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Message posted on 03/12/2018

Call for Abstracts "Applying artificial intelligence on vulnerable target groups: chances and challenges", 18th Annual STS Conference, Graz 2019

Liebe Kolleginnen und Kollegen,

wir möchten Euch gerne auf den folgenden Call for Abstracts zur Session
"Applying artificial intelligence on vulverable target groups: chances and
challenges" aufmerksam machen. Die Session ist Teil der 18th Annual STS
Conference Graz 2019, welche vom 6. bis 7. Mai 2019 stattfindet.

Einreichungsfrist: 21. Januar 2019
Benachrichtigung über ausgewählte Abstracts: Februar 2019
Für weitere Informationen besucht bitte:

Wir freuen uns auf Eure Einreichungen über das Online-Formular

Viele Grüße

Diana Schneider & Scarlet Siebert

S4: Applying artificial intelligence on vulnerable target groups: chances and

SCHNEIDER, Diana (FH Bielefeld - University of Applied Sciences) & SIEBERT,
Scarlet (TH Köln - University of Applied Sciences), Germany

Digitalization in general and artificial intelligence (AI) in particular, e.g.
applications of big data analytics and robotics, are radically changing
society. This applies not only to the world of industry and politics, but also
to an increasing extent to social services like education and healthcare,
where vulnerable groups like children, elderly or disabled people are
targeted. In this context, societal challenges, e.g. the demographic change,
are powerful narratives for a technology-push, that is supposed to foster
self-determination, participation, and equality of these groups. For instance,
applications of smart home shall allow the elderly to stay in their familiar
environment longer (Wessling, 2013), while social robots are supposed to
foster the participation of children with special needs in educational
settings (Dautenhahn et al., 2009; Kim et al., 2013). With the assessment of
big data, unemployed people shall receive adequate offers concerning their job
opportunities (Fanta, 2018) and refugees shall get sufficient treatments
concerning their health (Baeck, 2017). Furthermore, dangers to the welfare of
children shall be identified at an early stage (e.g. Gillingham & Graham,
2016). At the same time, the question arises if technology might transfer
social disparities into the digital world. For instance, algorithms for
predictive policing seem to replicate inequality because they are based on
biased data that leads to accusing ethnic and religious minorities more often
than the white majority (e.g. Tayebi & Glässer, 2018; Datta et al., 2015).
Living in a socially deprived neighbourhood in the analogue world accounts for
a bad digital score, which might then lead to analogously executed
Although AI is already being used in highly sensitive areas such as
kindergartens, welfare state institutions, and authorities, the effects of
this technology on these areas have hardly been researched, if at all. The
assessment of advantages and disadvantages of AI in these areas is still in
its infancy. Therefore, this session seeks to discuss challenges and chances
of the application of AI on vulnerable target groups, that shall function as a
“burning glass” for the current state and future trends of possibilities
to experience self-determination, participation, and equality in a digital
society. These groups include, e.g., children, the elderly, people with
disabilities, unemployed people as well as refugees.
By taking into account different disciplines, the session follows the concept
of integrated research (Stubbe, 2018), that might enable a broader view on the
technological impact on individuals (micro level) and institutions (macro
level) and help answering the following questions systematically (Manzeschke
et al., 2013): In which ways is the application of artificially intelligent
technologies ethically questionable with respect to a certain target group?
Which ethical challenges do emerge from the application of these technologies?
How can these challenges be mitigated or even dissolved? To answer these
questions, we would like to focus on conceptual and theoretical work. However,
empirical findings, that report on challenges or solutions concerning the
application of artificially intelligent technologies on vulnerable target
groups, are welcomed as well.

KEYWORDS: digital society, artificial intelligence, self-determination,
participation, integrated research

Baeck, J.-P. (2017, Mai 29). Überwachungssoftware für Geflüchtete: Der
gläserne Flüchtling. Die Tageszeitung: taz. Abgerufen von!5409816/
Datta, A. et al. (2015): Automated Experiments on Ad Privacy Settings. A Tale
of Opacity, Choice, and Discrimination, In: Proceedings on Privacy Enhancing
Technologies (1), S. 92-112.
Dautenhahn, K., Nehaniv, C. L., Walters, M. L., Robins, B., Kose-Bagci, H.,
Mirza, N. A., & Blow, M. (2009). KASPAR - a minimally expressive humanoid
robot for human-robot interaction research.

Fanta, A. (2018, Oktober 13). Österreichs Jobcenter richten künftig mit
Hilfe von Software über Arbeitslose. Abgerufen 23. Oktober 2018, von

Gillingham, P. & Graham, T. (2016): ”Big Data“ in social work: The
development of a critical perspective on social work´s latest ”electronic
turn“, In: Australian Social Work, March 2016
Kim, E. S., Berkovits, L. D., Bernier, E. P., Leyzberg, D., Shic, F., Paul,
R., & Scassellati, B. (2013). Social robots as embedded reinforcers of social
behavior in children with autism. Journal of Autism and Developmental
Disorders, 43(5), 1038–1049.

Manzeschke, A., Weber, K., Rother, E., & Fangerau, H. (2013). Ergebnisse der
Studie „Ethische Fragen im Bereich Altersgerechter Assistenzsysteme“ (neue
Ausg). Berlin: VDI.
Stubbe, J. (2018). Innovationsimpuls „Integrierte Forschung“.
Diskussionspapier des BMBF-Forschungsprogramms „Technik zum Menschen
bringen". Berlin: VDI/VDE Innovation + Technik GmbH. Abgerufen von

Tayebi, M. A., & Glässer, U. (2018). Social Network Analysis in Predictive
Policing: Concepts, Models and Methods (Softcover reprint of the original 1st
ed. 2016). Springer.
Wessling, C. (2013, Dezember 17). Smart Home für Senioren. Zwischen
Unterstützung und Überwachung. Abgerufen

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