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Büyük Veri (Big Data)’nin Yapay Zekâ Uygulamalarında Toplumsal Sınıflandırmaya Yönelik Kaygılar

Yıl 2021, Cilt: 12 Sayı: 44, 47 - 70, 19.02.2021
https://doi.org/10.5824/ajite.2021.01.004.x

Öz

Büyük Veri kavramı bağlamında gerçekleştirilen çalışmalarda, teknolojik determinist yaklaşımın etkileri dikkat çekmektedir. Bu etkilere yönelik eleştirilerin merkezinde, teknoloji odaklı bir yaklaşımın sosyal, kültürel ve etik yönleri göz ardı edeceği anlayışı bulunmaktadır. İki yönlü tartışmalar içerisinde Büyük Veri kavramı, bir yönden önemli bir teknolojik devrim olarak kabul edilirken diğer yönden ise kontrolün kaybedildiği, toplumsal huzursuzluğun kaynağı olarak distopik senaryolarda yer almaktadır. Bu çalışma, eleştirel bir bakış açısıyla, kavramın, toplumsal yaşamda yarattığı sorunlara odaklanmıştır. Makine yanlılığının büyük miktarlardaki verilerden, zamanla otomatik öğrenme sağlayan yapay zekâ olarak tanımlandığı süreçte, algoritmik ayrımcılığı ve bunun neticesinde yaşanan sınıflandırmayı örneklerle ortaya koymayı amaçlayan çalışmada, niteliksel betimsel analiz yöntemi kullanılmıştır. Büyük Veri kaygıları başlığı altındaki yanlılıklardan yola çıkan çalışma, insanların kâr ve verimlilik arayışındaki programlama ile manipüle edilmesine dikkat çekmek istemiştir. Çalışmanın sonucunda algoritmik ön yargıların, toplumsal sınıflandırmaya ve kaygılara sebep olduğu ve bunu anlayabilen bir toplumsal yapıya ihtiyaç duyulduğu, tartışma konusu olarak tespit edilmiştir. Büyük Veri kavramına ilişkin araştırmalarda bireye saygıyı ön plana alan çözüm süreçlerine gereksinim vardır. Böylece mantık güdümlü bir toplumsal görünümün ortaya çıkması önlenirken, kişi hak ve özgürlükleri, mahremiyeti dikkate alınabilecek, böylece etiketleme ve ötekileştirmeye yönelik çözümler üretilebilecektir.

Kaynakça

  • Amini, A., Soleimany, A., Schwarting, W., Bhatia, S., & Rus, D. (2019). Uncovering and Mitigating Algorithmic Bias through Learned Latent Structure.
  • Arrigo, A. B. (ed) (2018). The SAGE Encyclopedia of Surveillance, Security, and Privacy, Charlotte, NC, USA.
  • Arthur, L. (2013). Big Data Marketing: Engage Your Customers More Effectively and Drive Value. John Wiley & Sons.
  • Baum, S. (2017). On the promotion of safe and socially beneficial artificial intelligence. AI & Soc . 32:543–551. DOI 10.1007/s00146-016-0677-0
  • Barocas, S., Hood, S., & Ziewitz, M. (2013). Governing algorithms: A provocation piece. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2245322
  • Boyd D. & Crawford K. (2012). Critical questions for Big Data. Information Communication Society, 15(5):662–679.
  • Boyd, R., & Holton, R. J. (2018). Technology, innovation, employment and power: Does robotics and artificial intelligence really mean social transformation? Journal of Sociology, 54(3), 331-345.
  • Bronson, K., Knezevic, I. (2016). Big Data in food and agriculture. Big Data & Society, 3(1)
  • Burgess, J., Bruns, A. (2012). Twitter archives and the challenges of" Big Social Data" for media and communication research. M/C Journal, 15(5).
  • Cass, T. (1998). A Handler for Big Data.Vol. 282, Issue 5389, pp. 636 DOI: 10.1126/science.282.5389.636
  • Castelluccia, C.,Le Métayer, D. (2019). Understanding algorithmic decision-making: Opportunities and challenges. EPRS | European Parliamentary Research Service.
  • Castro, D., New, J. (2016). The Promise of Artificial Intelligence. https://euagenda.eu/upload/publications/untitled-53560-ea.pdf
  • Chandra, H. (2019) Artificial Intelligence (AI) vs Machine Learning (ML) vs Big Data https://heartbeat.fritz.ai/artificial-intelligence-ai-vs-machine-learning-ml-vs-big-data-909906eb6a92
  • Chen, E. E., & Wojcik, S. P. (2016). A practical guide to big data research in psychology. Psychological Methods, 21(4), 458.
  • Cheung, M. W. L., & Jak, S. (2016). Analyzing big data in psychology: A split/analyze/meta-analyze approach. Frontiers in psychology, 7, 738.
  • Cooper, A., & Green, C. (2016). Embracing the complexities of ‘big data’in archaeology: the case of the English Landscape and Identities project. Journal of Archaeological Method and Theory, 23(1), 271-304.
  • Cox, M., & Ellsworth, D. (1997). Managing big data for scientific visualization. In ACM Siggraph (Vol. 97, pp. 21-38).
  • Council of Europe (2019). Declaration by the Committee of Ministers on the manipulative capabilities of algorithmic processes. https://www.coe.int/en/web/data-protection/-/declaration-by-the-committee-of-ministers-on-the-manipulative-capabilities-of-algorithmic-processes.
  • Crawford, K. (2016) Artificial Intelligence’s White Guy Problem. Erişim: https://www.nytimes.com/2016/06/26/opinion /sunday/artificial-intelligences-white-guy-problem.html 27.12.2019.
  • Dalton, C. & Thatcher, J. (2014) What Does A Crıtıcal Data Studıes Look Lıke, And Why Do We Care?, Erişim: https:// societyandspace.org
  • Dourish, P. (2016). Algorithms and their others: Algorithmic culture in context. Big Data & Society, 3(2): 1–11.
  • Erevelles, S., Fukawa, N., & Swayne, L. (2016). Big Data consumer analytics and the transformation of marketing. Journal of Business Research, 69(2), 897-904.
  • Ess, C.(2002). “Cultures in Collision Philosophical Lessons From Computer-Mediated Communication.” Metaphilosophy 33(1), 229-253.
  • Friedman, B., & Nissenbaum, H. (1996). Bias in computer systems.ACM Transactions on Information Systems (TOIS), 14(3), 330-347.
  • Gandomi, A., & Haider, M. (2015). Beyond the hype: Big data concepts, methods, and analytics. International Journal of Information Management, 35(2), 137-144.
  • Gandy, O. H. (2010). Engaging rational discrimination: exploring reasons for placing regulatory constraints on decision support systems. Ethics and Information Technology, 12(1), 29-42.
  • Ghosh, D. AI is the Future of Hiring, But It’s Far From Immune to Bias, QUARTZ (Oct. 17, 2017), https://work.qz.com/ 1098954/ai-is-the-future-of-hiring-but-itcould-introduce-bias-if-were-not-careful/ [https://perma.cc/AX9G-B2D2]
  • Hurley, D. Can an Algorithm Tell When Kids Are in Danger?, N.Y. TIMES (Jan.2, 2018), https://www.nytimes.com /2018/01/02/magazine/can-an-algorithm-tell-whenkids-are-in-danger.html [https://perma.cc/3XH5-3NZG].
  • Hofmann, E. (2017). Big data and supply chain decisions: the impact of volume, variety and velocity properties on the bullwhip effect. International Journal of Production Research, 55(17), 5108-5126.
  • Johnson, J. S., Friend, S. B., & Lee, H. S. (2017). Big data facilitation, utilization, and monetization: Exploring the 3Vs in a new product development process. Journal of Product Innovation Management, 34(5), 640-658
  • Katzenbach, C. & Ulbricht, L. (2019). Algorithmic governance. Internet Policy Review, 8(4). DOI: 10.14763/2019.4.1424
  • Kennedy, H., Poell, T., & Van Dijck, J. (2015). Data and agency.
  • Kubler, K. (2017). State of urgency: Surveillance, power, and algorithms in France’s state of emergency. Big Data & Society, 4(2), 1-10.
  • Leavy, S. (2018, May). Gender bias in artificial intelligence: The need for diversity and gender theory in machine learning. In Proceedings of the 1st international workshop on gender equality in software engineering (pp. 14-16).
  • Loeb, S., Dynarski, S., McFarland, D., Morris, P., Reardon, S., & Reber, S. (2017). Descriptive Analysis in Education: A Guide for Researchers. NCEE 2017-4023. National Center for Education Evaluation and Regional Assistance.
  • Manovich, L. (2011). Trending: The promises and the challenges of big social data. Debates in the Digital Humanities, 2, 460-475.
  • McAfee, A., Brynjolfsson, E., Davenport, T. H., Patil, D. J., & Barton, D. (2012). Big data: the management revolution. Harvard Business Review, 90(10), 60-68.
  • McCoy, M. D. (2017). Geospatial Big Data and archaeology: Prospects and problems too great to ignore. Journal of Archaeological Science, 84, 74-94.
  • Murdoch, T. B., & Detsky, A. S. (2013). The inevitable application of big data to health care. Jama, 309(13), 1351-1352.
  • Nicholas, D. (2015) Algorithmic accountability. Digital Journalism 3 (3), 398-415.
  • Noble, S. U. (2018). Algorithms of Oppression: How Search Engines Reinforce Racism. New York: NYU Press.
  • Osoba, O. A., & Welser IV, W. (2017). An intelligence in our image: The risks of bias and errors in artificial intelligence. Rand Corporation.
  • Paganoni, M. C. (2019). Framing Big Data A Linguistic and Discursive Approach. Switzerland:Palgrave Macmillan
  • Panch, T., Mattie, H., & Atun, R. (2019). Artificial intelligence and algorithmic bias: implications for health systems. Journal of Global Health, 9(2).
  • Raghupathi, W., & Raghupathi, V. (2014). Big data analytics in healthcare: promise and potential. Health Information Science and Systems, 2(1), 3.
  • Rainie, L., & Anderson, J. (2017). Code-dependent: Pros and cons of the algorithm age. Pew Research Center, 8.
  • Raub, M. (2018). Bots, Bias and Big Data: Artificial Intelligence, Algorithmic Bias and Disparate Impact Liability in Hiring Practices. Ark. L. Rev., 71, 529.
  • Ravanelli, J. M. (2016). Deleuze and Big Data: How Facebook's Use of Big Data Analytics Shifts Legal Personhood, Privacy and Commercial Expression (Doctoral dissertation, Carleton University).
  • Reischauer, G. (2018). Industry 4.0 as policy-driven discourse to institutionalize innovation systems in manufacturing. Technological Forecasting and Social Change, 132, 26-33. https://doi.org/10.1016/j.techfore.
  • Sandelowski, M. (2000). Whatever happened to qualitative description? Research in Nursing & Health, 23(4), 334-340.
  • Seaver, N. (2017). Algorithms as culture: Some tactics for the ethnography of algorithmic systems. Big Data & Society, 4(2), 1-12.
  • Shin, D. H., & Choi, M. J. (2015). Ecological views of big data: Perspectives and issues. Telematics and Informatics, 32(2), 311-320. https://doi.org/10.1016/j.tele.2014.09.006.
  • Shorey, S., Howard, P. (2016). Automation, big data and politics: A research review, International Journal Communication,10.
  • Silberg, J., & Manyika, J. (2019). Notes from the AI frontier: Tackling bias in AI (and in humans). McKinsey Global Institute (June 2019).
  • Solon, O., Levin, S.(16 Ekim 2016). How Google's search algorithm spreads false information with a rightwing bias. www.theguardian.com/technology/2016/ dec/16/google-autocomplete-rightwing-bias-algorithm-political-propaganda
  • Storey, V. C., & Song, I. Y. (2017). Big data technologies and management: What conceptual modeling can do. Data & Knowledge Engineering, 108, 50-67.
  • Strauß, S. (2015). Big Data – towards a new techno-determinism? ISIS Summit Vienna 2015—The Information Society at the Crossroads.
  • Thatcher, J. (2014). Big data, big questions| Living on fumes: Digital footprints, data fumes, and the limitations of spatial big data. International Journal of Communication, 8 (19), 1765-1783.
  • Tsou, M. H. (2015). Research challenges and opportunities in mapping social media and Big Data. Cartography and Geographic Information Science, 42(sup1), 70-74.
  • Xu, Z., Frankwick, G. L., & Ramirez, E. (2016). Effects of big data analytics and traditional marketing analytics on new product success: A knowledge fusion perspective. Journal of Business Research, 69(5), 1562-1566.
  • Yıldırım, A. & Şimşek, H. (2018). Sosyal Bilimlerde Nitel Araştırma Yöntemleri. Ankara: Seçkin.
  • Završnik, A. (2020, March). Criminal justice, artificial intelligence systems, and human rights. In ERA Forum (Vol. 20, No. 4, pp. 567-583). Springer Berlin Heidelberg.
  • Zwitter, A. (2014) Büyük Veri Etiği (çev.) Ş. Karataş, İletişim Hakkı ve Yeni Medya, Tehditler ve Olanaklar. Ankara: Um:ag, 161-173.

Concerns on Social Sorting in Artificial Intelligence Applications of Big Data

Yıl 2021, Cilt: 12 Sayı: 44, 47 - 70, 19.02.2021
https://doi.org/10.5824/ajite.2021.01.004.x

Öz

The effects of technological determinist approach are noteworthy in the studies carried out in the context of Big Data concept. The understanding that a technology-oriented approach will ignore social, cultural and ethical aspects is at the center of criticism towards these effects. In two-way discussions, the concept of Big Data is regarded as an important technological revolution on the one hand, and on the other, it takes place in dystopic scenarios as the source of social unrest. This study focuses on the problems created by the concept in social life from a critical point of view. The aim of this study is to explain the algorithmic discrimination and the resulting classification in the process where machine bias is defined as artificial intelligence that provides automatic learning over time from large amounts of data. The study starts with biases called Big Data concerns and under this heading the study wanted to draw attention to the manipulation of people by programming in search of profit and efficiency. As a result of the study, it was determined that algorithmic prejudices cause social classification and a social structure that can understand this is needed as the main subject of discussion. In researches on the concept of Big Data, there is a need f or solution processes that prioritize respect for the individual. Thus, the emergence of a logic-driven social appearance will be prevented, individual rights and freedoms, privacy can be taken into consideration, labeling and othering solutions will be produced.

Kaynakça

  • Amini, A., Soleimany, A., Schwarting, W., Bhatia, S., & Rus, D. (2019). Uncovering and Mitigating Algorithmic Bias through Learned Latent Structure.
  • Arrigo, A. B. (ed) (2018). The SAGE Encyclopedia of Surveillance, Security, and Privacy, Charlotte, NC, USA.
  • Arthur, L. (2013). Big Data Marketing: Engage Your Customers More Effectively and Drive Value. John Wiley & Sons.
  • Baum, S. (2017). On the promotion of safe and socially beneficial artificial intelligence. AI & Soc . 32:543–551. DOI 10.1007/s00146-016-0677-0
  • Barocas, S., Hood, S., & Ziewitz, M. (2013). Governing algorithms: A provocation piece. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2245322
  • Boyd D. & Crawford K. (2012). Critical questions for Big Data. Information Communication Society, 15(5):662–679.
  • Boyd, R., & Holton, R. J. (2018). Technology, innovation, employment and power: Does robotics and artificial intelligence really mean social transformation? Journal of Sociology, 54(3), 331-345.
  • Bronson, K., Knezevic, I. (2016). Big Data in food and agriculture. Big Data & Society, 3(1)
  • Burgess, J., Bruns, A. (2012). Twitter archives and the challenges of" Big Social Data" for media and communication research. M/C Journal, 15(5).
  • Cass, T. (1998). A Handler for Big Data.Vol. 282, Issue 5389, pp. 636 DOI: 10.1126/science.282.5389.636
  • Castelluccia, C.,Le Métayer, D. (2019). Understanding algorithmic decision-making: Opportunities and challenges. EPRS | European Parliamentary Research Service.
  • Castro, D., New, J. (2016). The Promise of Artificial Intelligence. https://euagenda.eu/upload/publications/untitled-53560-ea.pdf
  • Chandra, H. (2019) Artificial Intelligence (AI) vs Machine Learning (ML) vs Big Data https://heartbeat.fritz.ai/artificial-intelligence-ai-vs-machine-learning-ml-vs-big-data-909906eb6a92
  • Chen, E. E., & Wojcik, S. P. (2016). A practical guide to big data research in psychology. Psychological Methods, 21(4), 458.
  • Cheung, M. W. L., & Jak, S. (2016). Analyzing big data in psychology: A split/analyze/meta-analyze approach. Frontiers in psychology, 7, 738.
  • Cooper, A., & Green, C. (2016). Embracing the complexities of ‘big data’in archaeology: the case of the English Landscape and Identities project. Journal of Archaeological Method and Theory, 23(1), 271-304.
  • Cox, M., & Ellsworth, D. (1997). Managing big data for scientific visualization. In ACM Siggraph (Vol. 97, pp. 21-38).
  • Council of Europe (2019). Declaration by the Committee of Ministers on the manipulative capabilities of algorithmic processes. https://www.coe.int/en/web/data-protection/-/declaration-by-the-committee-of-ministers-on-the-manipulative-capabilities-of-algorithmic-processes.
  • Crawford, K. (2016) Artificial Intelligence’s White Guy Problem. Erişim: https://www.nytimes.com/2016/06/26/opinion /sunday/artificial-intelligences-white-guy-problem.html 27.12.2019.
  • Dalton, C. & Thatcher, J. (2014) What Does A Crıtıcal Data Studıes Look Lıke, And Why Do We Care?, Erişim: https:// societyandspace.org
  • Dourish, P. (2016). Algorithms and their others: Algorithmic culture in context. Big Data & Society, 3(2): 1–11.
  • Erevelles, S., Fukawa, N., & Swayne, L. (2016). Big Data consumer analytics and the transformation of marketing. Journal of Business Research, 69(2), 897-904.
  • Ess, C.(2002). “Cultures in Collision Philosophical Lessons From Computer-Mediated Communication.” Metaphilosophy 33(1), 229-253.
  • Friedman, B., & Nissenbaum, H. (1996). Bias in computer systems.ACM Transactions on Information Systems (TOIS), 14(3), 330-347.
  • Gandomi, A., & Haider, M. (2015). Beyond the hype: Big data concepts, methods, and analytics. International Journal of Information Management, 35(2), 137-144.
  • Gandy, O. H. (2010). Engaging rational discrimination: exploring reasons for placing regulatory constraints on decision support systems. Ethics and Information Technology, 12(1), 29-42.
  • Ghosh, D. AI is the Future of Hiring, But It’s Far From Immune to Bias, QUARTZ (Oct. 17, 2017), https://work.qz.com/ 1098954/ai-is-the-future-of-hiring-but-itcould-introduce-bias-if-were-not-careful/ [https://perma.cc/AX9G-B2D2]
  • Hurley, D. Can an Algorithm Tell When Kids Are in Danger?, N.Y. TIMES (Jan.2, 2018), https://www.nytimes.com /2018/01/02/magazine/can-an-algorithm-tell-whenkids-are-in-danger.html [https://perma.cc/3XH5-3NZG].
  • Hofmann, E. (2017). Big data and supply chain decisions: the impact of volume, variety and velocity properties on the bullwhip effect. International Journal of Production Research, 55(17), 5108-5126.
  • Johnson, J. S., Friend, S. B., & Lee, H. S. (2017). Big data facilitation, utilization, and monetization: Exploring the 3Vs in a new product development process. Journal of Product Innovation Management, 34(5), 640-658
  • Katzenbach, C. & Ulbricht, L. (2019). Algorithmic governance. Internet Policy Review, 8(4). DOI: 10.14763/2019.4.1424
  • Kennedy, H., Poell, T., & Van Dijck, J. (2015). Data and agency.
  • Kubler, K. (2017). State of urgency: Surveillance, power, and algorithms in France’s state of emergency. Big Data & Society, 4(2), 1-10.
  • Leavy, S. (2018, May). Gender bias in artificial intelligence: The need for diversity and gender theory in machine learning. In Proceedings of the 1st international workshop on gender equality in software engineering (pp. 14-16).
  • Loeb, S., Dynarski, S., McFarland, D., Morris, P., Reardon, S., & Reber, S. (2017). Descriptive Analysis in Education: A Guide for Researchers. NCEE 2017-4023. National Center for Education Evaluation and Regional Assistance.
  • Manovich, L. (2011). Trending: The promises and the challenges of big social data. Debates in the Digital Humanities, 2, 460-475.
  • McAfee, A., Brynjolfsson, E., Davenport, T. H., Patil, D. J., & Barton, D. (2012). Big data: the management revolution. Harvard Business Review, 90(10), 60-68.
  • McCoy, M. D. (2017). Geospatial Big Data and archaeology: Prospects and problems too great to ignore. Journal of Archaeological Science, 84, 74-94.
  • Murdoch, T. B., & Detsky, A. S. (2013). The inevitable application of big data to health care. Jama, 309(13), 1351-1352.
  • Nicholas, D. (2015) Algorithmic accountability. Digital Journalism 3 (3), 398-415.
  • Noble, S. U. (2018). Algorithms of Oppression: How Search Engines Reinforce Racism. New York: NYU Press.
  • Osoba, O. A., & Welser IV, W. (2017). An intelligence in our image: The risks of bias and errors in artificial intelligence. Rand Corporation.
  • Paganoni, M. C. (2019). Framing Big Data A Linguistic and Discursive Approach. Switzerland:Palgrave Macmillan
  • Panch, T., Mattie, H., & Atun, R. (2019). Artificial intelligence and algorithmic bias: implications for health systems. Journal of Global Health, 9(2).
  • Raghupathi, W., & Raghupathi, V. (2014). Big data analytics in healthcare: promise and potential. Health Information Science and Systems, 2(1), 3.
  • Rainie, L., & Anderson, J. (2017). Code-dependent: Pros and cons of the algorithm age. Pew Research Center, 8.
  • Raub, M. (2018). Bots, Bias and Big Data: Artificial Intelligence, Algorithmic Bias and Disparate Impact Liability in Hiring Practices. Ark. L. Rev., 71, 529.
  • Ravanelli, J. M. (2016). Deleuze and Big Data: How Facebook's Use of Big Data Analytics Shifts Legal Personhood, Privacy and Commercial Expression (Doctoral dissertation, Carleton University).
  • Reischauer, G. (2018). Industry 4.0 as policy-driven discourse to institutionalize innovation systems in manufacturing. Technological Forecasting and Social Change, 132, 26-33. https://doi.org/10.1016/j.techfore.
  • Sandelowski, M. (2000). Whatever happened to qualitative description? Research in Nursing & Health, 23(4), 334-340.
  • Seaver, N. (2017). Algorithms as culture: Some tactics for the ethnography of algorithmic systems. Big Data & Society, 4(2), 1-12.
  • Shin, D. H., & Choi, M. J. (2015). Ecological views of big data: Perspectives and issues. Telematics and Informatics, 32(2), 311-320. https://doi.org/10.1016/j.tele.2014.09.006.
  • Shorey, S., Howard, P. (2016). Automation, big data and politics: A research review, International Journal Communication,10.
  • Silberg, J., & Manyika, J. (2019). Notes from the AI frontier: Tackling bias in AI (and in humans). McKinsey Global Institute (June 2019).
  • Solon, O., Levin, S.(16 Ekim 2016). How Google's search algorithm spreads false information with a rightwing bias. www.theguardian.com/technology/2016/ dec/16/google-autocomplete-rightwing-bias-algorithm-political-propaganda
  • Storey, V. C., & Song, I. Y. (2017). Big data technologies and management: What conceptual modeling can do. Data & Knowledge Engineering, 108, 50-67.
  • Strauß, S. (2015). Big Data – towards a new techno-determinism? ISIS Summit Vienna 2015—The Information Society at the Crossroads.
  • Thatcher, J. (2014). Big data, big questions| Living on fumes: Digital footprints, data fumes, and the limitations of spatial big data. International Journal of Communication, 8 (19), 1765-1783.
  • Tsou, M. H. (2015). Research challenges and opportunities in mapping social media and Big Data. Cartography and Geographic Information Science, 42(sup1), 70-74.
  • Xu, Z., Frankwick, G. L., & Ramirez, E. (2016). Effects of big data analytics and traditional marketing analytics on new product success: A knowledge fusion perspective. Journal of Business Research, 69(5), 1562-1566.
  • Yıldırım, A. & Şimşek, H. (2018). Sosyal Bilimlerde Nitel Araştırma Yöntemleri. Ankara: Seçkin.
  • Završnik, A. (2020, March). Criminal justice, artificial intelligence systems, and human rights. In ERA Forum (Vol. 20, No. 4, pp. 567-583). Springer Berlin Heidelberg.
  • Zwitter, A. (2014) Büyük Veri Etiği (çev.) Ş. Karataş, İletişim Hakkı ve Yeni Medya, Tehditler ve Olanaklar. Ankara: Um:ag, 161-173.
Toplam 63 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Bölüm Araştırma Makaleleri
Yazarlar

Sevda Ünal 0000-0003-2754-4780

Ayşe Aslı Sezgin 0000-0002-4557-7351

Yayımlanma Tarihi 19 Şubat 2021
Gönderilme Tarihi 12 Ağustos 2020
Yayımlandığı Sayı Yıl 2021 Cilt: 12 Sayı: 44

Kaynak Göster

APA Ünal, S., & Sezgin, A. A. (2021). Büyük Veri (Big Data)’nin Yapay Zekâ Uygulamalarında Toplumsal Sınıflandırmaya Yönelik Kaygılar. AJIT-E: Academic Journal of Information Technology, 12(44), 47-70. https://doi.org/10.5824/ajite.2021.01.004.x