Visual Monitoring of Complex Algorithms
Date
2020Abstract
Purchases, conversations, access to information, music and movies:
more and more of our online life is mediated by complex algorithms that are
designed to make the experience of the Web customized and more “personal”.
These algorithms can process an amount of heterogeneous data that would take
enormous resources for the human mind to cope with, and find valuable
patterns in it. Their use is not limited to our online experience as similar
algorithms have been also implemented, for example, to inform policymakers:
suggesting where to deploy police forces around the urban context, assessing
criminality risk scores of offenders, or allocating high school students to the
most suited school.
While the consequences of the decisions made by algorithms have a great
impact on people’s lives, the way they are built and designed makes them de
facto “black boxes”: a series of legal and technical barriers prevents from
accessing and understanding how a certain input influences a given output.
Overseeing their decision processes becomes then of the utmost importance.
This paper argues that visualizations can become a powerful tool to monitor
algorithms and make their complexity accessible and usable by visually showing
the relation between the inputs and the outputs in a manner that mimics an
observational study approach. The paper analyzes a case study developed as an
experiment to test opportunities and criticalities in using visualization to
represent the presence and the activity of algorithms.
This represents a shift from the main purpose of visualizations and Data
Visualization in general: since its strong suit is to support human decision-
making processes by transforming data into knowledge, the substitution of
people by machines in this activity seems to make visualizations obsolete. A
computer doesn’t need to “see” the data to make a decision – or at least not in
the same way as people do – no matter how multidimensional and hetero-
geneous the data is. With the diffusion of algorithms, the need to inspect their
accountability and performance will simply move visualizations at a later stage.
From a decision-making tool visualization becomes a monitoring and awareness
tool.