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Human Collaboration Reshaped: Applications and Perspectives

  • Martin Bogner
  • François Bry
  • Niels Heller
  • Stephan Leutenmayr
  • Sebastian Mader
  • Alexander Pohl
  • Clemens Schefels
  • Yingding Wang
  • Christoph Wieser
Chapter

Abstract

20th century iconic examples of human collaboration are the assembly line, centralised planning, bureaucracies, vote‐based decision making, and school education. These examples, and more generally all forms of human collaboration of the 20th century, are characterized by predefined human roles and little adaptable processes, that is, 20th century collaboration comes at the price of a restricted individual freedom. With the turn of the century, new forms of human collaboration have become widespread that exploit information and communication technologies, data generated by humans, Data Science in general and Machine Learning in particular, and let humans contribute as they like, when they like, and as much as they can, the lack of predefined roles and processes being accounted for by software. The phrase “Human Computation” coined for denoting the new forms of human collaboration stresses a core aspect of the paradigm which can be a downside: With Human Computation, humans become contributors to collaboration‐enabling algorithms that can also control and restrict how collaboration takes place. This article introduces to Human Computation and to its role in applications of Machine Learning, presents Human Computation prototype systems developed during the last decade at Ludwig‐Maximilian University of Munich, discusses ethical issues of Human Computation and Machine Learning, points to on‐going research in the field at Ludwig‐Maximilian University of Munich, and concludes with a reflection on the future of Human Computation. The original contribution of this article is a comprehensive presentation of recent research the main part of which has already been published in more detail elsewhere.

References

  1. 1.
    Luis von Ahn: Human Computation, Ph.D. Dissertation, Carnegie Mellon University, 2007Google Scholar
  2. 2.
    Alexander J. Quinn and Benjamin B. Bederson: Human Computation: A Survey and Taxonomy of a Growing Field. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI), pages 1403–1412, 2011Google Scholar
  3. 3.
    Jeff Howe: The Rise of Crowdsourcing, Wired, June 2006Google Scholar
  4. 4.
    Edith Law and Luis von Ahn: Human Computation. In R. J. Brachman, W. W Cohen, W. W. and T. Dietterich editors, Synthesis Lectures on Artificial Intelligence and Machine Learning, Morgan & Claypool Publishers, pages 1–121, 2011Google Scholar
  5. 5.
    Pietro Michelucci: Introduction, Handbook of Human Computation, Pietro Michelucci editor, Springer Verlag, 2013CrossRefGoogle Scholar
  6. 6.
    Mary Catherine Bateson: Foreword, Handbook of Human Computation, Pietro Michelucci editor, Springer Verlag, 2013Google Scholar
  7. 7.
    Alex Kohn, François Bry, and Alexander Manta: Exploiting a Company’s Knowledge: The Adaptive Search Agent YASA, Proceedings of the International Conference on Semantic Systems (I-Semantics), pages 166–169, 2007Google Scholar
  8. 8.
    Alex Kohn, François Bry, and Alexander Manta: Exploiting a Company’s Knowledge: The Adaptive Search Agent YASA, Proceedings of the sInternational Conference on Semantics, pp. 166–169, 2008Google Scholar
  9. 9.
    Alex Kohn: Professional Search in Pharmaceutical Research, Doctoral Thesis, Institute for Informatics, Ludwig-Maximilian University of Munich, 2009Google Scholar
  10. 10.
    Corinna Cortes and Vladimir Vapnik: Support-Vector Networks, Machine Learning, Volume 20, Number 3, pp. 273–297, 1995zbMATHGoogle Scholar
  11. 11.
    Thorsten Joachims: Text Categorization With Support Vector Machines: Learning With Many Relevant Features, Machine Learning: Proceedings of the 10th European Conference on Machine Learning (ECML), Claire Nedellec and Celine Rouveirol editors, Springer Verlag, pages 137–142, 1998Google Scholar
  12. 12.
    Fabrizio Sebastiani: Machine Learning in Automated Text Categorization, ACM Computing Surveys, Volume 34, Number 1, pages 1–47, 2002CrossRefGoogle Scholar
  13. 13.
    David Nadeau, and Satoshi Sekine: A Survey of Named Entity Recognition and Classification, Linguisticae Investigationes, 30, (1) pp. 3–26, 2007CrossRefGoogle Scholar
  14. 14.
    Christoph Wieser, François Bry, Alexandre Bérard, and Richard Lagrange: ARTigo: Building an Artwork Search Engine With Games and Higher-Order Latent Semantic Analysis, Proceedings of Disco 2013, Workshop on Human Computation and Machine Learning in Games at HComp, 2013Google Scholar
  15. 15.
    Christoph Wieser: Building a Semantic Search Engine with Games and Crowdsourcing, Doctoral Thesis, Institute for Informatics, Ludwig-Maximilian University of Munich, 2014Google Scholar
  16. 16.
    François Bry and Clemens Schefels: An Analysis of the ARTigo Gaming Ecosystem With a Purpose, Research Report, Institute for Informatics, Ludwig-Maximilian University of Munich, 2016Google Scholar
  17. 17.
    Luis Von Ahn and Laura Dabbish: Labeling Images With a Computer Game, Proceedings of the ACM SIGCHI Conference on Human Factors in Computing Systems (CHI), pp. 319–326, 2004Google Scholar
  18. 18.
    François Bry and Christoph Wieser: Squaring and scripting the ESP game, Proceedings of the 4th Human Computation Workshop (HCOMP), AAAI Press, 2012Google Scholar
  19. 19.
    Bartholomäus Steinmayr: Designing Image Labeling Games For More Informative Tags, Diploma Thesis, Institute for Informatics, Ludwig-Maximilian University of Munich, 2010Google Scholar
  20. 20.
    Bartholomäus Steinmayr, Christoph Wieser, Fabian Kneißl and François Bry: Karido: A GWAP for Telling Artworks Apart, Proceeding of the 16th International Conference on Computer Games (CGAMES), pp. 193–200, 2011 (Best Paper Award)CrossRefGoogle Scholar
  21. 21.
    Sebastian Mader, Christoph Wieser, François Bry, and Clemens Schefels: BibPad as a Library Service or Crowdsourcing a Library Search Engine, Proceedings of the 6th International Conference on Qualitative and Quantitative Methods in Libraries, 2014Google Scholar
  22. 22.
    Sebastian Mader: An Annotation Framework for a Collaborative Learning Platform, Master Thesis, Institute for Informatics, Ludwig-Maximilian University of Munich, 2016Google Scholar
  23. 23.
    François Bry, Vera Gehlen-Baum, and Alexander Pohl: Promoting Awareness and Participation in Large Class Lectures: The Digital Backchannel Backstage, Proceedings of the International Conference e-society, 2011Google Scholar
  24. 24.
    Daniel Baumgart, Alexander Pohl, Vera Gehlen-Baum, and François Bry: Providing Guidance on Backstage, a Novel Digital Backchannel for Large Class Teaching, Education in a Technological World: Communicating Current and Emerging Research and Technological Efforts, 2011Google Scholar
  25. 25.
    Vera Gehlen-Baum, Alexander Pohl and François Bry: Assessing Backstage – A Backchannel for Collaborative Learning in Large Classes, Proceedings of the 14th International Conference on Interactive Collaborative Learning (ICL), 2011Google Scholar
  26. 26.
    Vera Gehlen-Baum, Alexander Pohl, Armin Weinberger, and François Bry: Backstage – Designing a Backchannel for Large Lectures (Demo Paper), Proceedings of the European Conference on Technology Enhanced Learning (EC-TEL), 2012 (Demo Shootout Special Recognition Award)Google Scholar
  27. 27.
    François Bry, Alexander Pohl: Backstage: A Social Medium for Large Classes, Campus Transformation – Education, Qualification and Digitalization, Frank Keuper, Heinrich Arnold editors, Logos Verlag, Berlin, pp. 255–280, 2014Google Scholar
  28. 28.
    Vera Gehlen-Baum, Armin Weinberger, Alexander Pohl, François Bry: Technology use in lectures to enhance student’s attention, Proceedings of the 9th International Conference on Technology Enhanced Learning (EC-TEL), 16–19 September 2014, 2014Google Scholar
  29. 29.
    Alexander Pohl: Fostering Awareness and Collaboration in Large-Class Lectures – Principles and Evaluation of the Backchannel Backstage, Doctoral Thesis, Institute for Informatics, Ludwig-Maximilian University of Munich, 2015Google Scholar
  30. 30.
    François Bry and Alexander Pohl: Large-Class Teaching with Backstage, Journal of Applied Research in Higher Education, Volume 9, Number 1, 2017CrossRefGoogle Scholar
  31. 31.
    James Surowiecki: The Wisdom of Crowds, Doubleday, Anchor, 2004Google Scholar
  32. 32.
    Justin Wolfers and Eric Zitzewitz: Prediction Markets, Journal of Economic Perspectives, Volume 18, Number 2, 2004Google Scholar
  33. 33.
    Robert Forsythe, Forrest Nelson, George R. Neumann, and Jack Wright: Anatomy of an Experimental Political Stock Market, The American Economic Review, Volume 82, Number 5, pages 1142–1161, 1992Google Scholar
  34. 34.
    Joyce Berg, Robert Forsythe, Forrest Nelson, and Thomas Rietz: Results from a Dozen Years of Election Futures Markets Research, in Handbook of Experimental Economic Results, Elsevier, 2000Google Scholar
  35. 35.
    John Maynard Keynes: The General Theory of Employment, Interest, and Money. Macmillan Cambridge University Press, 1936Google Scholar
  36. 36.
    Stephan Leutenmayr, Sven Ziemer and François Bry: Decision Markets for Continuously Reflected Collective Decisions, Proceedings of the 3rd International Conference on Social Eco-Informatics, 2013 (Best Paper Award)Google Scholar
  37. 37.
    Stephan Leutenmayr: Liquid Decision Making: Applying the Market Metaphor to Collective Decision Making, Doctoral Thesis, Institute for Informatics, Ludwig-Maximilian University of Munich, 2015Google Scholar
  38. 38.
    Stephan Leutenmayr, Fabian Kneissl, Sven Ziemer and François Bry: Gameful Markets for Collaboration and Learning, Proceedings of Disco, Proceedings of the Workshop on Human Computation and Machine Learning in Games at HComp, 2013Google Scholar
  39. 39.
    François Bry, Fabian Kneißl, and Christoph Wieser: Field Research for Humanities with Social Media: Crowdsourcing and Algorithmic Data Analysis, Proceedings 4. Workshop Digitale Soziale Medien, 2011Google Scholar
  40. 40.
    Fabian Kneissl and François Bry: MetropolItalia: A Crowdsourcing Platform for Linguistic Field Research, Proceedings of the International Conference WWW/Internet, 2012Google Scholar
  41. 41.
    François Bry, Fabian Kneißl, Thomas Krefeld, Stephan Lücke and Christoph Wieser: Crowdsourcing for a Geographical and Social Mapping of Italian Dialects, Proceedings of the 2nd International Workshop on Social Media for Crowdsourcing and Human Computation (SOHUMAN), 2013Google Scholar
  42. 42.
    Fabian Kneißl: Crowdsourcing for Linguistic Field Research and E-Learning, Doctoral Thesis, Institute for Informatics, Ludwig-Maximilian University of Munich, 2014Google Scholar
  43. 43.
    François Bry: Human Computation-Enabled Network Analysis for a Systemic Credit Risk Rating, Handbook of Human Computation, Pietro Michelucci editor, Springer Verlag, pages 215–246, 2013CrossRefGoogle Scholar
  44. 44.
    Sebastian Poledna and Stefan Thurner: Elimination of Systemic Risk in Financial Networks by Means of a Systemic Risk Transaction Tax, Research Report, arXiv, arXiv:1401.8026v3 [q-fin.RM], 2016Google Scholar
  45. 45.
    David Martin, Benjamin V. Hanrahan, Jacki O’Neill, and Neha Gupta: Being A Turker, Proceedings of the 17th ACM Conference on Computer-Supported Cooperative Work and Social Computing (CSCW), 2014Google Scholar
  46. 46.
    Eli Pariser: The Filter Bubble: What The Internet Is Hiding From You. Penguin Press Limited, New York 2011Google Scholar
  47. 47.
    Samuel R. Bowman and Luke Vilnis: Generating Sentences from a Continuous Space, Arxiv , Arxiv:1511.06349v4, 1016Google Scholar
  48. 48.
    Richard Lea: Google swallows 11,000 novels to improve AI’s conversation, The Guardian, 28 September 2016Google Scholar
  49. 49.
    Jacky Alciné (@jackyalcine): “Google Photos, y’all fucked up. My friend’s not a gorilla”, Twitter, 29 June 2015 https://twitter.com/jackyalcine/status/615329515909156865 Google Scholar
  50. 50.
    Kabir Alli (@iBeKabir): YOOOOOO LOOK AT THIS (Video), Twitter, 7 June 2016 https://twitter.com/ibekabir/status/740005897930452992 Google Scholar
  51. 51.
    Cathy O’Neil: Weapon of Math Destruction – How Big Data Increases Inequality and Threathens Democracy, Penguin Random House UK, 2016Google Scholar
  52. 52.
    Martin Ford: The Rise of the Robots: Technology and the Threat of Mass Unemployment, Basic Book, 2015Google Scholar
  53. 53.
    Thomas Piketty: Capital in the Twenty-First Century, Belknap Press, 2014CrossRefGoogle Scholar
  54. 54.
    Hans Jonas: The Imperative of Responsibility, The University of Chicago Press, 1984Google Scholar
  55. 55.
    Bonnie Kamona (@BonKamona): “I saw a tweet saying ‘unprofessional hair style for work’. I did. Then I checked for ‘professional’ ones”, Twitter, 5 April 2016 https://twitter.com/bonkamona/status/717457819864272896?lang=de Google Scholar

Copyright information

© Springer-Verlag GmbH Deutschland  2017

Authors and Affiliations

  • Martin Bogner
    • 1
  • François Bry
    • 1
  • Niels Heller
    • 1
  • Stephan Leutenmayr
    • 1
  • Sebastian Mader
    • 1
  • Alexander Pohl
    • 1
  • Clemens Schefels
    • 1
  • Yingding Wang
    • 1
  • Christoph Wieser
    • 1
  1. 1.Institute for InformaticsLudwig-Maximilians-Universität MünchenMunichGermany

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