Pokaż uproszczony rekord

Are They Doing Artificial Intelligence? (Re)Constructing the Primary Activity in Data Science

dc.contributor.authorŻulicki, Remigiusz
dc.date.accessioned2024-12-03T07:17:59Z
dc.date.available2024-12-03T07:17:59Z
dc.date.issued2024-11-30
dc.identifier.urihttp://hdl.handle.net/11089/53851
dc.description.abstractData science (DS) is concerned with building so-called artificial intelligence, i.e., computer systems that automate tasks based on historical data. This article is the first attempt to examine DS using Adele E. Clarke’s framework of social worlds. The main goal of this paper is to show the (re)construction of primary activity based on the example of the social world of DS in Poland. Methodological reflection on this (re)construction is an underdeveloped element in the study of social worlds; therefore, this paper strives to make this process explicit. The empirical background is a three-year ethnographic study, following Clarke’s situational analysis approach. The methodological results demonstrate the indispensability of collaborative ethnography in (re)constructing primary activity and the importance of finding palpable elements as those being crucial to understanding primary activity. The substantive results focus on the idea that data scientists do not refer to their activity as doing artificial intelligence.en
dc.description.abstractData science (DS) zajmuje się budowaniem tzw. sztucznej inteligencji, czyli systemów komputerowych automatyzujących zadania na podstawie danych historycznych. Niniejszy artykuł jest pierwszą próbą zbadania DS z zastosowaniem ramy teoretycznej światów społecznych Adele E. Clarke. Głównym celem opracowania jest przedstawienie (re)konstrukcji działania podstawowego na przykładzie świata społecznego DS w Polsce. Refleksja metodologiczna nad tą (re)konstrukcją jest słabo rozwiniętym elementem badań nad światami społecznymi; niniejszy artykuł stara się ten proces wyeksplikować. Podstawą empiryczną jest trzyletnie badanie etnograficzne, przeprowadzone zgodnie z podejściem analizy sytuacyjnej Clarke. Wyniki metodologiczne prezentują niezbędność etnografii opartej na współpracy w (re)konstruowaniu działania podstawowego oraz znaczenie namacalnych elementów jako kluczowych dla zrozumienia tego działania. Substancjalne wyniki koncentrują się na spostrzeżeniu, że osoby zajmujące się data science nie określają swego działania z użyciem pojęcia sztucznej inteligencji.pl
dc.language.isoen
dc.publisherWydawnictwo Uniwersytetu Łódzkiegopl
dc.relation.ispartofseriesPrzegląd Socjologii Jakościowej;4pl
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0
dc.subjectsocial worldsen
dc.subjectprimary activityen
dc.subjectartificial intelligenceen
dc.subjectdata scienceen
dc.subjectspołeczne światypl
dc.subjectdziałanie podstawowepl
dc.subjectsztuczna inteligencjapl
dc.subjectdata sciencepl
dc.titleAre They Doing Artificial Intelligence? (Re)Constructing the Primary Activity in Data Scienceen
dc.title.alternativeCzy oni tworzą sztuczną inteligencję? (Re)konstrukcja działania podstawowego w data sciencepl
dc.typeArticle
dc.page.number190-213
dc.contributor.authorAffiliationUniversity of Lodz, Polanden
dc.identifier.eissn1733-8069
dc.referencesAlekseichenko Vladimir (2019a), 10 właściwych pytań przy wdrażaniu uczenia maszynowego, https://biznesmysli.pl/10-wlasciwych-pytan-przy-wdrazaniu-uczenia-maszynowego/ [accessed: 30.04.2019].en
dc.referencesAlekseichenko Vladimir (2019b), The difference between AI vs ML, https://www.linkedin.com/feed/update/urn:li:activity:6501030890754314240/ [accessed: 4.05.2019].en
dc.referencesAnderson Leon (2006), Analytic Autoethnography, “Journal of Contemporary Ethnography”, vol. 35(4), pp. 373–395, https://doi.org/10.1177/0891241605280449en
dc.referencesAndrus Calvin, Cook Jon, Sood Suresh (2017), Data Science: An Introduction, https://en.wikibooks.org/wiki/Data_Science:_An_Introduction [accessed: 21.03.2018].en
dc.referencesAngrosino Michael (2010), Badania etnograficzne i obserwacje, Warszawa: Wydawnictwo Naukowe PWN.en
dc.referencesAzam Anum (2014), The First Rule of Data Science, “Berkeley Science Review”, 27.04.2014, https://web.archive.org/web/20170922061629/https://berkeleysciencereview.com/article/first-rule-data-science/ [accessed: 26.01.2018].en
dc.referencesBaiju Nt (2014), What is a data scientist? 14 definitions of a data scientist!, https://web.archive.org/web/20171207002047/https://bigdata-madesimple.com/what-is-a-data-scientist-14-definitions-of-a-data-scientist/ [accessed: 11.01.2018].en
dc.referencesBatorski Dominik, Grzywińska Ilona (2018) Three dimensions of the public sphere on Facebook, “Information Communication and Society”, vol. 21(3), pp. 356–374, https://doi.org/10.1080/1369118X.2017.1281329en
dc.referencesBecker Howard S. (1953), Becoming a Marihuana User, “The American Journal of Sociology”, vol. 59(3), pp. 235–242.en
dc.referencesBiecek Przemysław (2015), Pogromcy Danych. Przetwarzanie danych w programie R, https://web.archive.org/web/20161205211608/http://pogromcydanych.icm.edu.pl/ [accessed: 28.12.2016].en
dc.referencesBig Data Borat (2013), @BigDataBorat: Data Science Is Statistics on Mac, https://twitter.com/bigdataborat/status/372350993255518208 [accessed: 19.02.2018].en
dc.referencesBoyd Danah, Crawford Kate (2011), Six Provocations for Big Data, “SSRN Electronic Journal”, s. 1–17, https://doi.org/10.2139/ssrn.1926431en
dc.referencesCao Longbing (2017), Data Science: A Comprehensive Overview, “ACM Computing Surveys”, vol. 50(3), pp. 1–42, https://doi.org/10.1145/3076253en
dc.referencesCharmaz Kathy (2006), Constructing grounded theory, London: Sage Publications.en
dc.referencesClarke Adele E. (1997), A Social Worlds Research Adventure: The Case of Reproductive Science, [in:] Anselm L. Strauss, Juliet Corbin (eds.), Grounded Theory in Practice, Thousand Oaks: Sage Publications, pp. 63–94.en
dc.referencesClarke Adele E. (2003), Situational Analyses: Grounded Theory Mapping After the Postmodern Turn, “Symbolic Interaction”, vol. 26(4), pp. 553–576.en
dc.referencesClarke Adele E. (2005), Situational Analysis. Grounded Theory After the Postmodern Turn, London: Sage Publications.en
dc.referencesClarke Adele E. (2015), From Grounded Theory to Situational Analysis. What’s New? Why? How?, [in:] Adele E. Clarke, Carrie Friese, Rachel S. Washburn (eds.), Situational Analysis in Practice. Mapping Research with Grounded Theory, Walnut Creek: Left Coast Press Inc., pp. 84–118.en
dc.referencesClarke Adele E., Star Susan Leigh (2008), The Social Worlds Framework: A Theory/Method Package, [in:] Edward J. Hackett, Olga Amsterdamska, Michael Lynch, Judy Wajcman (eds.), The Handbook of Science and Technology Studies, Cambridge–London: The MIT Press, pp. 113–158.en
dc.referencesClarke Adele E., Friese Carrie, Washburn Rachel S. (2015), Introducing Situational Analysis, [in:] Adele E. Clarke, Carrie Friese, Rachel S. Washburn (eds.), Situational Analysis in Practice. Mapping Research with Grounded Theory, Walnut Creek: Left Coast Press Inc., pp. 11–75.en
dc.referencesClarke Adele E., Friese Carrie, Washburn Rachel S. (2017), Situational Analysis: Grounded Theory After the Interpretive Turn, Los Angeles: Sage Publications.en
dc.referencesCleveland William S. (2001), Data Science: An Action Plan for Expanding the Technical Areas of the Field of Statistics, “International Statistical Review”, vol. 69(1), pp. 21–26, https://doi.org/10.1111/j.1751-5823.2001.tb00477.xen
dc.referencesCollins Harry M., Evans Robert (2002), The Third Wave of Science Studies: Studies of Expertise and Experience, “Social Studies of Science”, vol. 32(2), pp. 235–296, https://doi.org/10.1177/0306312702032002003en
dc.referencesConway Drew (2010), The Data Science Venn Diagram, https://web.archive.org/web/20110225163125/http://www.dataists.com/2010/09/the-data-science-venn-diagram/ [accessed: 18.02.2018].en
dc.referencesCrawford Kate (2021), Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence, New Haven: Yale University Press.en
dc.referencesCrowdFlower (2017), 2017 Data Scientist Report, https://visit.crowdflower.com/rs/416-ZBE-142/images/data-scientist-report-dec.pdf [accessed: 11.02.2018].en
dc.referencesDalton Craig M., Taylor Linnet, Thatcher Jim (2016), Critical Data Studies: A dialog on data and space, “Big Data & Society”, vol. 3(1), https://doi.org/10.1177/2053951716648346en
dc.referencesDavenport Thomas H., Patil D.J. (2012), Data Scientist: The Sexiest Job of the 21st Century, “Harvard Business Review”, https://hbr.org/2012/10/data-scientist-the-sexiest-job-of-the-21st-century [accessed: 3.10.2016].en
dc.referencesDean Jeff (2019), Deep Learning to Solve Challenging Problems (Google I/O’19), https://www.youtube.com/watch?v=rP8CGyDbxBY [accessed: 11.06.2019].en
dc.referencesDelapenha Lauren (2017), 42 Essential Quotes by Data Science Thought Leaders, https://www.kdnuggets.com/2017/05/42-essential-quotes-data-science-thought-leaders.html [accessed: 6.02.2018].en
dc.referencesDesai Jules, Watson David, Wang Vincent, Taddeo Mariarosaria, Floridi Luciano (2022), The epistemological foundations of data science: a critical analysis, “SSRN Electronic Journal”, pp. 1–26, https://doi.org/10.2139/ssrn.4008316en
dc.referencesDijck José van (2014), Datafication, dataism and dataveillance: Big Data between scientific paradigm and ideology, “Surveillance and Society”, vol. 12(2), pp. 197–208, https://doi.org/10.24908/ss.v12i2.4776en
dc.referencesDonoho David (2017), 50 Years of Data Science, “Journal of Computational and Graphical Statistics”, vol. 26(4), pp. 745–766, https://doi.org/10.1080/10618600.2017.1384734en
dc.referencesDoran Derek (2018), Data Scientist, [in:] Laurie A. Schintler, Connie L. McNeely (eds.), Encyclopedia of Big Data, Cham: Springer International Publishing, pp. 1–4, https://doi.org/10.1007/978-3-319-32001-4_61-1en
dc.referencesElish M.C., Boyd Danah (2018), Situating methods in the magic of Big Data and AI, “Communication Monographs”, vol. 85(1), pp. 57–80, https://doi.org/10.1080/03637751.2017.1375130en
dc.referencesGallopoulos Efstratios, Houstis Elias, Rice J.R. (1994), Computer as thinker/doer: problem-solving environments for computational science, “IEEE Computational Science and Engineering”, vol. 1(2), pp. 11–23, https://doi.org/10.1109/99.326669en
dc.referencesGerson Elihu M. (1983), Scientific Work and Social Worlds, “Knowledge: Creation, Diffusion, Utilization”, vol. 4(3), pp. 357–377.en
dc.referencesGold Raymond L. (1958), Roles in Sociological Field Observations, “Social Forces”, vol. 36(3), pp. 217–223, https://doi.org/10.2307/2573808en
dc.referencesGoodfellow Ian, Bengio Yoshua, Courville Aaron (2016), Deep Learning, http://www.deeplearningbook.org/ [accessed: 5.11.2017].en
dc.referencesGranville Vincent (2014), 16 analytic disciplines compared to data science, https://web.archive.org/web/20140808055923/http://www.datasciencecentral.com/group/resources/forum/topics/16-analytic-disciplines-compared-to-data-science [accessed: 2.01.2017].en
dc.referencesGrommé Francisca, Ruppert Evelyn, Cakici Baki (2018), Data scientists: a new faction of the transnational field of statistics, [in:] Hannah Knox, Dawn Nafus (eds.), Ethnography for a data-saturated world, Manchester: Manchester University Press, pp. 33–61.en
dc.referencesHughes Everet C. (1958), Men and Their Work, London: The Free Press.en
dc.referencesHyndman Rob (2014), Am I a data scientist?, https://robjhyndman.com/hyndsight/am-i-a-data-scientist/ [accessed: 5.01.2018].en
dc.referencesIwasiński Łukasz (2020), Theoretical Bases of Critical Data Studies, “Zagadnienia Informacji Naukowej – Studia Informacyjne”, vol. 58(1A(115A)), pp. 96–109, https://doi.org/10.36702/zin.726en
dc.referencesJarvis Jeremy (2014), @jeremyjarvis: A Data Scientist Is a Statistician Who Lives in San Francisco, https://twitter.com/jeremyjarvis/status/428848527226437632 [accessed: 7.12.2017].en
dc.referencesJesionek Robert (2017), Uczenie maszynowe i sztuczna inteligencja w opiniach polskich CIO, https://digitalandmore.pl/uczenie-maszynowe-i-sztuczna-inteligencja-w-opiniach-polskich-cio/ [accessed: 24.04.2018].en
dc.referencesJunker Buford H. (1960), Field Work: An Introduction to the Social Sciences, Chicago: University of Chicago Press.en
dc.referencesKacperczyk Anna (2016), Społeczne światy. Teoria – empiria – metody badań: na przykładzie społecznego świata wspinaczki, Łódź: Wydawnictwo Uniwersytetu Łódzkiego.en
dc.referencesKaggle (2017), 2017: The State of Data Science & Machine Learning, https://web.archive.org/web/20180222175627/https://www.kaggle.com/surveys/2017 [accessed: 27.03.2018].en
dc.referencesKitchin Rob (2014), Big Data, new epistemologies and paradigm shifts, “Big Data & Society”, vol. 1(1), pp. 1–12, https://doi.org/10.1177/2053951714528481en
dc.referencesKling Rob, Gerson Elihu M. (1978), Patterns of Segmentation and Intersection in the Computing World, “Symbolic Interaction”, vol. 1(2), pp. 24–43, https://doi.org/10.1525/si.1978.1.2.24en
dc.referencesKonecki Krzysztof (2020), Uwagi na temat tego, co jest postrzegane jako ważne i nieważne w socjologii, “Przegląd Socjologii Jakościowej”, vol. XVI, no. 2, pp. 188–207, https://doi.org/10.18778/1733-8069.16.2.11en
dc.referencesKozinets Robert V. (2003), The Field behind the Screen: Using Netnography for Marketing Research in Online Communities, “Journal of Marketing Research”, vol. 39(1), pp. 61–72, https://doi.org/10.1509/jmkr.39.1.61.18935en
dc.referencesKrzysztofek Kazimierz (2015), Technologie cyfrowe w dyskursach o przyszłości pracy, “Studia Socjologiczne”, vol. 4(219), pp. 5–31, https://journals.pan.pl/Content/91277/mainfile.pdf [accessed: 2.11.2018].en
dc.referencesKuncewicz Łukasz (2019), Lukasz Kuncewicz on LinkedIn: „Data Science Job Interview – How the Questions Will Change in 5 Years?, https://www.linkedin.com/feed/update/urn:li:activity:6556607403457155074 [accessed: 31.07.2019].en
dc.referencesLaney Douglas (2001), 3-D Data Management: Controlling Data Volume, Velocity and Variety, https://web.archive.org/web/20120813181324/https://blogs.gartner.com/doug-laney/files/2012/01/ad949-3D-Data-Management-Controlling-Data-Volume-Velocity-and-Variety.pdf [accessed: 14.03.2016].en
dc.referencesLanthier Mark (2011), An Introduction to Computer Science and Problem Solving, [in:] Mark Lanthier (ed.), COMP 1405, Ottawa: Carleton University, pp. 1–38.en
dc.referencesLassiter Luke Eric (2005), The Chicago Guide to Collaborative Ethnography, Chicago–London: The University of Chicago Press.en
dc.referencesLohr Steve (2009), For Today’s Graduate, Just One Word: Statistics, “The New York Times”, http://www.nytimes.com/2009/08/06/technology/06stats.html [accessed: 15.02.2018].en
dc.referencesLoukides Mike (2010), What is data science?, https://www.oreilly.com/ideas/what-is-data-science [accessed: 8.09.2016].en
dc.referencesLowrie Ian (2016), Caring for Computers: How Russian Data Scientists Refashion Their Laptops, “Anthropology Now”, vol. 8(2), pp. 25–33, https://doi.org/10.1080/19428200.2016.1202578en
dc.referencesLowrie Ian (2017), Algorithmic rationality: Epistemology and efficiency in the data sciences, “Big Data & Society”, vol. 4(1), pp. 1–13, https://doi.org/10.1177/2053951717700925en
dc.referencesLowrie Ian (2018), Becoming a Real Data Scientist. Expertise, Flexibility and Lifelong Learning, [in:] Hannah Knox, Dawn Nafus (eds.), Ethnography for a data-saturated world, Manchester: Manchester University Press, pp. 62–81.en
dc.referencesMarcus George E. (1995), Ethnography in/of the World System: The Emergence of Multi-Sited Ethnography, “Annual Review of Anthropology”, vol. 24, pp. 95–117.en
dc.referencesMartin Vivian B. (2006), The Postmodern Turn: Shall Classic Grounded Theory Take That Detour? A Review Essay, “The Grounded Theory Review”, vol. 5(2/3), pp. 119–129.en
dc.referencesMead George H. (1972), The Philosophy of the Act, Chicago: University of Chicago Press.en
dc.referencesNaur Peter (1974), Concise Survey of Computer Methods, Lund: Studentlitteratur.en
dc.referencesNowosad Jakub (2019), Elementarz programisty. Wstęp do programowania używając R, Poznań: Space A., https://jakubnowosad.com/elp/ [accessed: 16.03.2020].en
dc.referencesNunns James (2017), How Python rose to the top of the data science world, “Computer Business Review”, https://www.techmonitor.ai/technology/data/python-rose-top-data-science-world [accessed: 2.10.2018]en
dc.referencesO’Neil Cathy, Schutt Rachel (2015), Badanie danych: raport z pierwszej linii działań, Gliwice: Wydawnictwo Helion.en
dc.referencesPlummer Ken (2012), My Multiple Sick Bodies: Symbolic Interactionism, Autoethnography and Embodiment, [in:] Bryan S. Turner (ed.), Routledge Handbook of Body Studies, New York: Routledge, pp. 75–93.en
dc.referencesR Core Team (2021), R: A Language and Environment for Statistical Computing, https://www.r-project.org/ [accessed: 3.12.2021].en
dc.referencesSchoenfeld Alan H. (1992), Learning to Think Mathematically: Problem Solving, Metacognition, and Sense Making in Mathematics, [in:] Douglas Grouws (ed.), Handbook of Research on Mathematics Teaching and Learning, New York: Macmillan Publishers Limited, pp. 334–370.en
dc.referencesSeim Josh (2021), Participant Observation, Observant Participation, and Hybrid Ethnography, “Sociological Methods & Research”, vol. 53(1), pp. 1–32, https://doi.org/10.1177/0049124120986209en
dc.referencesShaw Zed A. (2014), Learn Python the Hard Way: A Very Simple Introduction to the Terrifyingly Beautiful World of Computers and Code, Donnelley: Addison-Wesley.en
dc.referencesShibutani Tamotsu (1955), Reference Groups as Perspectives, “American Journal of Sociology”, vol. 60(6), pp. 562–569, https://doi.org/10.1086/221630en
dc.referencesSt. Germain James H. de (2008), Problem Solving, https://www.cs.utah.edu/~germain/PPS/Topics/problem_solving.html [accessed: 27.01.2019].en
dc.referencesStrauss Anselm L. (1978), A Social World Perspective, [in:] Norman Denzin (ed.), Studies in Symbolic Interaction, vol. 1, Greenwitch: JAI Press, pp. 119–128.en
dc.referencesStrauss Anselm L. (1982), Social Worlds and Legitimation Processes, [in:] Norman Denzin (ed.), Studies in Symbolic Interaction, vol. 4, Greenwitch: JAI Press, pp. 171–190.en
dc.referencesStrauss Anselm L. (1984), Social Worlds and Their Segmentation Processes, [in:] Norman Denzin (ed.), Studies in Symbolic Interaction, vol. 5, Greenwitch: JAI Press, pp. 123–139.en
dc.referencesTaylor David (2016), Battle of the Data Science Venn Diagrams, https://web.archive.org/web/20170428061035/https://www.kdnuggets.com/2016/10/battle-data-science-venn-diagrams.html [accessed: 14.12.2017].en
dc.referencesThieme Nick (2018), R generation, “Significance”, vol. 15(4), pp. 14–19, https://doi.org/10.1111/j.1740-9713.2018.01169.xen
dc.referencesThomas Suzanne L., Nafus Dawn, Sherman Jamie (2018), Algorithms as fetish: Faith and possibility in algorithmic work, “Big Data & Society”, vol. 5(1), pp. 1–11, https://doi.org/10.1177/2053951717751552en
dc.referencesTrzpiot Grażyna (2017), Rozumienie Data Science, [in:] Grażyna Trzpiot (ed.), Statystyka a Data Science, Katowice: Wydawnictwo Uniwersytetu Ekonomicznego w Katowicach, pp. 6–30.en
dc.referencesTufekci Zeynep (2015), Algorithmic Harms Beyond Facebook and Google: Emergent Challenges of Computational Agency, “Telecomm & High Tech”, vol. 203, pp. 203–218.en
dc.referencesUnruh David R. (1980), The Nature of Social Worlds, “The Pacific Sociological Review”, vol. 23(3), pp. 271–296, https://doi.org/10.2307/1388823en
dc.referencesUri Therese (2015), The Strengths and Limitations of Using Situational Analysis Grounded Theory as Research Methodology, “Journal of Ethnographic & Qualitative Research”, vol. 10(1), pp. 135–151.en
dc.referencesVail D. Angus (1999), The Commodification of Time in Two Art Worlds, “Symbolic Interaction”, vol. 22(4), pp. 325–344.en
dc.referencesWacquant Loïc (2004), Body and Soul: Notebooks of an Apprentice Boxer, New York: Oxford University Press.en
dc.referencesWickham Hadley (2018), You Can’t Do Data Science in a GUI, https://www.youtube.com/watch?v=cpbtcsGE0OA [accessed: 27.11.2018].en
dc.referencesWickham Hadley, Grolemund Garrett (2017), R for Data Science: Import, Tidy, Transform, Visualize, and Model Data, Beijing–Boston–Farnham–Sebastopol–Tokyo: O’Reilly.en
dc.referencesXie Yihui, Allaire Joseph J., Grolemund Garrett (2018), R Markdown: The Definitive Guide, Boca Raton: Chapman and Hall/CRC.en
dc.referencesZuboff Shoshana (2019), The Age of Surveillance Capitalism: The Fight for a Human Future at the New Frontier of Power, New York: PublicAffairs.en
dc.referencesŻulicki Remigiusz (2022), Data science: najseksowniejszy zawód XXI wieku w Polsce. Big data, sztuczna inteligencja i PowerPoint, Łódź: Wydawnictwo Uniwersytetu Łódzkiego.en
dc.contributor.authorEmailremigiusz.zulicki@uni.lodz.pl
dc.identifier.doi10.18778/1733-8069.20.4.09
dc.relation.volume20


Pliki tej pozycji

Thumbnail
Nazwa:
190-213_Zulicki.pdf
Rozmiar:
789.4KB
Format:
PDF
Oglądaj/Otwórz

Pozycja umieszczona jest w następujących kolekcjach

Pokaż uproszczony rekord

https://creativecommons.org/licenses/by-nc-nd/4.0
Poza zaznaczonymi wyjątkami, licencja tej pozycji opisana jest jako https://creativecommons.org/licenses/by-nc-nd/4.0