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                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: http://sts-conference-graz.tugraz.at





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

challenges



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

punishments.

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



References:

Baeck, J.-P. (2017, Mai 29). Überwachungssoftware für Geflüchtete: Der

gläserne Flüchtling. Die Tageszeitung: taz. Abgerufen von

https://www.taz.de/!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.

http://dx.doi.org/10.1080/11762320903123567



Fanta, A. (2018, Oktober 13). Österreichs Jobcenter richten künftig mit

Hilfe von Software über Arbeitslose. Abgerufen 23. Oktober 2018, von

https://netzpolitik.org/2018/oesterreichs-jobcenter-richten-kuenftig-mit-hilf

e-von-software-ueber-arbeitslose/



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. https://doi.org/10.1007/s10803-012-1645-2



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

https://www.technik-zum-menschen-bringen.de/dateien/service/veranstaltungen/d

iskussionspapier-integrierte-forschung-2018-05-25.pdf



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

vonhttps://www.handelsblatt.com/technik/das-technologie-update/healthcare/sma

rt-home-fuer-senioren-zwischen-unterstuetzung-und-ueberwachung/9223758.html



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