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Schneider, D., Maier, A., Cimiano, P., Seelmeyer, U. Exploring Opportunities and Risks in Decision Support Technologies for Social Workers: An Empirical Study in the Field of Disabled People’s Services. Sozialer Fortschritt, 71(6-7), 489-511. https://doi.org/10.3790/sfo.71.6-7.489
Schneider, Diana; Maier, Angelika; Cimiano, Philipp and Seelmeyer, Udo "Exploring Opportunities and Risks in Decision Support Technologies for Social Workers: An Empirical Study in the Field of Disabled People’s Services" Sozialer Fortschritt 71.6-7, 2022, 489-511. https://doi.org/10.3790/sfo.71.6-7.489
Schneider, Diana/Maier, Angelika/Cimiano, Philipp/Seelmeyer, Udo (2022): Exploring Opportunities and Risks in Decision Support Technologies for Social Workers: An Empirical Study in the Field of Disabled People’s Services, in: Sozialer Fortschritt, vol. 71, iss. 6-7, 489-511, [online] https://doi.org/10.3790/sfo.71.6-7.489
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Exploring Opportunities and Risks in Decision Support Technologies for Social Workers: An Empirical Study in the Field of Disabled People’s Services

Schneider, Diana | Maier, Angelika | Cimiano, Philipp | Seelmeyer, Udo

Sozialer Fortschritt, Vol. 71 (2022), Iss. 6-7 : pp. 489–511

3 Citations (CrossRef)

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Duncker & Humblot
DOI
https://doi.org/10.3790/sfo.71.6-7.489
Language
German
Pages
23
Keywords

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€ 15.90 (incl. local VAT if applicable)

Author Details

Schneider, Diana, Fraunhofer Institute for Systems and Innovation Research ISI, Competence Center Emerging Technologies, Breslauer Strasse 48, 76139 Karlsruhe.

Maier, Angelika, Bielefeld University, Faculty of Technology, Inspiration 1, 33619 Bielefeld.

Cimiano, Prof. Dr. Philipp, Bielefeld University, Faculty of Technology, Inspiration 1, 33619 Bielefeld.

Seelmeyer, Prof. Dr. phil. Udo, FH Bielefeld University of Applied Sciences, Faculty of Social Sciences, Interaktion 1, 33619 Bielefeld.

Cited By

  1. Handbuch Digitalisierung in Staat und Verwaltung

    Digitalisierung Sozialer Dienste

    Seelmeyer, Udo

    2023

    https://doi.org/10.1007/978-3-658-23669-4_81-1 [Citations: 0]

  2. AI‑based decision support systems and society: An opening statement

    Schneider, Diana | Weber, Karsten

    TATuP - Zeitschrift für Technikfolgenabschätzung in Theorie und Praxis, Vol. 33 (2024), Iss. 1 P.9

    https://doi.org/10.14512/tatup.33.1.9 [Citations: 0]

  3. Ensuring Privacy and Confidentiality in Social Work Through Intentional Omissions of Information in Client Information Systems: a Qualitative Study of Available and Non-available Data

    Schneider, Diana

    Digital Society, Vol. 1 (2022), Iss. 3

    https://doi.org/10.1007/s44206-022-00029-9 [Citations: 2]

References

  1. Abbott, A. (1988): The system of professions. An essay on the division of expert labor, Chicago.  Google Scholar
  2. Ackermann, T. (2020): Risikoeinschätzungsinstrumente und professionelles Handeln im Kinderschutz: Wie Sozialarbeiter_innen mit „Kinderschutzbögen“ interagieren und was das mit Professionalität zu tun hat, Sozial Extra, Advance online publication, https://doi.org/10.1007/s12054-020-00351-x.  Google Scholar
  3. Allhutter, D./Mager, A./Cech, F./Fischer, F./Grill, G. (2020): Der AMS-Algorithmus. Eine Soziotechnische Analyse des Arbeitsmarktchancen-Assistenz-Systems (AMAS): Endbericht, Wien, epub.oeaw.ac.at/ita/ita-projektberichte/2020-02.pdf.  Google Scholar
  4. Barocas, S./Boyd, D. (2017): Engaging the ethics of data science in practice. Communications of the ACM, 60(11), pp. 23–25, https://doi.org/10.1145/3144172.  Google Scholar
  5. Bastian, P. (2014): Statistisch urteilen – professionell handeln. Überlegungen zu einem (scheinbaren) Widerspruch, Zeitschrift für Sozialpädagogik, 12(4), pp. 145–164.  Google Scholar
  6. Bastian, P. (2019): Sozialpädagogische Entscheidungen. Professionelle Urteilsbildung in der Sozialen Arbeit, Leverkusen.  Google Scholar
  7. Berg, M. (1996): Practices of reading and writing. The constitutive role of the patient record in medical work, Sociology of Health & Illness, 18(4), pp. 499–524.  Google Scholar
  8. Börjeson, L./Höjer, M./Dreborg, K.-H./Ekvall, T./Finnveden, G. (2006): Scenario types and techniques: Towards a user’s guide. Futures, 38(7), pp. 723–739, https://doi.org/10.1016/j.futures.2005.12.002.  Google Scholar
  9. Boyd, D. (2016): Undoing the neutrality of big data, Florida Law Review Forum, 67(1), pp. 226–232.  Google Scholar
  10. Braun, M./Hummel, P./Beck, S./Dabrock, P. (2020): Primer on an ethics of AI-based decision support systems in the clinic, Journal of medical ethics, https://doi.org/10.1136/medethics-2019-105860.  Google Scholar
  11. Cairns, I./Jonas, M./Wallis, K. (2018): The ethics of sharing: How do social workers decide what to record in shared health records? Ethics and Social Welfare, 12(4), p. 348–369, https://doi.org/10.1080/17496535.2017.1384849.  Google Scholar
  12. Chiusi, F./Fischer, S./Kayser-Bril, N./Spielkamp, M. (eds.) (2020): Automating society report 2020, AlgorithmWatch, Berlin/Gütersloh, https://automatingsociety.algorithmwatch.org/ [15.12.2020].  Google Scholar
  13. Coeckelbergh, M. (2019): Artificial Intelligence, Responsibility Attribution, and a Relational Justification of Explainability. Science and Engineering Ethics, pp. 2051–2068, https://doi.org/10.1007/s11948-019-00146-8.  Google Scholar
  14. Collingridge, D. (1980): The social control of technology, The Open University Press, Milton Keynes.  Google Scholar
  15. Crawford, K. (2013): The hidden biases in big data, https://hbr.org/2013/04/the-hidden-biases-in-big-data [29.03.2019].  Google Scholar
  16. Evans, M./Hilbert, J. (2020): Zur Zukunft der Arbeit in der Sozial- und Gesundheitswirtschaft in der Digitalisierungsära, in: Nadia Kutscher/Thomas Ley/Udo Seelmeyer et al. (eds.), Handbuch Soziale Arbeit und Digitalisierung, Weinheim, pp. 76–88.  Google Scholar
  17. Foster, K. A./Stiffman, A. R. (2009): Child welfare workers’ adoption of decision support technology. Journal of Technology in Human Services, 27(2), pp. 106–126, https://doi.org/10.1080/15228830902749039.  Google Scholar
  18. Geis, J. R./Brady, A./Wu, C. C./Ranschaert, E. et al. (2019): Ethics of artificial intelligence in radiology. Summary of the joint European and North American multisociety statement, Insights into Imaging 10: 101.  Google Scholar
  19. German Council for Scientific Information Infrastructures (RfII) (2020): The data quality challenge. Recommendations for sustainable research in the digital turn, Göttingen, https://rfii.de/?p=4203 [3.8.2021].  Google Scholar
  20. Gillingham, P. (2019a): Decision support systems, social justice and algorithmic accountability in social work. A new challenge, Practice, 31(4), pp. 277–290.  Google Scholar
  21. Gillingham, P. (2019b): The development of algorithmically based decision-making systems in children’s protective services. Is administrative data good enough? British Journal of Social Work, 50(2), pp. 565–580.  Google Scholar
  22. Gillingham, P. (2019c): Can predictive algorithms assist decision‐making in social work with children and families? Child Abuse Review, 28(2), pp. 114–126, https://doi.org/10.1002/car.2547.  Google Scholar
  23. Gillingham, P. (2016): Big data in social welfare. The development of a critical perspective on social work’s latest “electronic turn”, Australian Social Work, 70(2), pp. 135–147.  Google Scholar
  24. Greilich, T. (2019): Predictive Policing. Wenn die Polizei in die Zukunft schaut. Serie: Digitalisierung der Polizeiarbeit. Wegweiser Verwaltung Der Zukunft, Wegweiser Media & Conferences GmbH Berlin, https://www.vdz.org/oeffentliche-sicherheit/predictive-policing.  Google Scholar
  25. Grunwald, A. (2019): Technology assessment in practice and theory, London/New York.  Google Scholar
  26. Hoose, F./Schneiders, K./Schönauer, A.-L. (2021): Von Robotern und Smartphones. Stand und Akzeptanz der Digitalisierung im Sozialsektor, in: Wunder, M. (ed.), Digitalisierung und Soziale Arbeit: Transformationen, Beharrungen, Herausforderungen, Bad Heilbrunn, pp. 97–109, https://doi.org/10.35468/5909-07.  Google Scholar
  27. Institute of Electrical and Electronics Engineers (Ed.) (1990): IEEE standard glossary of software engineering terminology, www.informatik.htw-dresden.de/~hauptman/SEI/IEEE_Standard_Glossary_of_Software_Engineering_Terminology%20.pdf [9.8.2021].  Google Scholar
  28. Kolleck, A./Orwat, C. (2020): Mögliche Diskriminierung durch algorithmische Entscheidungssysteme und maschinelles Lernen – ein Überblick: TAB-Hintergrundpapier Nr. 24.  Google Scholar
  29. Kreidenweis, H. (2018): Digitalisierung ändert nichts – außer alles: Chancen und Risiken für Einrichtungen der Behindertenhilfe. Praxis Und Management, 57(3), pp. 122–125.  Google Scholar
  30. Larson, J./Mattu, S./Kirchner, L./Angwin, J. (2016): How we analyzed the COMPAS recidivism algorithm, https://www.propublica.org/article/how-we-analyzed-the-compas-recidivism-algorithm.  Google Scholar
  31. Latour, B. (1996): On actor-network theory. A few clarifications, Soziale Welt, 47(4), pp. 369–381.  Google Scholar
  32. Liedgren, P./Elvhage, G./Ehrenberg, A./Kullberg, C. (2016): The use of decision support systems in social work: A scoping study literature review. Journal of Evidence-Informed Social Work, 13(1), pp. 1–20, https://doi.org/10.1080/15433714.2014.914992.  Google Scholar
  33. Maier, A./Cimiano, P. (2020): Predicting independent living outcomes from written reports of social workers, Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science, Association of Computational Linguistics, pp. 139–148.  Google Scholar
  34. Merchel, J./Tenhaken, W. (2015): Dokumentation pädagogischer Prozesse in der Sozialen Arbeit. Nutzen durch digitalisierte Verfahren, in: Kutscher, N./Ley, T./Seelmeyer, U. (eds.), Mediatisierung (in) der sozialen Arbeit, Baltmannsweiler, pp. 171–191.  Google Scholar
  35. Monnickendam, M./Savaya, R./Waysman, M. (2005): Thinking processes in social workers’ use of a clinical decision support system: A qualitative study. Social Work Research, 29(1), pp. 21–30, https://doi.org/10.1093/swr/29.1.21.  Google Scholar
  36. Nami, M. R./Bertels, K. (2007): A survey of autonomic computing systems, Third international conference on autonomic and autonomous systems (ICAS’07), IEEE, pp. 26–31.  Google Scholar
  37. Neri, E./Coppola, F./Miele, V./Bibbolino, C./Grassi, R. (2020): Artificial intelligence. Who is responsible for the decision? Radiol med, 125, pp. 517–521.  Google Scholar
  38. Raji, D. (2020): How our data encodes systematic racism. Technologists must take responsibility for the toxic ideologies that our data sets and algorithms reflect, MIT Technology Review, https://www.technologyreview.com/2020/12/10/1013617/racism-data-science-artificial-intelligence-ai-opinion/ [21.01.2021].  Google Scholar
  39. Schneider, D. (2020): Decision Support Systeme in der Sozialen Arbeit. Herausforderungen an die Rolle der TA in Innovationsprozessen, in: Nierling, L./Torgersen, H. (eds.), Die neutrale Normativität der Technikfolgenabschätzung. Konzeptionelle Auseinandersetzung und praktischer Umgang, Baden-Baden, pp. 117–138.  Google Scholar
  40. Schneider, D. (2022): „das braucht die Technik nicht alles zu wissen“. Digitale Datenerfassung im Spannungsfeld zwischen Privatheit, Datenschutz und gesellschaftlichem Auftrag, in: Friedewald, M./Kreutzer, M./Hansen, M. (eds.), Selbstbestimmung, Privatheit und Datenschutz. Gestaltungsoptionen für einen europäischen Weg. DUD Fachbeiträge, Wiesbaden, pp. 241–260.  Google Scholar
  41. Schneider, D. (in press): Ethische und professionsspezifische Herausforderungen im Diskurs um algorithmische Systeme der Entscheidungsunterstützung im Kontext der Teilhabeplanung für Menschen mit Behinderung, in: Sonar, A./Weber, K. (eds.), Künstliche Intelligenz und Gesundheit, Stuttgart, pp. 87–132.  Google Scholar
  42. Schneider, D./Seelmeyer, U. (2019): Challenges in using big data to develop decision support systems for social work in Germany, Journal of Technology in Human Services, 37(2–3), pp. 113–128.  Google Scholar
  43. Schneider, D./Sonar, A./Weber, K. (2022): Zwischen Automatisierung und ethischem Anspruch. Disruptive Effekte des KI-Einsatzes in und auf Professionen der Gesundheitsversorgung, in: Pfannstiel, M. (ed.), Künstliche Intelligenz im Gesundheitswesen, Wiesbaden, pp. 325–348.  Google Scholar
  44. Schomberg, R. von (2015): Responsible Innovation: The new paradigm for science, technology and innovation policy, in: Bogner, A./Decker, M./Sotoudeh, M. (eds.), Gesellschaft – Technik – Umwelt. Neue Folge: Vol. 18. Responsible Innovation: Neue Impulse für die Technikfolgenabschätzung? Baden-Baden, pp. 47–70.  Google Scholar
  45. Schrödter, M./Bastian, P./Taylor, B. (2018): Risikodiagnostik in der Sozialen Arbeit an der Schwelle zum „digitalen Zeitalter“ von Big Data Analytics, Preprint, Advance online publication, https://doi.org/10.13140/RG.2.2.22119.14240.  Google Scholar
  46. Shiller, U./Strydom, M. (2018): Evidence-based practice in child protection services: Do we have time for this? Social Work, 54(4), https://doi.org/10.15270/54-4-669.  Google Scholar
  47. Spielkamp, M. (Ed.) (2019): Automating society. Taking stock of automated decision making in the EU, A report by AlgorithmWatch in cooperation with Bertelsmann Stiftung, supported by the Open Society Foundations, AW AlgorithmWatch gGmbH, www.algorithmwatch.org/automating-society [18.03.2019].  Google Scholar
  48. Ückert, S./Sürgit, H./Diesel, G. (Eds.) (2020): Digitalisierung als Erfolgsfaktor für das Sozial- und Wohlfahrtswesen, Baden-Baden.  Google Scholar
  49. van der Put, C. E./Hermanns, J./van Rijn-van Gelderen, L./Sondeijker, F. (2016): Detection of unsafety in families with parental and/or child developmental problems at the start of family support, BMC Psychiatry, 16, 15, https://doi.org/10.1186/s12888-016-0715-y.  Google Scholar
  50. Van Someren, M. W./Barnard, Y. F./Sandberg, J. A. C. (1994): The think aloud method. A practical approach to modelling cognitive processes, London.  Google Scholar
  51. Wachter, S./Mittelstadt, B./Floridi, L. (2017): Transparent, explainable, and accountable AI for robotics: To create fair and accountable AI and robotics, we need precise regulation and better methods to certify, explain, and audit inscrutable systems, Science Robotics, 2(6), https://doi.org/10.1126/scirobotics.aan6080.  Google Scholar
  52. Weber, K. (2013): What is it like to encounter an autonomous artificial agent? AI & Soc, 28(4), pp. 483–489.  Google Scholar
  53. Weizenbaum, J. (1976): Computer power and human reason. From judgement to calculation, W. H. Freeman & Co Ltd.  Google Scholar
  54. Zweig, K. A./Fischer, S./Lischka, K. (2018): Wo Maschinen irren können. Fehlerquellen und Verantwortlichkeiten in Prozessen algorithmischer Entscheidungsfindung, Arbeitspapier, Bertelsmann Stiftung.  Google Scholar
  55. Abbott, A. (1988): The system of professions. An essay on the division of expert labor, Chicago.  Google Scholar
  56. Ackermann, T. (2020): Risikoeinschätzungsinstrumente und professionelles Handeln im Kinderschutz: Wie Sozialarbeiter_innen mit „Kinderschutzbögen“ interagieren und was das mit Professionalität zu tun hat, Sozial Extra, Advance online publication, https://doi.org/10.1007/s12054-020-00351-x.  Google Scholar
  57. Allhutter, D./Mager, A./Cech, F./Fischer, F./Grill, G. (2020): Der AMS-Algorithmus. Eine Soziotechnische Analyse des Arbeitsmarktchancen-Assistenz-Systems (AMAS): Endbericht, Wien, epub.oeaw.ac.at/ita/ita-projektberichte/2020-02.pdf.  Google Scholar
  58. Barocas, S./Boyd, D. (2017): Engaging the ethics of data science in practice. Communications of the ACM, 60(11), pp. 23–25, https://doi.org/10.1145/3144172.  Google Scholar
  59. Bastian, P. (2014): Statistisch urteilen – professionell handeln. Überlegungen zu einem (scheinbaren) Widerspruch, Zeitschrift für Sozialpädagogik, 12(4), pp. 145–164.  Google Scholar
  60. Bastian, P. (2019): Sozialpädagogische Entscheidungen. Professionelle Urteilsbildung in der Sozialen Arbeit, Leverkusen.  Google Scholar
  61. Berg, M. (1996): Practices of reading and writing. The constitutive role of the patient record in medical work, Sociology of Health & Illness, 18(4), pp. 499–524.  Google Scholar
  62. Börjeson, L./Höjer, M./Dreborg, K.-H./Ekvall, T./Finnveden, G. (2006): Scenario types and techniques: Towards a user’s guide. Futures, 38(7), pp. 723–739, https://doi.org/10.1016/j.futures.2005.12.002.  Google Scholar
  63. Boyd, D. (2016): Undoing the neutrality of big data, Florida Law Review Forum, 67(1), pp. 226–232.  Google Scholar
  64. Braun, M./Hummel, P./Beck, S./Dabrock, P. (2020): Primer on an ethics of AI-based decision support systems in the clinic, Journal of medical ethics, https://doi.org/10.1136/medethics-2019-105860.  Google Scholar
  65. Cairns, I./Jonas, M./Wallis, K. (2018): The ethics of sharing: How do social workers decide what to record in shared health records? Ethics and Social Welfare, 12(4), p. 348–369, https://doi.org/10.1080/17496535.2017.1384849.  Google Scholar
  66. Chiusi, F./Fischer, S./Kayser-Bril, N./Spielkamp, M. (eds.) (2020): Automating society report 2020, AlgorithmWatch, Berlin/Gütersloh, https://automatingsociety.algorithmwatch.org/ [15.12.2020].  Google Scholar
  67. Coeckelbergh, M. (2019): Artificial Intelligence, Responsibility Attribution, and a Relational Justification of Explainability. Science and Engineering Ethics, pp. 2051–2068, https://doi.org/10.1007/s11948-019-00146-8.  Google Scholar
  68. Collingridge, D. (1980): The social control of technology, The Open University Press, Milton Keynes.  Google Scholar
  69. Crawford, K. (2013): The hidden biases in big data, https://hbr.org/2013/04/the-hidden-biases-in-big-data [29.03.2019].  Google Scholar
  70. Evans, M./Hilbert, J. (2020): Zur Zukunft der Arbeit in der Sozial- und Gesundheitswirtschaft in der Digitalisierungsära, in: Nadia Kutscher/Thomas Ley/Udo Seelmeyer et al. (eds.), Handbuch Soziale Arbeit und Digitalisierung, Weinheim, pp. 76–88.  Google Scholar
  71. Foster, K. A./Stiffman, A. R. (2009): Child welfare workers’ adoption of decision support technology. Journal of Technology in Human Services, 27(2), pp. 106–126, https://doi.org/10.1080/15228830902749039.  Google Scholar
  72. Geis, J. R./Brady, A./Wu, C. C./Ranschaert, E. et al. (2019): Ethics of artificial intelligence in radiology. Summary of the joint European and North American multisociety statement, Insights into Imaging 10: 101.  Google Scholar
  73. German Council for Scientific Information Infrastructures (RfII) (2020): The data quality challenge. Recommendations for sustainable research in the digital turn, Göttingen, https://rfii.de/?p=4203 [3.8.2021].  Google Scholar
  74. Gillingham, P. (2019a): Decision support systems, social justice and algorithmic accountability in social work. A new challenge, Practice, 31(4), pp. 277–290.  Google Scholar
  75. Gillingham, P. (2019b): The development of algorithmically based decision-making systems in children’s protective services. Is administrative data good enough? British Journal of Social Work, 50(2), pp. 565–580.  Google Scholar
  76. Gillingham, P. (2019c): Can predictive algorithms assist decision‐making in social work with children and families? Child Abuse Review, 28(2), pp. 114–126, https://doi.org/10.1002/car.2547.  Google Scholar
  77. Gillingham, P. (2016): Big data in social welfare. The development of a critical perspective on social work’s latest “electronic turn”, Australian Social Work, 70(2), pp. 135–147.  Google Scholar
  78. Greilich, T. (2019): Predictive Policing. Wenn die Polizei in die Zukunft schaut. Serie: Digitalisierung der Polizeiarbeit. Wegweiser Verwaltung Der Zukunft, Wegweiser Media & Conferences GmbH Berlin, https://www.vdz.org/oeffentliche-sicherheit/predictive-policing.  Google Scholar
  79. Grunwald, A. (2019): Technology assessment in practice and theory, London/New York.  Google Scholar
  80. Hoose, F./Schneiders, K./Schönauer, A.-L. (2021): Von Robotern und Smartphones. Stand und Akzeptanz der Digitalisierung im Sozialsektor, in: Wunder, M. (ed.), Digitalisierung und Soziale Arbeit: Transformationen, Beharrungen, Herausforderungen, Bad Heilbrunn, pp. 97–109, https://doi.org/10.35468/5909-07.  Google Scholar
  81. Institute of Electrical and Electronics Engineers (Ed.) (1990): IEEE standard glossary of software engineering terminology, www.informatik.htw-dresden.de/~hauptman/SEI/IEEE_Standard_Glossary_of_Software_Engineering_Terminology%20.pdf [9.8.2021].  Google Scholar
  82. Kolleck, A./Orwat, C. (2020): Mögliche Diskriminierung durch algorithmische Entscheidungssysteme und maschinelles Lernen – ein Überblick: TAB-Hintergrundpapier Nr. 24.  Google Scholar
  83. Kreidenweis, H. (2018): Digitalisierung ändert nichts – außer alles: Chancen und Risiken für Einrichtungen der Behindertenhilfe. Praxis Und Management, 57(3), pp. 122–125.  Google Scholar
  84. Larson, J./Mattu, S./Kirchner, L./Angwin, J. (2016): How we analyzed the COMPAS recidivism algorithm, https://www.propublica.org/article/how-we-analyzed-the-compas-recidivism-algorithm.  Google Scholar
  85. Latour, B. (1996): On actor-network theory. A few clarifications, Soziale Welt, 47(4), pp. 369–381.  Google Scholar
  86. Liedgren, P./Elvhage, G./Ehrenberg, A./Kullberg, C. (2016): The use of decision support systems in social work: A scoping study literature review. Journal of Evidence-Informed Social Work, 13(1), pp. 1–20, https://doi.org/10.1080/15433714.2014.914992.  Google Scholar
  87. Maier, A./Cimiano, P. (2020): Predicting independent living outcomes from written reports of social workers, Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science, Association of Computational Linguistics, pp. 139–148.  Google Scholar
  88. Merchel, J./Tenhaken, W. (2015): Dokumentation pädagogischer Prozesse in der Sozialen Arbeit. Nutzen durch digitalisierte Verfahren, in: Kutscher, N./Ley, T./Seelmeyer, U. (eds.), Mediatisierung (in) der sozialen Arbeit, Baltmannsweiler, pp. 171–191.  Google Scholar
  89. Monnickendam, M./Savaya, R./Waysman, M. (2005): Thinking processes in social workers’ use of a clinical decision support system: A qualitative study. Social Work Research, 29(1), pp. 21–30, https://doi.org/10.1093/swr/29.1.21.  Google Scholar
  90. Nami, M. R./Bertels, K. (2007): A survey of autonomic computing systems, Third international conference on autonomic and autonomous systems (ICAS’07), IEEE, pp. 26–31.  Google Scholar
  91. Neri, E./Coppola, F./Miele, V./Bibbolino, C./Grassi, R. (2020): Artificial intelligence. Who is responsible for the decision? Radiol med, 125, pp. 517–521.  Google Scholar
  92. Raji, D. (2020): How our data encodes systematic racism. Technologists must take responsibility for the toxic ideologies that our data sets and algorithms reflect, MIT Technology Review, https://www.technologyreview.com/2020/12/10/1013617/racism-data-science-artificial-intelligence-ai-opinion/ [21.01.2021].  Google Scholar
  93. Schneider, D. (2020): Decision Support Systeme in der Sozialen Arbeit. Herausforderungen an die Rolle der TA in Innovationsprozessen, in: Nierling, L./Torgersen, H. (eds.), Die neutrale Normativität der Technikfolgenabschätzung. Konzeptionelle Auseinandersetzung und praktischer Umgang, Baden-Baden, pp. 117–138.  Google Scholar
  94. Schneider, D. (2022): „das braucht die Technik nicht alles zu wissen“. Digitale Datenerfassung im Spannungsfeld zwischen Privatheit, Datenschutz und gesellschaftlichem Auftrag, in: Friedewald, M./Kreutzer, M./Hansen, M. (eds.), Selbstbestimmung, Privatheit und Datenschutz. Gestaltungsoptionen für einen europäischen Weg. DUD Fachbeiträge, Wiesbaden, pp. 241–260.  Google Scholar
  95. Schneider, D. (in press): Ethische und professionsspezifische Herausforderungen im Diskurs um algorithmische Systeme der Entscheidungsunterstützung im Kontext der Teilhabeplanung für Menschen mit Behinderung, in: Sonar, A./Weber, K. (eds.), Künstliche Intelligenz und Gesundheit, Stuttgart, pp. 87–132.  Google Scholar
  96. Schneider, D./Seelmeyer, U. (2019): Challenges in using big data to develop decision support systems for social work in Germany, Journal of Technology in Human Services, 37(2–3), pp. 113–128.  Google Scholar
  97. Schneider, D./Sonar, A./Weber, K. (2022): Zwischen Automatisierung und ethischem Anspruch. Disruptive Effekte des KI-Einsatzes in und auf Professionen der Gesundheitsversorgung, in: Pfannstiel, M. (ed.), Künstliche Intelligenz im Gesundheitswesen, Wiesbaden, pp. 325–348.  Google Scholar
  98. Schomberg, R. von (2015): Responsible Innovation: The new paradigm for science, technology and innovation policy, in: Bogner, A./Decker, M./Sotoudeh, M. (eds.), Gesellschaft – Technik – Umwelt. Neue Folge: Vol. 18. Responsible Innovation: Neue Impulse für die Technikfolgenabschätzung? Baden-Baden, pp. 47–70.  Google Scholar
  99. Schrödter, M./Bastian, P./Taylor, B. (2018): Risikodiagnostik in der Sozialen Arbeit an der Schwelle zum „digitalen Zeitalter“ von Big Data Analytics, Preprint, Advance online publication, https://doi.org/10.13140/RG.2.2.22119.14240.  Google Scholar
  100. Shiller, U./Strydom, M. (2018): Evidence-based practice in child protection services: Do we have time for this? Social Work, 54(4), https://doi.org/10.15270/54-4-669.  Google Scholar
  101. Spielkamp, M. (Ed.) (2019): Automating society. Taking stock of automated decision making in the EU, A report by AlgorithmWatch in cooperation with Bertelsmann Stiftung, supported by the Open Society Foundations, AW AlgorithmWatch gGmbH, www.algorithmwatch.org/automating-society [18.03.2019].  Google Scholar
  102. Ückert, S./Sürgit, H./Diesel, G. (Eds.) (2020): Digitalisierung als Erfolgsfaktor für das Sozial- und Wohlfahrtswesen, Baden-Baden.  Google Scholar
  103. van der Put, C. E./Hermanns, J./van Rijn-van Gelderen, L./Sondeijker, F. (2016): Detection of unsafety in families with parental and/or child developmental problems at the start of family support, BMC Psychiatry, 16, 15, https://doi.org/10.1186/s12888-016-0715-y.  Google Scholar
  104. Van Someren, M. W./Barnard, Y. F./Sandberg, J. A. C. (1994): The think aloud method. A practical approach to modelling cognitive processes, London.  Google Scholar
  105. Wachter, S./Mittelstadt, B./Floridi, L. (2017): Transparent, explainable, and accountable AI for robotics: To create fair and accountable AI and robotics, we need precise regulation and better methods to certify, explain, and audit inscrutable systems, Science Robotics, 2(6), https://doi.org/10.1126/scirobotics.aan6080.  Google Scholar
  106. Weber, K. (2013): What is it like to encounter an autonomous artificial agent? AI & Soc, 28(4), pp. 483–489.  Google Scholar
  107. Weizenbaum, J. (1976): Computer power and human reason. From judgement to calculation, W. H. Freeman & Co Ltd.  Google Scholar
  108. Zweig, K. A./Fischer, S./Lischka, K. (2018): Wo Maschinen irren können. Fehlerquellen und Verantwortlichkeiten in Prozessen algorithmischer Entscheidungsfindung, Arbeitspapier, Bertelsmann Stiftung.  Google Scholar

Abstract

This paper examines how social care provider professionals could be supported by decision support systems (DSSs) in social care service planning (SSP). Since DSSs are not yet used in Germany, we rely on interviews with social work professionals to explore expectations and fears about DSSs, and how they could be integrated into professional practice. Our findings support three conclusions. First, DSSs providing visualisations of clients’ development are perceived to support decision-making. Second, there is a need for DSSs to support shared decision-making. Finally, it is crucial not to confound technical reliability with professional reliability.

Table of Contents

Section Title Page Action Price
Diana Schneider et al.: Exploring Opportunities and Risks in Decision Support Technologies for Social Workers: An Empirical Study in the Field of Disabled People’s Services 1
Abstract 1
Zusammenfassung: Chancen und Risiken von Entscheidungsunterstützungssystemen für Fachkräfte der Sozialen Arbeit: Ergebnisse einer empirischen Studie für das Feld der Teilhabeplanung für Menschen mit Behinderung 1
1. Introduction 2
2. Approach to Considering Unintended Implications in DSS Development 4
3. Data and Methods 7
4. Results 8
4.1 General Expectations of Social Workers Regarding DSSs in Integration Assistance 8
4.2 Interaction with a Prototype DSS 1
5. Discussion 1
5.1 Clarify Different Perspectives Using Data-Driven DSSs 1
5.2 Promoting the Development of DSSs in (Shared) Decision-Making Processes 1
5.3 De-Mystifying Technical Reliability 1
6. Conclusion 1
Acknowledgements 1
References 1

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