Designing Alternative Representations of Confusion Matrices to Support Non-Expert Public Understanding of Algorithm Performance
Published at
CSCW
| Miami
2020
Abstract
Ensuring effective public understanding of algorithmic decisions that are
powered by machine learning techniques has become an urgent task with the
increasing deployment of AI systems into our society. In this work, we present a
concrete step toward this goal by redesigning confusion matrices for binary
classification to support non-experts in understanding the performance of
machine learning models. Through interviews (n=7) and a survey (n=102), we
mapped out two major sets of challenges lay people have in understanding
standard confusion matrices: the general terminologies and the matrix design. We
further identified three sub-challenges regarding the matrix design, namely,
confusion about the direction of reading the data, layered relations and
quantities involved. We then conducted an online experiment with 483
participants to evaluate how effective a series of alternative representations
target each of those challenges in the context of an algorithm for making
recidivism predictions. We developed three levels of questions to evaluate
users' objective understanding. We assessed the effectiveness of our
alternatives for accuracy in answering those questions, completion time, and
subjective understanding. Our results suggest that (1) only by contextualizing
terminologies can we significantly improve users' understanding and (2) flow
charts, which help point out the direction of reading the data, were most useful
in improving objective understanding. Our findings set the stage for developing
more intuitive and generally understandable representations of the performance
of machine learning models.