means that assessing and addressing
bias needs to be part of the product
team’s normal goal-setting processes,
while in-depth research potentially
has to be resourced separately so
it does not weigh on teams’ local
roadmaps. Education efforts have
to be structured in a manner that
doesn’t appear as yet another
training. In our pilot, we started to
include algorithmic bias in existing
machine-learning courses for
different internal audiences, and
in both engineering and diversity
events, rather than requiring special
Companies can highlight the
importance of algorithmic-bias efforts
by including it in company-wide
baseline expectations combined with
specific individual team goals, by
resourcing the simplification of tools
to fit company-specific practices, and
by making (future) dashboards and
meta-datasets widely available. This
does benefit from leadership buy-in, which requires quick executive
summaries on why algorithmic bias is
important and how to best address it
organizationally. Different arguments
will convince different audiences, and
a step-by-step approach is crucial,
rather than a purist approach that
strives to solve everything all at
once or that potentially punishes
developers for sharing issues that they
INCREASING IMPACT AS
A RESEARCH COMMUNITY
Great work is being done in the HCI
and machine-learning communities to
address algorithmic bias; however, in
practice it’s challenging to consume
all of it and even harder to translate
the discussions and findings into
practical approaches for product
teams. Continually following the
scientific literature on fairness
requires specific expertise and focus
not always available in production
settings. Turning research literature
into guidelines applicable in practice
requires a translation that cannot
be expected from all paper authors.
Applied industry researchers
absolutely must be able to fulfill this
mediating role, hopefully aided by
more accessible literature.
Easy-to-use summaries of papers
and the informal sharing of concrete
experiences with both successful and
problematic results is useful in better
understanding what gaps to close, and
how to best have impact. For example,
when inevitable discussions on “what
fair means” arise, summaries such
as the 21 Definitions of Fairness [ 11]
are invaluable points of reference.
When setting up an effort to assess
and address bias, overviews of how
other companies are approaching
the issue are especially helpful and
can motivate prioritization and
resourcing. This also means that
we need to be open to sharing less-
than-ideal learning experiences. In
the majority of cases, teams want to
make the right decisions but don’t
necessarily know how—let’s help
make this easier. In this article,
we have offered insights into our
approach to doing so.
1. Friedman, B. and Nissenbaum, H. Bias in
computer systems. ACM Trans. Inf. Syst.
14, 3 (1996), 330–347.
2. FATML; https://www.fatml.org/
3. ACM Conference on Fairness,
Accountability, and Transparency
(ACM FAT*); https://fatconference.org
4. AI Now institute. Algorithmic impact
assessments: A practical framework
for public agency accountability. Apr.
5. Data & Society. Algorithmic
accountability primer. Apr. 2018;
6. World Wide Web Foundation.
Algorithmic accountability report, 2017;
7. Gebru, T., Morgenstern, J., Vecchione,
B., Wortman Vaughan, J., Wallach,
H., Daumeé III, H., and Crawford, K.
Datasheets for datasets. 2018; https://
8. Olteanu, A., Castillo, C., Diaz, F.,
and Kiciman, E. Social data: Biases,
methodological pitfalls, and ethical
boundaries. 2016; http://dx.doi.
9. Baeza-Yates, R. Data and algorithmic
bias in the web. Proc. of the 8th
ACM Conference on Web Science.
ACM, New York, 2016; https://doi.
10. Springer, A. and Cramer, H. “Play
PRBLMS”: Identifying and correcting less
accessible content in voice interfaces. Proc.
of CHI ’ 18. ACM, New York, 2018.
11. Narayanan, A. FAT* 2018 tutorial: 21
fairness definitions and their politics.
Henriette Cramer is a lab lead at Spotify,
where her research has focused on voice
interactions and road managing Spotify’s
algorithmic accountability effort. She is
especially interested in data and design
decisions that affect machine-learning
outcomes, and the (mis)match between
machine models and people’s perceptions.
Jean Garcia-Gathright is a machine-learning engineer and researcher at Spotify,
where she specializes in the evaluation of
data-driven models that power engaging,
personalized user experiences.
Aaron Springer is a Ph. D. candidate
at University of California Santa Cruz. His
research focuses on the user experience of
machine learning, including fairness, trust,
Sravana Reddy is a researcher at Spotify
working in natural language processing
and machine learning. Her interests lie in
computational sociolinguistics, NLP for
creative language, audio processing, and of
late, fairness and bias in ML. She received her
Ph.D. from the University of Chicago.
DOI: 10.1145/3278156 COP YRIGH T HELD BY AUTHORS. PUBLICATION RIGHTS LICENSED TO ACM. $15.00
Figure 4. Example of voice-recognition issues within the music domain.
Dile Que Tu Me Quieres
by Val how