“That’s what techquity is”: youth perceptions of technological and algorithmic bias
| DOI | https://doi.org/10.1108/ILS-03-2022-0024 |
| Published date | 02 August 2022 |
| Date | 02 August 2022 |
| Pages | 500-525 |
| Subject Matter | Library & information science,Librarianship/library management,Library & information services |
| Author | Merijke Coenraad |
“That’s what techquity is”: youth
perceptions of technological and
algorithmic bias
Merijke Coenraad
Digital Promise, Washington, D.C., USA
Abstract
Purpose –Computing technologyis becoming ubiquitous within modern society and youth use technology
regularly for school, entertainment and socializing. Yet, despite societal belief that computing technologyis
neutral, the technologies of today’s society are rife with biases that harm and oppress populations that
experience marginalization. While previous research has explored children’s values and perceptions of
computing technology, few studieshave focused on youth conceptualizations of this technological bias and
their understandings of how computingtechnology discriminates against them and their communities. This
paper aimsto examine youth conceptualizations of inequitiesin computing technology.
Design/methodology/approach –This study analyzes a series of codesign sessions and artifacts
partnering with eight black youth to learn about their conceptualizations of te chnology bias.
Findings –Without introduction, the youth demonstrated an awareness of visible negative impacts of
technologyand provided examples of this bias within theirlives, but they did not have a formal vocabulary to
discuss said bias or knowledge of biased technologies less visible to the naked eye. Once presented with
common technological biases, the youth expanded their conceptualizations to include both visible and
invisiblebiases.
Originality/value –This paper buildson the current body of literature around how youth viewcomputing
technologyand provides a foundation to ground futurepedagogical work around technologicalbias for youth.
Keywords Technology bias, Techquity, Equity, Youth, Cooperative inquiry, Codesign
Paper type Research paper
Introduction
While the prevalence of computing technology within society has grown, the influence of
computers on people’s lives goes unrecognized and people often view as neutral those
computers that they do acknowledge (Pew Research Center, 2018). But the computing
technologies that daily affect the lives of individuals globally are not neutral, as people
commonly assume, nor is it likely that a personin modern society will live a life unaffected
by these technologies, even if they do not use them themselves (Winner, 1980). Computing
devices and the algorithms that run them are now part of many everyday objects.
Government bodies use algorithms to make influential decisions about the likelihood of
recidivism in sentencing (Angwin et al.,2016;Garvie, Bedoya and Frankle, 2016) and
welfare eligibility (Eubanks, 2018). The computing technology of today’s world passes on
the biases of its creators and of society as a whole, leading to technologies that treat users
Thank you to Dr David Weintrop and Dr Elizabeth Bonsignore for their support in developing this
article and for providing comments on early drafts. Additionally, thank you to Dr Tamara Clegg, Dr
Diana Franklin, Dr Diane Jass Ketelhut and Dr Jean Ryoo for their mentorship and support
throughout my dissertation. Finally, I would like to thank the youth codesigners who partnered with
me on this work and readily shared their thoughts, ideas and opinions.
ILS
123,7/8
500
Received1 March 2022
Revised22 June 2022
28June 2022
Accepted28 June 2022
Informationand Learning
Sciences
Vol.123 No. 7/8, 2022
pp. 500-525
© Emerald Publishing Limited
2398-5348
DOI 10.1108/ILS-03-2022-0024
The current issue and full text archive of this journal is available on Emerald Insight at:
https://www.emerald.com/insight/2398-5348.htm
differently as a result of their race, gender, class, ability or a combination of these factors
(Buolamwini and Gebru, 2018;Eubanks, 2018;Benjamin, 2019;Whittaker et al.,2019;
Scheuerman et al.,2020).
I call this phenomenon Threats to Techquity (Techquity is a portmanteau of
“technology”and “equity”) and define Threats to Techquity as those aspects of computing
and technologies that cause or could cause inequities, especially inequity based on
marginalized identities. Threats to Techquity include identified instances of algorithmic or
technological bias, such as those previously mentioned, as well as instance of inequity
caused or exacerbated by computing technologies that have not yet been identified. While
the use of the term “Threats to Techquity”is unique within my work, this concept draws
from and is related to the work of other fields and researchgroups. It brings together work
on singular issues [e.g. data and datafication, artificial intelligence (AI), privacy] under an
overarching thematic umbrella focused on how computing technologies and technological
systems can cause or perpetuateinequalities within society.
With regards to user privacy and the protection of user data, Threats to Techquity
overlap with cybersecurity and privacy research (Kumar and Schoenebeck, 2015;Zhang-
Kennedy et al.,2016;Kumar et al.,2017).Threats to Techquity relating to data collection and
the use of user data align to work on data justice and the importanceof foregrounding social
justice concerns and societal inequality in conversations about data and surveillance
(Dencik et al.,2016;Taylor, 2017;Dencik et al., 2019). Additionally, Threats to Techquity
related to or caused by AI systems relateto and are drawn from the work of members of the
Algorithmic Justice League(The Algorithmic Justice League, 2020), AI Now (Crawford et al.,
2019) and other groups focusedon equitable and accountable AI.
Furthermore, Threats to Techquity encompass ideas and concepts by foundational
researchers focused on the impacts of technology on society including Ruha Benjamin’s
definition of the “New Jim Code”(Benjamin, 2019), Cathy O’Neil’s characterization of
algorithms as “Weapons of Math Destruction”(O’Neil, 2016), Safiya Umoja Noble’s
making visible instances of “technological redlining”(Noble, 2018) and Virginia
Eubank’s depiction of the “digital poorhouse”caused by algorithms, databases and risk
models (Eubanks, 2018). Beyond the Threats to Techquity themselves, ideas around
educating youth and other users about Threats to Techquity relate to work extending
computational thinking to include opportunities to combat societal challenges through
computational action (Tissenbaum et al., 2019) and youth understanding and critically
engaging with the effects of digital technologies on society through computational
empowerment (Iversen et al., 2018).
Given the continuous introduction of newcomputing technologies and the prevalence of
data collection and algorithmic decision making within society, Threats to Techquity are
continuously changing and new threats are designed,developed and identified. Therefore, a
concrete or finite list of Threats to Techquity cannot be developed. Instead, Threats to
Techquity are identified within this work when computing technology or technological
systems create, exacerbateor perpetuate societal inequalities. Just as many computers work
invisibly within society, so does this bias. Therefore, it is important to understand youth
conceptualizations of not only a singular aspect of the thematic umbrella Threats to
Techquity, but ratherof the entire body of work and the varied examples it brings together.
With society becoming more centered on computing technologies, the influence of the
algorithms powering computer-based technologies only increases;with that increase comes
an increased likelihoodthat youth will be exposed to and affected by the biases built into the
computing technologies they use. This means that as youth interact with computing
technology they are encountering visible and invisible Threats to Techquity. Yet, little is
Youth
perceptions
501
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