A toolkit for algorithmic equity and community empowerment
Author(s)
Katell, Michael
Young, Meg
Herman, Bernease
Dailey, Dharma
Binz, Corinne
Guetler, Vivian
Raz, Daniella
Tam, Aaron
Krafft, P.M.
Issue Date
2020-03-23
Keyword(s)
Participatory design
Surveillance
Regulation
Algorithmic equity
Fairness
Accountability
Transparency
Abstract
A wave of recent scholarship documenting the discriminatory harms of algorithmic systems has spurred widespread interest in algorithmic accountability and regulation. Yet effective accountability and regulation is stymied by a persistent lack of resources supporting public understanding of algorithms and artificial intelligence. We present a toolkit for algorithmic legibility developed using participatory design methodologies. Through interactions with a US-based civil rights organization and their coalition of community organizations, we iden- tify a need for (i) 'street level' heuristics that aid stakeholders in distinguishing between types of analytic and information systems in lay language, and (ii) risk assessment tools for such systems that begin by making algorithms more legible. The present work delivers a toolkit to achieve these aims.
Publisher
iSchools
Series/Report Name or Number
iConference 2020 Proceedings
Type of Resource
text
Language
eng
Permalink
http://hdl.handle.net/2142/106571
Copyright and License Information
Copyright 2020 Michael Katell, Meg Young, Bernease Herman, Dharma Dailey, Corinne Binz, Vivian Guetler, Daniella Raz, Aaron Tam, and P.M. Krafft
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