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Human-Machine Collaboration 
in Networked Information Systems

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Over the past decade, computers and networks have become increasingly ubiquitous. They have a central role in how we work, communicate, and learn. In this talk, I introduce the concept of human-machine collaboration and show how networked information systems enable coupled relationships between humans and machines. I will take four perspectives on this coupled relationships: how we can design, analyze, develop and also extend them. The presented approaches contribute to the research field of networked socio-technical information systems. By unfolding design parameters of these systems, we can develop adaptive networked information systems that, in the future, can reduce the friction between humans and machines to a point where they become a natural extension of our human experience.

Publicado en: Software
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Human-Machine Collaboration 
in Networked Information Systems

  1. 1. Claudia Müller-Birn Institute of Computer Science, Freie Universität Berlin March 2, 2017 Human-Machine Collaboration 
 in Networked Information Systems
  2. 2. Prof. Dr. Claudia Müller-Birn | Human-Machine Collaboration | March 2017 2 Social Web that connects people and knowledge Semantic Web that connects machines and knowledge … … Human-Machine Collaboration
  3. 3. Prof. Dr. Claudia Müller-Birn | Human-Machine Collaboration | March 2017 3 Research Context Networked information systems are socio-technical systems that integrate distributed information resources based on usable user and programming interfaces to enable human-machine collaborations.
  4. 4. Prof. Dr. Claudia Müller-Birn | Human-Machine Collaboration | March 2017 Research Areas We focus on designing networked information systems for both, human and algorithmic agents in the following three areas: (1) Designing efficient, effective and easy-to-use interfaces. (2) Enabling a seamless coordination between knowledge processes. (3) Expanding collaboration into the physical world. 4
  5. 5. Prof. Dr. Claudia Müller-Birn | Human-Machine Collaboration | March 2017 Work Location 1 5 The "almost" complete HCC:Team (Summer 2016)
  6. 6. Prof. Dr. Claudia Müller-Birn | Human-Machine Collaboration | March 2017 Work Location 2 6
  7. 7. Prof. Dr. Claudia Müller-Birn | Human-Machine Collaboration | March 2017 7 Working Process Review and Analysis Ideas and Concept User Scenario DesignPrototyping Testing Implementing
  8. 8. Prof. Dr. Claudia Müller-Birn | Human-Machine Collaboration | March 2017 Designingnetworked information systems Analysingrelationships between humans and machines in networked information systems Developingcoupled relationships between humans and machines in networked information systems Extending networked information systems 8
  9. 9. Prof. Dr. Claudia Müller-Birn | Human-Machine Collaboration | March 2017 Designingnetworked information systems Analysingrelationships between humans and machines in networked information systems Developingcoupled relationships between humans and machines in networked information systems Extending networked information systems 9
  10. 10. Prof. Dr. Claudia Müller-Birn | Human-Machine Collaboration | March 2017 10 Challenges in online communities Everyone is better off 
 if everyone contributes 
 than if no one does Each individual is even better off if she does not contribute 
 while the others do Critical mass Social loafing
  11. 11. Prof. Dr. Claudia Müller-Birn | Human-Machine Collaboration | March 2017 Feedback: Goal setting Context: MovieLens is a movie recommendation site 
 (http://www.movielens.org) Hypothesis: In an online community, specific, numeric 
 goals will motivate greater contributions than 
 non-specific goals Approach: Members were recruited by sending them an 
 email message which contained an invitation to participate 
 in a movie rating campaign Result: Providing community members with specific goals increases their contributions. 11 Ling, Kimberly, et al. "Using social psychology to motivate contributions to online communities." Journal of Computer-Mediated Communication 10.4 (2005).
  12. 12. Prof. Dr. Claudia Müller-Birn | Human-Machine Collaboration | March 2017 Sociality: Social contact Context: Wikipedia Hypothesis: Social contact improves the retention rate of new editors Approach: Create a online teahouse for meeting new editors Result: Social contact with other similar contributors causes members to stay and to contribute more. 12 Wikimedia, 2012. http://en.wikipedia.org/wiki/Wikipedia:Teahouse
  13. 13. Prof. Dr. Claudia Müller-Birn | Human-Machine Collaboration | March 2017 Feedback: Make readers’ interests visible Constant interest Regularly accessed articles, sometimes only for fact finding, e.g. Albert Einstein, Facebook, IMDB 13 Peak interest Death of people, game and movie releases, e.g. Whitney Houston, The Hunger Games, 2012 Phenomeno Increasing/decreasing interest Items that became popular/loose popularity during our observation period, e.g. One direction, Instagram Lehmann, J.; Müller-Birn, C.; Laniado, D.; Lalmas, M.; Kaltenbrunner, A. (2014): Readers Preference and Behavior on Wikipedia. In Proceedings of the 25th ACM Conference on Hypertext and Hypermedia, Santiago (Chile).
  14. 14. Prof. Dr. Claudia Müller-Birn | Human-Machine Collaboration | March 2017 Designingnetworked information systems Analysingrelationships between humans and machines in networked information systems Developingcoupled relationships between humans and machines in networked information systems Extending networked information systems 14
  15. 15. Prof. Dr. Claudia Müller-Birn | Human-Machine Collaboration | March 2017 Research Goal Funding
 Data Collection Analysis of Contribution Patterns » Identifying participation patterns for collaborative ontology development » DFG-funded research project in collaboration with GESIS - Leibniz-Institute for Social Science » JSON and XML-based data dump (10/2012 to 10/2014) 15 Wikidatameter by Lukas Benedix (2013)
  16. 16. Prof. Dr. Claudia Müller-Birn | Human-Machine Collaboration | March 2017 16 “What is the population of Berlin?”
  17. 17. Prof. Dr. Claudia Müller-Birn | Human-Machine Collaboration | March 2017 17 project launch in 2012 | over 460 million edits | 25 million items
  18. 18. Prof. Dr. Claudia Müller-Birn | Human-Machine Collaboration | March 2017 18 Human Contribution Patterns Created items Item terms Item statements Set sitelinks Created properties Property terms Discussions Protected pages Reverts Müller-Birn, C., Karran, B., Lehmann, J. and Luczak-Rösch, M., 2015. Peer-production system or collaborative ontology engineering effort: What is Wikidata?. In Proceedings of the 11th International Symposium on Open Collaboration (p. 20). ACM.
  19. 19. Prof. Dr. Claudia Müller-Birn | Human-Machine Collaboration | March 2017 19 Contributions on German Wikipedia Müller-Birn, C., Dobusch, L. and Herbsleb, J.D., 2013, June. Work-to-rule: the emergence of algorithmic governance in Wikipedia. In Proceedings of the 6th International Conference on Communities and Technologies (pp. 80-89). ACM.
  20. 20. Prof. Dr. Claudia Müller-Birn | Human-Machine Collaboration | March 2017 20 Human and Algorithmic Contribution Patterns Created items Item terms Item statements Set sitelinks Created properties Property terms Discussions Protected pages Reverts ? Müller-Birn, C., Karran, B., Lehmann, J. and Luczak-Rösch, M., 2015. Peer-production system or collaborative ontology engineering effort: What is Wikidata?. In Proceedings of the 11th International Symposium on Open Collaboration. ACM.
  21. 21. Prof. Dr. Claudia Müller-Birn | Human-Machine Collaboration | March 2017 21 Müller-Birn, C.; Dobusch, L.; Herbsleb, J.D. (2013): Work-to-rule: the emergence of algorithmic governance in Wikipedia. In Proceedings of the 6th International Conference on Communities and Technologies (C&T '13). ACM, New York, NY, USA, 80-89. 0 1,000,000 2,000,000 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 boteditcountperyear Category Main Other Portal Talk User User talk Wikipedia editing activities includes additional namespaces editing activities extends to community space focus expands further to project spaces about 45 % of all edits are outside of article namespace
  22. 22. Prof. Dr. Claudia Müller-Birn | Human-Machine Collaboration | March 2017 22 Algorithmically translated rules are more scalable, but they are less likely to handle exceptions. Müller-Birn, C.; Dobusch, L.; Herbsleb, J.D. (2013): Work-to-rule: the emergence of algorithmic governance in Wikipedia. In Proceedings of the 6th International Conference on Communities and Technologies (C&T '13). ACM, New York, NY, USA, 80-89. Changing task focus of a bot over time
  23. 23. Prof. Dr. Claudia Müller-Birn | Human-Machine Collaboration | March 2017 23 Algorithmic Governance - how bots influence user retention Halfaker, A., Geiger, R.S., Morgan, J.T. and Riedl, J., 2012. The rise and decline of an open collaboration system: How Wikipedia’s reaction to popularity is causing its decline. American Behavioral Scientist, p.0002764212469365.
  24. 24. Prof. Dr. Claudia Müller-Birn | Human-Machine Collaboration | March 2017 Designingnetworked information systems Analysingrelationships between humans and machines in networked information systems Developingcoupled relationships between humans and machines in networked information systems Extending networked information systems 24
  25. 25. Prof. Dr. Claudia Müller-Birn | Human-Machine Collaboration | March 2017 Research Context Research Goal
 Context
 Integrating Interdisciplinary Research Data » Research Data in the area of biological research » Allow experts to discover research data beyond disciplinary terminologies » Research project with 20 partners; in close collaboration with Botanical Garden and Botanical Museum (BGBM) 25 Karam,N.;Müller-Birn,C.,Gleisberg,M,;Fichtmüller,D.;Tolksdorf,R.Güntsch, A. (2016): A Terminology Service supporting semantic annotation, integration, discovery and analysis of interdisciplinary research data. Datenbank-Spektrum. Zeitschrift für Datenbanktechnologien und Information Retrieval, Vol. 16, No.3, 195-205.
  26. 26. Prof. Dr. Claudia Müller-Birn | Human-Machine Collaboration | March 2017 26 German Federation for Biological Data (GFBio) Karam,N.;Müller-Birn,C.,Gleisberg,M,;Fichtmüller,D.;Tolksdorf,R.Güntsch, A. (2016): A Terminology Service supporting semantic annotation, integration, discovery and analysis of interdisciplinary research data. Datenbank-Spektrum. Zeitschrift für Datenbanktechnologien und Information Retrieval, Vol. 16, No.3, 195-205.
  27. 27. Prof. Dr. Claudia Müller-Birn | Human-Machine Collaboration | March 2017 27 Authmann, C., Beilschmidt, C., Drönner, J., Mattig, M. and Seeger, B., 2015. VAT: a system for visualizing, analyzing and transforming spatial data in science. 
 Datenbank-Spektrum, 15(3), pp.175-184.
  28. 28. Prof. Dr. Claudia Müller-Birn | Human-Machine Collaboration | March 2017 28 The Terminology Service CHEBIBCO PATO RECORDBASIS ENVO OBOE KINGDOMQUDT SWEET ISOCOUNTRIES LIT_I NCBITAXON PESI COL ITIS … External Terminologies Karam,N.;Müller-Birn,C.,Gleisberg,M,;Fichtmüller,D.;Tolksdorf,R.Güntsch, A. (2016): A Terminology Service supporting semantic annotation, integration, discovery and analysis of interdisciplinary research data. Datenbank-Spektrum. Zeitschrift für Datenbanktechnologien und Information Retrieval, Vol. 16, No.3, 195-205.
  29. 29. Prof. Dr. Claudia Müller-Birn | Human-Machine Collaboration | March 2017 29 Terminology Matching CHEBIBCO PATO RECORDBASIS ENVO OBOE KINGDOMQUDT SWEET ISOCOUNTRIES LIT_I NCBITAXON Abbreviation Name # classes # same URI’s NCBITAXON National Center for Biotechnology Information (NCBI) Organismal Classification 1.507.562 CHEBI Chemical Entities of Biological Interest Ontology 103.755 ENVO Environment Ontology 6.190 BCO: 11
 PATO: 221 SWEET Semantic Web for Earth and Environment Technology Ontology 4.549 ISOCOUNTRIES ISO 3166 Countries and Subdivisions 5.307 PATO Phenotypic Quality Ontology 2.603 ENVO: 221 LIT_I The lithologs rock names ontology for igneous rocks 1.870 OBOE Extensible Observation Ontology 538 QUDT Quantity, Unit, Dimension and Type 204 BCO Biological Collections Ontology 127 ENVO: 11 RECORDBASIS GFBio Agreed Vocabulary for RecordBasis 14 KINGDOM GFBio Agreed Vocabulary for Kingdoms 9 PESI COL ITIS … External Terminologies
  30. 30. Prof. Dr. Claudia Müller-Birn | Human-Machine Collaboration | March 2017 Designingnetworked informaton systems Analysingrelationships between humans and machines in networked informaton systems Developingcoupled relationships between humans and machines in networked information systems Extending networked information systems 30
  31. 31. Prof. Dr. Claudia Müller-Birn | Human-Machine Collaboration | March 2017 Research Context
 Research Goal Context 
 Collaborative, geographically distributed research processes » Many collaborative research practices evolve around physical artefacts, e.g. in archeology, and are geographically distributed » Integrating physical and digital activities more smoothly and allow for computer-supported collaborative sensemaking by 
 networked information systems » Research project Hybrid Knowledge Interaction of the Cluster of Excellence Image, Knowledge Gestaltung. An Interdisciplinary Laboratory 31
  32. 32. Prof. Dr. Claudia Müller-Birn | Human-Machine Collaboration | March 2017 32 Computer-Supported Collaborative Sensemaking Software Agents Cognitive Process of Person 2 Representation Shift Loop Search for good representations Instantiate Representations Cognitive Process of Person 1 Representation Interface Processing Requirements of Task Generation Loop Data Coverage Loop Representation Shift Loop Search 
 for good representations Instantiate 
 Representations Generation Loop Data Coverage Loop Processing Requirements of Task Set of Annotations External Knowledge Bases Hong, M. and Müller-Birn, C., 2017, February. Conceptualization of Computer-Supported Collaborative Sensemaking. In Proceedings of the 20th ACM Conference Companion on Computer Supported Cooperative Work & Social Computing. ACM.
  33. 33. Prof. Dr. Claudia Müller-Birn | Human-Machine Collaboration | March 2017 Conclusion & Future Work » The presented qualitative knowledge-based workflows build upon Licklider's vision of a “man- machine symbiosis” and Engelbart’s “augmenting human intellect” proposal » The technical and human components of a web information system are equally important, whereby the design of the interfaces and the human ability to use them should be coordinated. » The algorithmic component should be implemented in a way that it allows users to get an understanding of their functioning 33
  34. 34. Prof. Dr. Claudia Müller-Birn | Human-Machine Collaboration | March 2017 Discussion 34

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