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"ACM SIGCHI General Interest Announcements (Mailing List)" <[log in to unmask]>
CHI-announcements <[log in to unmask]>
Sat, 9 Sep 2017 20:03:41 +0000
Rick Wash <[log in to unmask]>
Rick Wash <[log in to unmask]>
Emilee Rader <[log in to unmask]>
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We seek participants for a National Science Foundation sponsored workshop
in December 2017 to work together to better understand algorithms that are
currently being used to make decisions for and about people, and how those
algorithms and decisions can be made more trustworthy. We invite interested
scholars to submit whitepapers of no more than 2 pages (excluding
references); attendees will be invited based on whitepaper submissions.
Meals and travel expenses will be provided.

Online algorithms, often based on data-driven machine-learning approaches,
are increasingly being used to make decisions for and about people in
society. One very prominent example is the Facebook News Feed algorithm
that ranks posts and stories for each person, and effectively prioritizes
what news and information that person sees. Police are using “predictive
policing” algorithms to choose where to patrol, and courts are using
algorithms that predict the likelihood of repeat offending in sentencing.
Face recognition algorithms are being implemented in airports in lieu of ID
checks. Both Uber and Amazon use algorithms to set and adjust prices.
Waymo/Google’s self-driving cars are using Google maps not just as a
suggestion, but to actually make route choices.

As these algorithms become more integrated into people’s lives, they have
the potential to have increasingly large impacts. However, if these
algorithms cannot be trusted to perform fairly and without undue
influences, then there may be some very bad unintentional effects. For
example, some computer vision algorithms have mis-labeled African Americans
as “gorillas”, and some likelihood of repeat offending algorithms have been
shown to be racially biased. Many organizations employ “search engine
optimization” techniques to alter the outcomes of search algorithms, and
“social media optimization” to improve the ranking of their content on
social media.

Researching and improving the trustworthiness of algorithmic
decision-making will require a diverse set of skills and approaches. We
look to involve participants from multiple sectors (academia, industry,
government, popular scholarship) and from multiple intellectual and
methodological approaches (computational, quantitative, qualitative, legal,
social, critical, ethical, humanistic).

### Whitepapers

To help get the conversation started and to get new ideas into the
workshop, we solicit whitepapers of no more than two pages in length that
describe an important aspect of trustworthy algorithmic decision-making.
These whitepapers can motivate specific questions that need more research;
they can describe an approach to part of the problem that is particularly
interesting or likely to help make progress; or they can describe a case
study of a specific instance in the world of algorithmic decision-making
and the issues or challenges that case brings up.

Some questions that these whitepapers can address include (but are not
limited to):
* What does it mean for an algorithm to be trustworthy?
* What outcomes, goals, or metrics should be applied to algorithms and
algorithm-made decisions (beyond classic machine-learning accuracy metrics)?
* What does it mean for an algorithm to be fair? Are there multiple
perspectives on this?
* What threat models are appropriate for studying algorithms? For
algorithm-made decisions?
* What are ways we can study data-driven algorithms when researchers don’t
always have access to the algorithms or to the data, and when the data is
constantly changing?
* Should algorithms that make recommendations be held to different
standards than algorithms that make decisions? Should filtering algorithms
have different standards than ranking or prioritization algorithms?
* When systems use algorithms to make decisions, are there ways to
institute checks and balances on those decisions? Should we automate those?
* Does transparency really achieve trustworthiness? What are alternative
approaches to trusting algorithms and algorithm-made decisions?

Please submit white papers along with a CV or current webpage by October 9,
2017 via email to [log in to unmask] We plan to
post whitepapers publicly on the workshop website (with authors’
permission) to facilitate conversation ahead of, at, and after the
workshop. More information about the workshop can be found at

We have limited funding for PhD students interested in these topics to
attend the workshop. Interested students should also submit a whitepaper
with a brief description of their research interests and thoughts on these
topics, and indicate in their email that they are PhD students.

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