Skip to main content
SpringerLink
Log in

A taxonomy of human–machine collaboration: capturing automation and technical autonomy

  • Open Forum
  • Published:
AI & SOCIETY Aims and scope Submit manuscript

Abstract

Due to the ongoing advancements in technology, socio-technical collaboration has become increasingly prevalent. This poses challenges in terms of governance and accountability, as well as issues in various other fields. Therefore, it is crucial to familiarize decision-makers and researchers with the core of human–machine collaboration. This study introduces a taxonomy that enables identification of the very nature of human–machine interaction. A literature review has revealed that automation and technical autonomy are main parameters for describing and understanding such interaction. Both aspects must be carefully evaluated, as their increase has potentially far-reaching consequences. Hence, these two concepts comprise the taxonomy’s axes. Five levels of automation and five levels of technical autonomy are introduced below, based on the assumption that both automation and autonomy are gradual. The levels of automation were developed from existing approaches; those of autonomy were carefully derived from a review of the literature. The taxonomy’s use is also explained, as are its limitations and avenues for further research.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1

Similar content being viewed by others

References

  • Alonso E, Mondragón E (2004) Agency, learning and animal-based reinforcement learning. In: Nickles M, Rovatsos M, Weiss G (eds) Agents and computational autonomy – potential risks and solutions. Springer, Berlin, pp 1–6

    Google Scholar 

  • Balkin JM (2015) The path of robotics law. 6 California Law Review, Circuit 45.

  • Beck S (2015) Technisierung des Mensche: Vermenschlichung der Technik. Neue Herausforderungen für das rechtliche Konzept “Verantwortung”. In: Gruber MC, Bung J, Ziemann S (eds) Autonome Automaten: Künstliche Körper und artifizielle Agenten in der technisierten Gesellschaft. BWV Verlag, Berlin, pp 173–187

    Google Scholar 

  • Beer JM, Fisk AD, Rogers WA (2014) Toward a framework for levels of robot autonomy in human-robot interaction. J Hum Robot Interact 3:74–99

    Article  Google Scholar 

  • Bradshaw JM, Feltovich PJ, Jung H, Kulkarni S, Taysom W, Uszok A (2004) Dimensions of adjustable autonomy and mixed-initiative interaction. In: Nickles M, Rovatos M, Weiss G (eds) Agents and computational autonomy: potential, risks, and solutions. Springer, Berlin, pp 17–39

    Chapter  Google Scholar 

  • Castelfranchi C, Falcone R (2004) Founding autonomy: The dialectics between (social) environment and agent’s architecture and powers. In: Nickles M, Rovatos M, Weiss G (eds) Agents and computational autonomy: potential, risks, and solutions. Springer, Berlin, pp 40–54

    Chapter  Google Scholar 

  • Chinen MA (2016) The co-evolution of autonomous machines and legal responsibility. Va J Law Technol 20:338

    Google Scholar 

  • Danaher J, Hogan MJ, Noone C, Kennedy R, Behan A, De Paor A et al (2017) Algorithmic governance: developing a research agenda through the power of collective intelligence. Big Data Soc 4:1–21. https://doi.org/10.1177/2053951717726554

    Article  Google Scholar 

  • Endsley MR (1987) The application of human factors to the development of expert systems for advanced cockpits. Proc Hum Factors Soc Annu Meet 31(12):1388–1392. https://doi.org/10.1177/154193128703101219

    Article  Google Scholar 

  • Flemisch F, Heesen M, Hesse T, Kelsch J, Schieben A, Beller J (2012) Towards a dynamic balance between humans and automation: authority, ability, responsibility and control in shared and cooperative control situations. Cogn Technol Work 14:3–18. https://doi.org/10.1007/s10111-011-0191-6

    Article  Google Scholar 

  • Floridi L, Sanders JW (2004) On the morality of artificial agents. Mind Mach 14:349–379. https://doi.org/10.1023/b:mind.0000035461.63578.9d

    Article  Google Scholar 

  • Franklin S, Graesser A (1997) Is It an agent, or just a program?: a taxonomy for autonomous agents. In: Müller JP, Wooldridge MJ, Jennings NR (eds) Intelligent agents III agent theories, architectures, and languages. ATAL 1996. Lecture notes in computer science (lecture notes in artificial intelligence). Springer, Berlin, pp 21–35

  • Gransche B, Shala E, Hubig C, Alpsancar S, Harrach S (2014) Wandel von Autonomie und Kontrolle durch neue Mensch-Technik-Interaktionen. Grundsatzfragen autonomieorientierter Mensch-Technik-Verhältnisse. Fraunhofer Verlag, Stuttgart

    Google Scholar 

  • Hertzberg J (2015) Technische Gestaltungsoptionen für autonom agierende Komponenten und Systeme. In: Hilgendorf E, Hötitzsch S (eds) Das Recht vor den Herausforderungen der modernen Technik. Nomos, Baden-Baden, pp 63–74

    Google Scholar 

  • Hilgendorf E (2017) Automated driving and the law. In: Hilgendorf E, Seidel U (eds) Robotics, autonomics, and the law. Nomos, Baden-Baden, pp 171–194

    Chapter  Google Scholar 

  • Janssen M, Kuk G (2016) The challenges and limits of big data algorithms in technocratic governance. Gov Inf Q 33:371–377. https://doi.org/10.1016/j.giq.2016.08.011

    Article  Google Scholar 

  • Jordan MI, Mitchell TM (2015) Machine learning: trends, perspectives, and prospects. Science 349:255–260. https://doi.org/10.1126/science.aaa8415

    Article  MathSciNet  MATH  Google Scholar 

  • Kaber DB (2018) Issues in human–automation interaction modeling: presumptive aspects of frameworks of types and levels of automation. J Cogn Eng Decis Mak 12:7–24. https://doi.org/10.1177/1555343417737203

    Article  Google Scholar 

  • Kirchkamp O, Strobel C (2019) Sharing responsibility with a machine. J Behav Exp Econ 80:25–33. https://doi.org/10.1016/j.socec.2019.02.010

    Article  Google Scholar 

  • Korsgaard CM (2014) The normative constitution of agency. In: Vargas M, Yaffe G (eds) Rational and social agency: the philosophy of Michael Bratman. Oxford University Press, New York, pp 190–215

    Chapter  Google Scholar 

  • Lambe P (2007) Organising knowledge: taxonomies. Knowledge and organisational effectiveness. Chandos, Oxford

    Book  Google Scholar 

  • Loh W, Loh J (2017) Autonomy and responsibility in hybrid systems. In: Lin P, Jenkins R, Abney K (eds) Robot ethics 2.0: from autonomous cars to artificial intelligence. Oxford University Press, Oxford. https://doi.org/10.1093/oso/9780190652951.003.0003

    Chapter  Google Scholar 

  • Martin K (2018) Ethical implications and accountability of algorithms. J Bus Ethics 160:835–850. https://doi.org/10.1007/s10551-018-3921-3

    Article  Google Scholar 

  • Matthias A (2004) The responsibility gap: ascribing responsibility for the actions of learning automata. Ethics Inf Technol 6:175–183. https://doi.org/10.1007/s10676-004-3422-1

    Article  Google Scholar 

  • Misselhorn C (2015) Collective agency and cooperation in natural and artificial systems. Springer International Publishing, Cham. https://doi.org/10.1007/978-3-319-15515-9_1

    Book  Google Scholar 

  • Mittelstadt BD, Allo P, Taddeo M, Wachter S, Floridi L (2016) The ethics of algorithms: mapping the debate. Big Data Soc 3:1–21. https://doi.org/10.1177/2053951716679679

    Article  Google Scholar 

  • Müller-Hengstenberg CD, Kirn S (2016) Rechtliche Risiken autonomer und vernetzter Systeme: eine Herausforderung. Walter de Gruyter GmbH, Berlin

    Book  Google Scholar 

  • NHTSA (2013) Preliminary statement of policy concerning automated vehicles. US National Highway Traffic Safety Administration, 30 May 2013

  • Nickerson R, Muntermann J, Varshney U, Isaac H (2009) Taxonomy development in information systems: developing a taxonomy of mobile applications. https://halshs.archives-ouvertes.fr/halshs-00375103/document. Accessed 3 Aug 2019

  • Nof SY (2009) Automation: what it means to us around the world. In: Nof S (ed) Springer handbook of automation. Springer, Berlin, pp 13–52

    Chapter  Google Scholar 

  • Nunes I, Jannach D (2017) A systematic review and taxonomy of explanations in decision support and recommender systems. User Model User Adapt Interact 27:393–444. https://doi.org/10.1007/s11257-017-9195-0

    Article  Google Scholar 

  • Onnasch L, Maier X, Jürgensohn T (2016) Mensch-Roboter-Interaktion-Eine Taxonomie für alle Anwendungsfälle. Bundesanstalt für Arbeitsschutz und Arbeitsmedizin (BAuA), Dortmund

    Google Scholar 

  • Pagallo U (2017) From automation to autonomous systems: a legal phenomenology with problems of accountability. In: Proceedings of the twenty-sixth international joint conference on artificial intelligence (IJCAI-17), pp 17–23. https://doi.org/10.24963/ijcai.2017/3

  • Parasuraman R, Sheridan TB, Wickens CD (2000) A model for types and levels of human interaction with automation. IEEE Trans Syst Man Cybern Part A Syst Hum 30:286–297. https://doi.org/10.1109/3468.844354

    Article  Google Scholar 

  • Proud RW, Hart JJ, Mrozinski RB (2003). Methods for determining the level of autonomy to design into a human spaceflight vehicle: a function specific approach. NASA Johnson Space Center Report, NASA Road, Houston, TX, 2003

  • Rammert W (2009) Hybride Handlungsträgerschaft: Ein soziotechnisches Modell verteilten Handelns. In: Herzog O, Schildhauer T (eds) Intelligente Objekte. Springer, Berlin, pp 23–33

    Chapter  Google Scholar 

  • Rammert W, Schulz-Schaeffer I (2002) Technik und Handeln: wenn soziales Handeln sich auf menschliches Verhalten und technische Artefakte verteilt. In: Rammert W, Schulz-Schaeffer I (eds) Können Maschinen handeln?: soziologische Beiträge zum Verhältnis von Mensch und Technik. Campus Verlag, Frankfurt, pp 11–64

    Google Scholar 

  • Rijsdijk SA, Hultink EJ, Diamantopoulos A (2007) Product intelligence: its conceptualization, measurement and impact on consumer satisfaction. J Acad Mark Sci 35:340–356. https://doi.org/10.1007/s11747-007-0040-6

    Article  Google Scholar 

  • Riley V (1989) A general model of mixed-initiative human-machine systems. Proc Hum Factors Soc Ann Meet 33:124–128. https://doi.org/10.1177/154193128903300227

    Article  Google Scholar 

  • Russell SJ, Norvig P (2014) Artificial intelligence: a modern approach. Pearson education limited, Malaysia

    MATH  Google Scholar 

  • Santosuosso A, Bottalico B (2017) Autonomous systems and the law: why intelligence matters. In: Hilgendorf E, Seidel U (eds) Robotics, autonomics, and the law. Nomos, Baden-Baden, pp 27–58

    Google Scholar 

  • Sartor G, Omicini A (2016) The autonomy of technological systems and responsibilities for their use. In: Bhuta N, Beck S, Geiss R, Lui HY, Kress C (eds) Autonomous weapon systems: law, ethics, policy. Cambridge University Press, Cambridge, pp 39–74

    Google Scholar 

  • Sheridan TB, Verplank WL (1978). Human and computer control of undersea teleoperators. Institute of Technology Cambridge, Cambridge. https://www.dtic.mil/dtic/tr/fulltext/u2/a057655.pdf. Accessed 23 May 2019

  • Shin D, Park YJ (2019) Role of fairness, accountability, and transparency in algorithmic affordance. Comput Hum Behav 98:277–284. https://doi.org/10.1016/j.chb.2019.04.019

    Article  Google Scholar 

  • Shneiderman B (2016) The dangers of faulty, biased, or malicious algorithms requires independent oversight. Proc Natl Acad Sci USA 113:13538–13540. https://doi.org/10.1073/pnas.1618211113

    Article  Google Scholar 

  • Simmler M (2019) Maschinenethik und strafrechtliche Verantwortlichkeit. In: Bendel O (ed) Handbuch Maschinenethik. Springer, Wiesbaden, pp 1–18

    Google Scholar 

  • Sommerville I (2007) Software engineering. Pearson Education Limited, Essex

    MATH  Google Scholar 

  • Thürmel S (2015) The participatory turn: a multidimensional gradual agency concept for human and non-human actors. In: Misselhorn C (ed) Collective agency and cooperation in natural and artificial systems. Springer, Cham, pp 45–60

    Chapter  Google Scholar 

  • Vagia M, Transeth AA, Fjerdingen SA (2016) A literature review on the levels of automation during the years. What are the different taxonomies that have been proposed? Appl Ergon 53:190–202. https://doi.org/10.1016/j.apergo.2015.09.013

    Article  Google Scholar 

  • Verhagen H (2004) Autonomy and reasoning for natural and artificial agents. In: Nickles M, Rovatsos M, Weiss G (eds) Agents and computational autonomy. Lecture notes in computer science, vol 2969. Springer, Berlin, pp 83–94

  • Wein LE (1992) Responsibility of intelligent artifacts: toward an automation jurisprudence. Harvard J Law Technol 6:103–154. https://heinonline.org/HOL/P?h=hein.journals/hjlt6&i=109. Accessed 8 Aug 2019

  • Weyer J (2006) Die Kooperation menschlicher Akteure und nicht-menschlicher Agenten: Ansatzpunkte einer Soziologie hybrider Systeme. Working Paper, 16-2006. Wirtschafts- und Sozialwissenschaftliche Fakultät Universität Dortmund, Dortmund, pp 1–36. https://nbn-resolving.de/urn:nbn:de:0168-ssoar-120992. Accessed 10 June 2019

  • Weyer J, Reineke S (2005) Creating order in hybrid systems: reflections on the interaction of man and smart machines. Working Paper, 7-2005. Technische Universität Dortmund, Dortmund, pp 1–48. https://nbn-resolving.de/urn:nbn:de:0168-ssoar-109749. Accessed 10 June 2019

  • Zarsky T (2016) The trouble with algorithmic decisions: an analytic road map to examine efficiency and fairness in automated and opaque decision making. Sci Technol Hum Values 41:118–132. https://doi.org/10.1177/0162243915605575

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Law School, University of St. Gallen, Bodanstrasse 3, 9000, St. Gallen, Switzerland

    Monika Simmler

  2. Institute for Systemic Management and Public Governance, University of St. Gallen, Dufourstrasse 40a, 9000, St. Gallen, Switzerland

    Ruth Frischknecht

Authors
  1. Monika Simmler
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ruth Frischknecht
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ruth Frischknecht.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Simmler, M., Frischknecht, R. A taxonomy of human–machine collaboration: capturing automation and technical autonomy. AI & Soc 36, 239–250 (2021). https://doi.org/10.1007/s00146-020-01004-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00146-020-01004-z

Keywords

Search

Navigation