Is Google Responsible for Providing Fair and Unbiased Results?

  • Dirk Lewandowski
Part of the Law, Governance and Technology Series book series (LGTS, volume 31)


This chapter discusses the responsibilities of Google as the leading search engine provider to provide fair and unbiased results. In its role, Google has a large influence on what is actually searchable on the Web as well as what results users get to see when they search for information. Google serves billions of queries per month, and users only seldom consider alternatives to this search engine. This market dominance further exacerbates the situation. This leads to questions regarding the responsibility of search engines in general, and Google in particular, for providing fair and balanced results. Areas to consider here are (1) the inclusion of documents in the search engine’s databases and (2) results ranking and presentation. I find that, while search engines should at least be held responsible for their practices regarding indexing, results ranking, delivering results from collections built by the search engine provider itself and the presentation of search engine results pages; today’s dominant player, Google, argues that there actually is no problem with these issues. Its basic argument here is that “competition is one click away”, and, therefore, it should be treated like any other smaller search engine company. I approach the topic from two standpoints: from a technical standpoint, I will discuss techniques and algorithms from information retrieval and how decisions made in the design of the algorithms influence what we as users get to see in search engines. From a societal standpoint, I will discuss what biased search engines mean for knowledge acquisition in society and how we can overcome today’s unwanted search monopoly.


Search Engine Information Object Ranking Algorithm Organic Result Deliberate Choice 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. Bar-Ilan, J., Keenoy, K., Levene, M., & Yaari, E. (2009). Presentation bias is significant in determining user preference for search results—A user study. Journal of the American Society for Information Science and Technology, 60(1), 135–149.CrossRefGoogle Scholar
  2. Broder, A. (2002). A taxonomy of web search. ACM Sigir Forum, 36(2), 3–10.CrossRefGoogle Scholar
  3. Bundesverband Digitale Wirtschaft. (2009). Nutzerverhalten Auf Google-Suchergebnisseiten: Eine Eyetracking-Studie Im Auftrag Des Arbeitskreises Suchmaschinen-Marketing Des Bundesverbandes Digitale Wirtschaft (BVDW) e.V.Google Scholar
  4. comScore. (2013). Europe digital future in focus: Key insights from 2012 and what they mean for the coming year. From Accessed 12 May 2016.Google Scholar
  5. Denecke, K. (2012). Diversity-Aware search : New possibilities and challenges for web search. In D. Lewandowski (Ed.), Web search engine research (pp. 139–162). Bingley: Emerald Group Publishing Ltd.. doi: 10.1108/S1876-0562(2012)002012a008.CrossRefGoogle Scholar
  6. Diakopoulos, N. (2013). Sex, violence, and autocomplete algorithms: What words do Bing and google censor from their suggestions? Slate. Accessed 12 May 2016.
  7. Edelman, B. (2010). Hard-coding bias in google ‘Algorithmic’ search results. Accessed 12 May 2016.
  8. Edelman, B. (2014). Google’s Advertisement Labeling in 2014. Accessed 12 May 2016.
  9. Filistrucchi, L., Tucker, C., Edelman, B., & Gilchrist, D. S. ((2012). Advertising disclosures: Measuring labeling alternatives in internet search engines. Information Economics and Policy, 24(1), 75–89.CrossRefGoogle Scholar
  10. Gillespie, T. (2014). The relevance of algorithms. In T. Gillespie, P. Boczkowski, & K. Foot (Eds.), Media technologies (pp. 167–193). Cambridge, MA: MIT Press.Google Scholar
  11. Giunchiglia, F., Maltese, V., Madalli, D., Baldry, A., Wallner, C., Lewis, P., Denecke, K., Skoutas, D., & Marenzi, I. (2009). Foundations for the representation of diversity, evolution, opinion and bias.Google Scholar
  12. Hendry, D. G., & Efthimiadis, E. N. (2008). Conceptual models for search engines. In A. Spink & M. Zimmer (Eds.), Web searching : Multidisciplinary perspectives (pp. 277–308). Berlin: Springer.CrossRefGoogle Scholar
  13. Karaganis, J., & Urban, J. (2015). The rise of the robo notice. Communications of the ACM, 58(9), 28–30. doi: 10.1145/2804244.CrossRefGoogle Scholar
  14. Keane, M. T., O’Brien, M., & Smyth, B. (2008). Are people biased in their use of search engines? Communications of the ACM, 51(2), 49–52.CrossRefGoogle Scholar
  15. Lewandowski, D. (2012). Credibility in web search engines. In M. Folk & S. Apostel (Eds.), Online credibility and digital ethos: Evaluating computer-mediated communication (pp. 131–146). Hershey: IGI Global.Google Scholar
  16. Lewandowski, D. (2014a). Why we need an independent index of the web. Information retrieval; digital libraries. In R. König & M. Rasch (Eds.), Society of the query reader: Reflections on web search (pp. 49–58). Amsterdam: Institute of Network Culture.Google Scholar
  17. Lewandowski, D. (2014b). Wie Lässt Sich Die Zufriedenheit Der Suchmaschinennutzer Mit Ihren Suchergebnissen Erklären? In H. Krah & R. Müller-Terpitz (Eds.), Suchmaschinen (Passauer Schriften Zur Interdisziplinären Medienforschung, Band 4) (pp. 35–52). Münster: LIT.Google Scholar
  18. Lewandowski, D. (2015a). Living in a world of biased search engines. Online Information Review, 39(3), 278–280. doi: 10.1108/OIR-03-2015-0089.CrossRefGoogle Scholar
  19. Lewandowski, D. (2015b). Suchmaschinen Verstehen. Berlin Heidelberg: Springer Vieweg.CrossRefGoogle Scholar
  20. Lewandowski, D.(2016). Status Quo und Entwicklungsperspektiven des Suchmaschinenmarkts. In T. Pellegrini and J. Krone (Eds.), Hanbuch Medienökonomie. Berlin Heidelberg: Springer. doi:  10.1007/978-3-658-09632-8_38-1.
  21. Lewandowski, D., & Sünkler, S. (2013). Representative online study to evaluate the revised commitments proposed by Google on 21 October 2013 as Part of EU Competition Investigation AT.39740-Google Report for Germany. Hamburg.Google Scholar
  22. Liu, Zeyang, Yiqun Liu, Ke Zhou, Min Zhang, and Shaoping Ma. (2015). Influence of vertical result in web search examination. In Proceedings of SIGIR’15, August 09–13, 2015, Santiago, Chile. New York: ACM.Google Scholar
  23. Mager, A. (2012). Algorithmic ideology: How capitalist society shapes search engines. Information, Communication & Society, 15(5), 769–787. doi: 10.1080/1369118X.2012.676056.CrossRefGoogle Scholar
  24. Noble, S. U. (2012). Missed connections: What search engines say about women. Bitch Magazine, 54, 36–41.Google Scholar
  25. Noble, S.U. (2013). Google search: Hyper-visibility as a means of rendering black women and girls invisible. InVisible culture: An electronic journal for visual culture. Accessed 12 May 2016.
  26. Ntoulas, A., Cho, J., & Olston, C. (2004). What’s new on the web?: The evolution of the web from a search engine perspective. InProceedings of the 13th international conference on World Wide Web (pp. 1–12). New York: ACM.Google Scholar
  27. Pan, B., Hembrooke, H., Joachims, T., Lorigo, L., Gay, G., & Granka, L. (2007). In google we trust: Users’ decisions on rank, position, and relevance. Journal of Computer-Mediated Communication, 12(3), 801–823.CrossRefGoogle Scholar
  28. Pariser, E. (2011). The filter bubble: What the internet is hiding from you. London: Viking.Google Scholar
  29. Patterson, A. (2004). Why writing your own search engine is hard. Queue, 2(2), 49–53.CrossRefGoogle Scholar
  30. Piper, P. S. (2000). Better read that again: Web hoaxes and misinformation. Searcher. Searcher, 8(8), 40.Google Scholar
  31. Purcell, K., Brenner, J., & Lee, R. (2012). Search engine use 2012. Washington, DC: Pew Internet & American Life Project.Google Scholar
  32. Risvik, K. M., & Michelsen, R. (2002). Search engines and web dynamics. Computer Networks, 39(3), 289–302.CrossRefGoogle Scholar
  33. Saracevic, T. (2015). Why is relevance still the basic notion in information science ? In F. Pehar, C. Schlögl, & C. Wolff (Eds.), Re:inventing Information Science in the Networked Society. Proceedings of the 14th International Symposium on Information Science (ISI 2015), Zadar, Croatia, 19th–21st May 2015 (pp. 26–35). Glückstadt: Verlag Werner Hülsbusch.Google Scholar
  34. Spink, A., Jansen, B. J., Blakely, C., & Koshman, S. (2006). A study of results overlap and uniqueness among major web search engines. Information Processing & Management, 42(5), 1379–1391.CrossRefGoogle Scholar
  35. Stats: comScore. (2015). Search engine land. Accessed 12 May 2016.
  36. Taddeo, M., & Floridi, L. (2015). The debate on the moral responsibilities of online service providers. Science and Engineering Ethics. doi: 10.1007/s11948-015-9734-1.Google Scholar
  37. Tavani, H. (2012). Search engines and ethics.. [Plattformname.] Accessed 12 May 2016.
  38. van Couvering, E. (2007). Is relevance relevant? Market, science, and war: Discourses of search engine quality. Journal of Computer-Mediated Communication, 12(3), 866–887.CrossRefGoogle Scholar
  39. Vaughan, L., & Zhang, Y. (2007). Equal representation by search engines? A comparison of websites across countries and domains. Journal of Computer-Mediated Communication, 12(3), 888–909.CrossRefGoogle Scholar
  40. White, R. W., & Horvitz, E. (2009). Cyberchondria. ACM Transactions on Information Systems, 27(4): Article No. 23. doi:10.1145/1629096.1629101.Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Hamburg University of Applied SciencesHamburgGermany

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