparency
We are now living in age where algorithms, and the data that feed them, govern a wide variety of decisions in our lives: not just search engines and personalized Netflix suggestions, but educational evaluations, stock market trades and political campaigns, the urban planning, and even how social services like welfare and public safety are managed. Heterogeneous lists like this have become the norm in any critical examination of algorithms, giving the impression of a ubiquitous relevance of algorithms. But algorithms can make mistakes that directly affect individuals and often contain both implicit and explicit biases. The technical complexity of algorithms, the scale at which they operate, and their proprietary nature makes them difficult to scrutinize, creating challenges to fully comprehend of how they exercise their power and influence over society. When used to make legal decisions, questions must be asked as to how automated decision-making systems affect the right to due process afforded to citizens.
The goal of this research project is to augment this discussion by focusing on algorithmic transparency, due process rights, and what can be done to help protect said rights when automated decision-making systems are used. Therefore, the research question that guides this paper is as follows: In what ways do algorithms in legal processes negatively impact an individual’s right to due process and how might the ability to audit legal algorithms help protect due process rights?
To answer this question this research project will present recommendations for research methods, adapted from Communication scholar, Christian Sandvig’s proposed research methods (2014) that can be utilized to audit algorithms so as to provide greater transparency to those on the receiving end of algorithmic judgements within the legal process.
Auditing Algorithms to Preserve Due Process Rights
Master of Arts
Communication, Media and Film
Major Research Paper