Artificially Intelligent Technology for the Margins: A
Multidisciplinary Design Agenda
Franziska Tachtler
TU Wien
Vienna, Austria
franziska.tachtler@tuwien.ac.at
Konstantin Aal
Tanja Ertl
Daniel Diethei
Jasmin Niess
konstantin.aal@uni-siegen.de
tanja.ertl@uni-siegen.de
University of Siegen
Germany
diethei@uni-bremen.de
niessj@uni-bremen.de
University of Bremen
Germany
Mohammed Khwaja
Reem Talhouk
Giovanna Nunes Vilaza
Imperial College London
London, UK
mohammed.khwaja16@imperial.ac.uk
Northumbria University
Newcastle, UK
reem.talhouk@northumbria.ac.uk
Technical University of Denmark
Copenhagen, Denmark
gnvi@dtu.dk
Shaimaa Lazem
Aneesha Singh
Marguerite Barry
City of Scientifc Research and
Technological Applications
Alexandria, Egypt
slazem@srtacity.sci.eg
University College London
London, UK
aneesha.singh@ucl.ac.uk
University College Dublin
Dublin, Ireland
marguerite.barry@ucd.ie
Volker Wulf
Geraldine Fitzpatrick
University of Siegen
Siegen, Germany
volker.wulf@uni-siegen.de
TU Wien
Vienna, Austria
geraldine.ftzpatrick@tuwien.ac.at
ABSTRACT
CCS CONCEPTS
There has been increasing interest in socially just use of Artifcial
Intelligence (AI) and Machine Learning (ML) in the development of
technology that may be extended to marginalized people. However,
the exploration of such technologies entails the development of an
understanding of how they may increase and/or counter marginalization. The use of AI/ML algorithms can lead to several challenges,
such as privacy and security concerns, biases, unfairness, and lack
of cultural awareness, which especially afect marginalized people. This workshop will provide a forum to share experiences and
challenges of developing AI/ML health and social wellbeing technologies with/for marginalized people and will work towards developing design methods to engage in the re-envisioning of AI/ML
technologies for and with marginalized people. In doing so we
will create cross-research area dialogues and collaborations. These
discussions build a basis to (1) explore potential tools to support
designing AI/ML systems with marginalized people, and (2) develop
a design agenda for future research and AI/ML technology for and
with marginalized people.
• Human-centered computing → Participatory design; User
centered design; • Security and privacy → Human and societal aspects of security and privacy; • Computing methodologies → Machine learning; Artifcial intelligence.
Permission to make digital or hard copies of part or all of this work for personal or
classroom use is granted without fee provided that copies are not made or distributed
for proft or commercial advantage and that copies bear this notice and the full citation
on the frst page. Copyrights for third-party components of this work must be honored.
For all other uses, contact the owner/author(s).
CHI ’21 Extended Abstracts, May 8–13, 2021, Yokohama, Japan
© 2021 Copyright held by the owner/author(s).
ACM ISBN 978-1-4503-8095-9/21/05.
https://doi.org/10.1145/3411763.3441333
KEYWORDS
AI, ML, Data, Privacy, Security, Marginalized people, Global South,
HCI4D, ICT4D, Ethics
ACM Reference Format:
Franziska Tachtler, Konstantin Aal, Tanja Ertl, Daniel Diethei, Jasmin Niess,
Mohammed Khwaja, Reem Talhouk, Giovanna Nunes Vilaza, Shaimaa
Lazem, Aneesha Singh, Marguerite Barry, Volker Wulf, and Geraldine
Fitzpatrick. 2021. Artifcially Intelligent Technology for the Margins: A
Multidisciplinary Design Agenda. In CHI Conference on Human Factors
in Computing Systems Extended Abstracts (CHI ’21 Extended Abstracts),
May 8–13, 2021, Yokohama, Japan. ACM, New York, NY, USA, 7 pages.
https://doi.org/10.1145/3411763.3441333
1 BACKGROUND
Artifcial Intelligence (AI) is increasingly being promoted and developed for diferent areas such as the public sector, social care and
public health. Indeed, such technologies are being applied and/or
proposed as tools for addressing multiple factors that constitute
the social determinants of health and not just clinical healthcare
services. Especially in the context of marginalized communities,
CHI ’21 Extended Abstracts, May 8–13, 2021, Yokohama, Japan
the capability to navigate social and welfare services, such as housing, educational and rehabilitation services, are as important to
promoting health and social wellbeing as navigating healthcare
services [42, 43]. Researchers, management and strategy consulting
companies argue for the potential of integrating AI technologies for
the social good as they are projected to improve decision making
and the efective management of public- and social-sector entities
[9], catch fraud in the insurance system [32], enable automated
diagnostic systems in remote areas [13] or help allocating homeless
youth to housing services [4]. Furthermore, De-Artega et al. [12]
introduced the term machine learning for the developing world
(ML4D), where they highlight the potential for ML technologies,
including applications and data, in addressing the societal challenges in developing countries. The authors present a road map
for future work on ML4D, suggesting to improve data reliability,
provide direct solutions and inform policy and decision makers.
However, despite the increasing interest in the design, development and deployment of AI/ML technologies, gaps still remain
regarding its adoption by marginalized groups (e.g., the ‘Global
South’, people of lower socio-economic status, refugees, asylum
seekers, homeless people, prisoners and people with multiple disabilities). Recent works highlight that challenges around technology
design for marginalized people have persisted and are more pressing than ever [40]. For instance, not taking into the account the
specifc needs of marginalized communities in the design of AI/ML
health technologies lead to the widening of treatment gaps [31],
and potential harms concerning data-injustice [11, 15]. There is a
clear need for further eforts to design with and for the margins,
especially when it comes to using artifcially intelligent approaches
[25]. The use of AI in the public social, welfare and health services
leads to a vicious cycle of AI-based discrimination that may further
marginalize people [8].
Therefore, we propose to use this one-day workshop as a space
for HCI and AI researchers, designers and third and public sector
support organisations to come together and discuss cases, experiences and challenges that relate to AI/ML technologies for and with
marginalized people. In particular, we want to focus on exploring
and developing novel design methods and approaches to engage
with marginalized people in the design of AI/ML technologies as
well as providing a set of overarching practice principles to guide
our community’s work.
This workshop is motivated by two main research gaps identifed
in the literature. First, knowledge and data obtained from studies
with ‘mainstream’ groups cannot be directly applied to minorities.
There is, therefore, a need to work with marginalized people to
overcome challenges in obtaining data, related to lack of trust and
transparency, to build more inclusive data sets and algorithms.
Second, as a starting point to tackle this issue, there should be
more eforts on the development of design tools to engage the wide
variety of diferent marginalized people and take their diverse needs
into account. We expand on these points in the following sections.
1.1 Moving from the Mainstream to the
Margins
A successful deployment of AI/ML technologies requires the creation of relevant data sets based on data shared by end-user groups.
Tachtler, et al.
Ownership, data control and knowledge are also key elements in existing data-justice principles [22]. However, in a recent systematic
review of HCI literature on ML systems for mental health technologies, the authors emphasize that challenges in the development
of ML solutions have even bigger consequences for marginalized
people. First, the accuracy of AI/ML solutions depends on where
and with which populations the algorithms are developed and
trained, which means that models created for one context cannot
be transferred and deployed in another [40]. Such challenges may
be extended to other health related domains and to those within
social care and the public sector (e.g., housing). Given that mainstream eforts within AI applications are constrained to a select set
of nations and groups, the results may further reinforce discrimination against minorities [46]. In addition, demographic disparities
may be further magnifed in the context of sensitive domains where
stigma already hinders engagement in data collection processes
[16, 36]. Therefore, it is an open challenge about how future initiatives should strive for more diversity and inclusion in order to
mitigate the risk of bias.
Furthermore, designing for trust among marginalized people
is essential, as shown by previous studies with, refugees, asylum
seekers [16, 36], homeless youth [1], and prisoners [17]. One of
our goals in this workshop is to develop a deeper understanding
of whether and how vulnerable populations develop trust in AIdriven services and to work on a more diferentiated picture of
their trusting stance towards such technology. For instance, in
line with work from McKnight et al. [29] on trust in technology,
previous research on marginalized people suggests that one has to
diferentiate between the lack of initial trust as a barrier to starting
engaging with AI-based technology (e.g., sharing data) and the lack
of knowledge-based trust [29], which builds on previous trustortrustee interactions (e.g., distrust while already using AI-based
technology). In addition to the diferent types of trust, contextual
factors also seem to be an essential factor when building trust. A
recent study suggests that public trust in AI is lower for sensitive
contexts [3].
To illustrate, marginalized people such as refugees and asylum
seekers have been shown to have high privacy and security concerns [24] coupled with low agency in detailing what data they are
willing to share [10, 33]. In addition, stigma and vulnerability are
some of the harmful consequences of data leakage, which may have
a stronger impact within already marginalized people [28]. The lack
of trust and fear of being treated negatively additionally hinders
accessing services and causes further marginalization [27, 36] but
is also a barrier to accessing technological driven services. Previous
research shows that transparency and choice over how the data
collected is used (and re-used) have an important role in the perception of trustworthiness of the technologies using/asking to share
data, but existing initiatives are failing to provide that [44]. Thus,
we need better tools and methods to support designing more trustworthy technologies which provide reassurance to marginalized
people who had negative experiences with diferent services and
fear discrimination.
Artificially Intelligent Technology for the Margins: A Multidisciplinary Design Agenda
1.2
Striving to Design for Inclusion and
Diversity
The aforementioned challenges are underpinned by the lack of
meaningful and active inclusion of marginalized people in the design of AI/ML technologies. However, designing with marginalized
people presents its own challenges [7, 19, 35, 45]. It has been identifed that even in non-marginalized contexts there is a lack of designing human-friendly which involve target-populations [40, 47].
In previous CHI workshops it has been emphasized that HCI researchers need more accessible, intuitive and easier-to-use tools to
facilitate an inclusive design process [21]. Despite recent eforts exploring how to create design tools (e.g., for explainable AI systems
[26]), such works have not been focused on engaging with marginalized people. Consequently, we are aiming to provide a platform
to discuss possible starting points on how to engage marginalized
people in the design process.
Researchers working with people with disabilities argue for including people with disabilities in the development of models of AI
systems and point out inclusive design, participatory design and
value based design as promising approaches [41]. In addition, successful intercultural communication within the design process can
promote its implication in the technical development, which in turn
helps building trust through the use of the application itself [16].
Indeed, communicating the nature of the technology through participatory or community-based design processes [20, 48ś50] may
not only increase the understanding among marginalized groups
but will also enable the design of AI/ML technologies based on
their principles and understanding of data-justice, trust and transparency.
However, diferent marginalized groups have diferent needs [18,
40]. For instance, prisoners’ opportunities for technical participation in the design process are massively restricted as they are not
trusted to use technologies without criminal intention. Additionally,
post-colonial HCI has identifed several intercultural challenges of
designing technologies in the ‘Global South’ [6, 23]. Overall, the
aforementioned literature highlights the need to develop a better
understanding of how to design AI/ML technologies with and for
specifc marginalized populations in order to counter challenges
related to transferability and data-justice.
1.3
CHI ’21 Extended Abstracts, May 8–13, 2021, Yokohama, Japan
Workshop Goals
Designing inclusive AI/ML technologies is a feld that involves
stakeholders from multiple areas with a variety of diferent backgrounds. Consequently, we aim to utilise virtual platforms to broaden
participation in our one-day workshop. We are striving to include
participants with a variety of diferent backgrounds such as HCI
scholars, AI/ML researchers, non-academic community organisations and people from marginalized communities Ð that either have
experience/interest in AI/ML technologies, and/or in working with
marginalized people.
The workshop will promote knowledge and experience exchange
particularly in relation to the previously outlined challenges. Through
the workshop we aim to formulate novel approaches/methods and
principles for designing AI/ML technologies for and with marginalized people.
The key objectives of this workshop are:
(1) bringing together communities working with marginalized
people and exchanging experiences on working with developing and investigating data driven technologies
(2) identifying and developing a design agenda for how to design/develop AI/ML technologies with/for marginalized people and for the design itself of the AI/ML driven technologies
(3) ideating processes and tools for designing/ developing AI/ML
technologies with/for marginalized people.
These key objectives lead to the following workshop outputs:
(1) the creation of a community of cross-disciplinary researchers/
practitioners working with AI/ML technologies and/or marginalized people
(2) a collection of guiding principles supporting designing/developing
AI/ML technologies with/for marginalized people in the future
(3) a collection of initial tools/methods supporting the application of these principles.
2
ORGANIZERS
This group of workshop organizers refect expertise in the intersection of working with marginalized people in multiple geographical
contexts and AI/ML and data driven technologies. Additionally, the
organizers have experience running successful workshops at CHI
and other ACM and afliated venues [2, 5, 14, 30, 34, 37ś39].
Franziska Tachtler is a PhD student and research associate at
HCI Group, TU Wien. Her research focuses on the role of technology in promoting resilience of young asylum-seekers and their
social-ecological systems. She works with young asylum-seekers,
volunteer organizations and mental health service providers using
participatory engagement methods.
Konstantin Aal is a PhD student and a research associate at
the Institute for Information Systems and New Media, University
of Siegen. He is part of come_ IN, a research project which founded
several computer clubs for children and their relatives including
refugees. His current research focuses on technology appropriation
by local communities in the Global South.
Tanja Ertl is a PhD student at the Institute for Information Systems and New Media, University of Siegen. Based on the Grounded
Design approach, her research focuses on the promotion of wellbeing of marginalized groups by transforming therapeutic methods
into self-help tools to bridge the gaps in state care that currently
exist to support integration and resocialization processes.
Daniel Diethei is a PhD student and researcher at the University
of Bremen, Germany. As a member of the Leibniz Science-Campus
Digital Public Health he works with digital technologies in medicine
and healthcare with a focus on underserved populations.
Jasmin Niess is a postdoctoral researcher at the University of
Bremen, Germany. Her research focuses on the design and evaluation of technologies that improve wellbeing. Her past experiences
in academia and socially-relevant work engagements make her
particularly enthusiastic about conducting research that improves
societal wellbeing.
Mohammed Khwaja is a research scientist and a PhD student
at Imperial College London. He investigates how data science and
psychology can be merged to personalise digital interventions. His
CHI ’21 Extended Abstracts, May 8–13, 2021, Yokohama, Japan
previous research included the exploration of diferences in machine
learning prediction models across diferent cultures.
Reem Talhouk is a Vice Chancellor Research Fellow in the
School of Design and the Centre for International Development
at Northumbria University. She has over 5 years of experience in
designing technologies with refugees and asylum seekers in the
Middle East, Europe and Australia. Her current research focuses
on developing design methods for socially just humanitarian and
global development innovation.
Giovanna Nunes Vilaza is a PhD student and research associate at Department of Health Technology, Technical University
of Denmark. Her current research aims at proposing ethical and
participant-centered futures for open-access data platforms for behavioral monitoring.
Shaimaa Lazem is an Associate Research Professor at SRTACity, Egypt. Her research interests include participatory design,
post-colonial computing, and decolonizing HCI. Her previous projects
included designing heritage documentation applications for rural
populations.
Aneesha Singh is an Assistant Professor of Human Computer
Interaction at the UCL Interaction Centre. She is interested in the
design, adoption and use of personal health and wellbeing technologies in everyday contexts. Her research focuses on digital health,
ubiquitous computing, multisensory feedback and wearable technology, especially in sensitive and stigmatised populations.
Marguerite Barry is Lecturer and Assistant Professor at the
School of Information & Communication Studies at University
College Dublin. Her research focuses on developing practical ethical
frameworks to inform design of AI technologies.
Volker Wulf is a computer scientist with an interest in the area
of IT system design in real-world contexts, this includes the development of innovative applications from the areas of cooperation
systems, knowledge management and community support. He is
head of the Institute for Information Systems and New Media at
the University of Siegen.
Geraldine Fitzpatrick is Professor of Technology Design and
Assessment and heads the Human Computer Interaction Group
at TU Wien. She is also an ACM Distinguished Scientist, IFIP TC13 Pioneer Award recipient, and IFIP Fellow. Her research is at
the intersection of social and computer sciences, with a particular
interest in supporting wellbeing, community building and active
engagement.
3
PRE-WORKSHOP PLANS
The workshop will be promoted through a dedicated website that
will communicate the aims and structure of the workshop and will
also be updated with position papers of workshop participants and
workshop outputs (see https://techforthemargins.wordpress.com).
We will spread the call for submissions through relevant academic
mailing lists (e.g., displaced-hci mailing list, CHI Announcements,
ArabHCI) and social media groups (afriCHI, HCI4d Google Group,
Computing and Mental Health Linkedin group, HCI across borders
Facebook group). We will also actively seek submissions from our
network and contacts, with a special focus on non-academic community organisations working with and for marginalized people
Tachtler, et al.
such as (forced) migrants, as well as social activists within marginalized. A total of 20 participants (excluding organizers) will be invited
to partake in the workshop based on the position papers submitted.
4
WORKSHOP STRUCTURE
The workshop will facilitate interactive activities among participants, clustered based on application areas (e.g., social care) and
the marginalized people they are working with, in order to achieve
workshop goals.
To better facilitate remote participation living across time zones
and exchange among participants, we will ofer pre- and postworkshop activities, such as collecting materials in advance to
provoke discussions at the workshop and fnal refections after the
workshop. Prior to the workshop, we will create a shared platform,
e.g., a google doc sheet, where the participants can introduce themselves and discuss their backgrounds, interests and challenges to
establish a frst exchange.
In addition, we will use platforms such as Well-Sorted to collect and group overarching challenges and themes, and ofer the
possibility to meet pre-conference virtually, which takes place at a
diferent timezone than the conference, to establish frst discussions
and support participants to become familiar with the overall scope
and idea of the workshop. In order to create a social connection
at the workshop, we will ask the participants in advance that they
either keep their video on or have a profle picture of themselves.
Across all phases of the workshop, we will work with digital
platforms such as Miro, Well-Sorted, and Wonder. Wonder will be
used as an inclusive, immersive and interactive space for all by
creating several room areas for discussion and exchange. It is a
robust online tool that workshop co-organisers have trialled with
participants living in contexts of intermittent and weak internet
connectivity. During the workshop co-organisers will act as facilitators and support groups in diferent rooms in documenting the
discussion and adding content to digital platforms. As a backup
conference tool we will use Zoom.
The 4 hour long workshop will consist of three main activities
(see workshop schedule in Table 1). We may adapt the time of the
workshop based on a form to be sent to accepted participants inquiring about their time zones in order to ensure that most participants
can attend. In the case of participants not able to attend all of the
workshop synchronously, participants will have the opportunity
to participate in pre and post workshop activities asynchronously
through contributing discussion points on the digital platforms
Well-Sorted and Miro. By setting up the digital infrastructure in
advance and involving the accepted participants in pre-workshop
activities, we will ensure that participants are familiar with the
tools and know each other in advance. Therefore, the barriers to
participating in the workshop via a video conference tool are much
lower.
Discussions in the activities will be based on the challenges and
experiences presented by participants in position papers.
5
POST-WORKSHOP PLANS
The post-workshop activities will focus on establishing a community that engages in the development of design methods that HCI
researchers can draw on when engaging in the designing of AI/ML
Artificially Intelligent Technology for the Margins: A Multidisciplinary Design Agenda
CHI ’21 Extended Abstracts, May 8–13, 2021, Yokohama, Japan
Timetable (CET)
Activities
13:00-13:15
Welcome
13:15-14:00
Activity 1: Understanding and situating AI/ML technologies in contexts
of marginalization in each application area
14:00-14:15
Break
14:15-15:00
Activity 2: Formulating principles that will guide future work
15:00-15:15
Presenting back
15:15-15:30
Break
15:30-16:30
Activity 3: Developing methods and approaches for designing with
marginalized groups
16:30-16:45
Presenting back
16:45-17:00
Wrap up and next steps
Table 1: Workshop Schedule in CET. We plan to adjust the schedule to the participants’ time zones.
technologies for/with marginalized populations. To support future
collaborations, we will discuss a means of communication through
which participants can support one another, and identify collaborations/ funding streams (e.g., slack channel, series of seminars to
be conducted throughout the year, meetings for further discussion)
for future research in this area. In addition, we will facilitate a postCHI refection meeting to discuss the outputs of the workshop and
exchange our experience of the whole conference. This will help to
create a sense of research community.
We will share all the documentation of the activities and workshop outputs through the workshop websites and in the form of a
public facing blog post and Interactions article. Interested participants will be invited for further discussions on working towards a
special issue in ToCHI that will unpack the challenges and future
of AI/ML technologies and design in contexts of marginalization.
Follow-up workshops and Special Interest Groups at other ACM
and diferent disciplinary conferences in the area of health & social
wellbeing and ML will be held to further discussions and grow the
community interested in this research area.
6
CALL FOR PARTICIPATION
The integration of Artifcial Intelligence (AI) and Machine Learning
(ML) into social, health, and public sectors could bring promising
benefts. However, these technologies lead to several challenges, e.g.,
privacy concerns, unfairness, and the increase of marginalization.
Thus, it is essential that more attention is given on how we design
such systems for/with marginalized people.
This workshop will provide a forum to exchange experiences on
designing technologies for/with marginalized people and AI/ML
driven technologies. We will explore potential tools to support designing AI/ML systems with marginalized people, and develop a
design agenda. We use a broad defnition of marginalized people
that may include, but is not limited to, people of/with low socioeconomic status, disabilities and migration histories.
We invite researchers, practitioners and community members
interested in the aforementioned topic to submit a 2-4 page position
paper as PDF using ACM Master Article Template. Submissions
should critically refect on current projects and/or present challenges and approaches. In particular, we encourage submissions
that refect on:
• case studies with marginalized people and/or AI/ML,
• design methods used with marginalized people that may be
transferable to the design of AI/ML technologies,
• past experiences illustrating positive or negative outcomes
of AI/ML in this context.
Submissions should be sent to techforthemargins@gmail.com
by February 3. Acceptance will be based on quality, relevance, and
diversity and will be communicated by February 21. At least one
author of each accepted position paper must register and attend
the workshop and register for at least one day of the conference.
More details on https://techforthemargins.wordpress.com/
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