Structural Ageism in Big Data Approaches

Open access

Abstract

Digital systems can track every activity. Their logs are the fundamental raw material of intelligent systems in big data approaches. However, big data approaches mainly use predictions and correlations that often fail in the prediction of minorities or invisibilize collectives, causing discriminatory decisions. While this discrimination has been documented regarding, sex, race and sexual orientation, age has received less attention. A critical review of the academic literature confirms that structural ageism also shapes big data approaches. The article identifies some instances in which ageism is in operation either implicitly or explicitly. Concretely, biased samples and biased tools tend to exclude the habits, interests and values of older people from algorithms and studies, which contributes to reinforcing structural ageism.

If the inline PDF is not rendering correctly, you can download the PDF file here.

  • AGE Platform Europe. (2016). AGE Platform Europe position on structural ageism. Brussels Belgium.

  • Alvarez-Lozano J. Osmani V. Mayora O. et al. (2014). Tell me your apps and I will tell you your mood. In Conference on pervasive technologies related to assistive environments (PETRA’14) (pp. 1-7). Island of Rhodes: ACM Press.

  • Ayalon L. & Tesch-Römer C. (eds.) (2018). Contemporary perspectives on ageism. Cham: Springer Open.

  • Bayot R. K. & Gon T. (2017). Age and gender classification of tweets using convolutional neural networks. In Machine learning optimization and big data (MOD 2017) (pp. 337-348). Volterra: Springer.

  • Bi B. Shokouhi M. Kosinski M. & Graepel T. (2013). Inferring the demographics of search users: Social data meets search queries. In Conference on World Wide Web (WWW’13) (pp. 131-140) Rio de Janeiro: ACM Press.

  • Bijker W. E. Hughes T. P. & Pinch T. J. (eds.) (1989). The social construction of technological systems. London: MIT Press.

  • Bolukbasi T. Chang K.-W. Zou J. Y. Saligrama V. & Kalai A. T. (2016). Man is to computer programmer as woman is to homemaker? Debiasing word embeddings. In Neural information processing systems (NIPS’16). Barcelona. Retrieved from https://www.semanticscholar.org/paper/Man-is-to-Computer-Programmer-as-Woman-is-to-Word-Bolukbasi-Chang/274459c52103f9b7880d0697aa28755ac3366987

  • Bonchi F. Hajian S. Mishra B. & Ramazzotti D. (2017). Exposing the probabilistic causal structure of discrimination. International Journal of Data Science and Analytics 3: 1-21.

  • Boyd D. & Crawford K. (2012). Critical questions for big data. Information and Communication Society 15: 662-679.

  • Bucholtz M. & Hall K. (2005). Identity and interaction: A sociocultural linguistic approach. Discourse Studies 7: 585-614.

  • Böhmer M. Hecht B. Schöning J.J. Krüger A. & Bauer G. (2011). Falling asleep with Angry Birds Facebook and Kindle: A large scale study on mobile application usage. In Human–computer interaction with mobile devices and services (MobileHCI’11) (pp. 47-56). Stockholm: ACM Press.

  • Calasanti T. & King N. (2015). Intersectionality and age. In J. Twigg & W. Martin (eds.) Routledge handbook of cultural gerontology (pp. 193-200). London: Routledge/Taylor and Francis.

  • Castells M. (2009). Communication power. United Kingdom: Oxford University Press.

  • Castells M. Fernández-Ardèvol M. Linchuan Qiu J. & Sey A. (2006). Mobile communication and society: A global perspective. Cambridge MA: The MIT Press.

  • Choudrie J. & Vyas A. (2014). Silver surfers adopting and using Facebook? A quantitative study of Hertford-shire UK applied to organizational and social change. Technological Forecasting and Social Change 89: 293-305.

  • Culotta A. Ravi N. K. & Cutler J. (2016). Predicting Twitter user demographics using distant supervision from website traffic data. Journal of Artificial Intelligence Research 55: 389-408.

  • De Montjoye Y.-A. Quoidbach J. Robic F. & Pentland A. (2013). Predicting personality using novel mobile phone-based metrics. In A. Greenberg W. Kennedy & N. Bos (eds.) Social computing behavioral-cultural modeling and prediction (pp. 48-55). Heidelberg: Springer

  • Eckert P. (1998). Age as a sociolinguistic variable. In F. Coulmas (ed.) The handbook of sociolinguistics. Oxford United Kingdom: Blackwell.

  • EPSC. (2018). The age of artificial intelligenceTowards a European strategy for human-centric machines. Heidelberg: Springer

  • Eubanks V. (2018). Automating inequality: How high-tech tools profile police and punish the poor. New York: St Martin’s Press.

  • Eurostat. (2017). Population structure and ageing. Retrieved from http://ec.europa.eu/eurostat/statistics-explained/index.php/Population_structure_and_ageing [Accessed 2018 March 1].

  • Eurostat. (2018). Individuals Internet use. Last Internet use in the last 3 months. Table [isoc_ci_ifp_iu]. Retrieved from http://ec.europa.eu/eurostat/web/products-datasets/-/isoc_ci_ifp_iu [Accessed 2018 March 1].

  • Ferdous R. Osmani V. & Mayora O. (2015). Smartphone app usage as a predictor of perceived stress levels at workplace. In Proceedings of the 2015 9th international conference on pervasive computing technologies for healthcare (PervasiveHealth’15) (pp. 225-228). https://doi.org/10.4108/icst.pervasive-health.2015.260192. Istanbul: European Union Digital Library.

  • Fernández-Ardèvol M. & Ivan L. (2013). Older people and mobile communication in two European contexts. Romanian Journal of Communication and Public Relations 15: 83-101.

  • Ferreira D. Goncalves J. Kostakos V. et al. (2014). Contextual experience sampling of mobile application micro-usage. In Human–computer interaction with mobile devices & services (MobileHCI’14) (pp. 91-100). Toronto: ACM Press.

  • Ferreira D. Kostakos V. & Dey A. K. (2012). Lessons learned from large-scale user studies: Using Android market as a source of data. International Journal of Mobile Human Computer Interaction 4: 28-43.

  • Garattini C. & Prendergast D. (2015). Critical reflections on ageing and technology in the twenty-first century. In D. Prendergast & C. Garattini (eds.) Aging and the digital life course (pp. 1-15). New York: Berghahn Books.

  • Greenwald A. G. & Banaji M. R. (1995). Implicit social cognition: Attitudes self-esteem and stereotypes. Psychological Review 102: 4-27.

  • Greenwood S. Perrin A. & Duggan M. (2016). Social media update. Retrieved from http://www.pewinternet.org/2016/11/11/social-media-update-2016/

  • Hajian S. & Domingo-Ferrer J. (2013). A methodology for direct and indirect discrimination prevention in data mining. IEEE Transactions on Knowledge and Data Engineering 25: 1445-1459.

  • Hendricks J. (2005). Ageism: Looking across the margin in the new millennium. Generations 29: 5-7.

  • Holmes J. (2013). An introduction to sociolinguistics (4th ed.). New York: Routledge.

  • Holz C. Bentley F. Church K. & Patel M. (2015). “I’m just on my phone and they’re watching TV”: Quantifying mobile device use while watching television. In Conference on interactive experiences for TV and online video (TVX’15). Brussels: ACM Press

  • Ikebe Y. Katagiri M. & Takemura H. (2012). Friendship prediction using semi-supervised learning of latent features in smartphone usage data. In Knowledge discovery and information retrieval (KDIR’2012). Barcelona: Science and Technology Publications Lda.

  • Jacobson J. Lin C. Z. & McEwen R. (2017). Aging with technology: Seniors and mobile connections. Canadian Journal of Communication 42: 331.

  • Jensen M. (2013). Challenges of privacy protection in big data analytics. In BigData’13 (pp. 235-238). doi: 10.1109/BigData.Congress.2013.39

  • Jones S. L. Ferreira D. Hosio S. Goncalves J. & Kostakos V. (2015). Revisitation analysis of smartphone app use. In Pervasive and ubiquitous computing (UbiComp’15) (pp. 1197-1208). Osaka: ACM Press

  • Karikoski J. & Soikkeli T. (2013). Contextual usage patterns in smartphone communication services. Personal and Ubiquitous Computing 17: 491-502.

  • Kitchin R. (2014). The data revolution: Big data open data data infrastructures and their consequences. Los Angeles: Sage.

  • Kiukkonen N. Blom J. Dousse O. Gatica-Perez D. & Laurila J. (2010). Towards rich mobile phone datasets: Lausanne data collection campaign. In Pervasive services (ICPS’10). Berlin.

  • Kosinski M. Stillwell D. & Graepel T. (2013). Private traits and attributes are predictable from digital records of human behavior. National Academy of Sciences 110: 5802-5805.

  • Lagacé M. Charmarkeh H. Tanguay J. & Annick L. (2015). How ageism contributes to the second-level digital divide: The case of Canadian seniors. Journal of Technologies and Human Usability 11: 1-13.

  • Lee U. Lee J. Ko M. et al. (2014). Hooked on smartphones: An exploratory study on smartphone overuse among college students. In Human factors in computing systems (CHI’14) (pp. 2327-2336). Toronto: ACM Press

  • Letouzé E. (2015). Big data and development: General overview primer. Data-Pop Alliance. Retrieved from http://datapopalliance.org/wp-content/uploads/2015/12/Big-Data-Dev-Overview.pdf

  • Liao L. Jiang J. Ding Y. et al. (2014). Lifetime lexical variation in social media. In Artificial intelligence (AAAI’14) (pp. 1643-1649).

  • Ling R. Bertel T. F. & Sundsøy P. R. (2012). The socio-demographics of texting: An analysis of traffic data. New Media & Society 14: 281-298.

  • Liu J-.Y. & Yang Y.-H. (2012). Inferring personal traits from music listening history. In Music information retrieval with user-centered and multimodal strategies (MIRUM ’12) (p. 31).

  • Mihailidis P. (2014). A tethered generation: Exploring the role of mobile phones in the daily life of young people. Mobile Media & Communication 2: 58-72.

  • Möller A. Kranz M. Schmid B. Roalter L. & Diewald S. (2013). Investigating self-reporting behavior in long-term studies. In Human factors in computing systems (CHI’13) (pp. 2931-2940). Paris: ACM Press.

  • Neugarten B. L. (1996). The meanings of age: Selected papers of Bernice L. Neugarten. Chicago IL: University of Chicago Press.

  • Nguyen D. Gravel R. Trieschnigg D. & Meder T. (2013). “How old do you think I am?”: A study of language and age in Twitter. In AAAI conference on weblogs and social media (pp. 439-448). Palo Alto CA: AAAI Press.

  • Nguyen D. Trieschnigg D. Doğruöz A. S. et al. (2014). Why gender and age prediction from tweets is hard: Lessons from a crowdsourcing experiment. In The annual meeting of the EPSRC network on vision & language and the technical meeting of the European network on integrating vision and language: A workshop of the international conference on computational linguistics (COLING 2014) (pp. 1950-1961). Dublin Ireland: COLING.

  • O’Neil C. (2016). Weapons of math destruction. How big data increases inequality and threatens democracy. New York: Broadway Books.

  • Officer A. & de la Fuente-Núñez V. (2018). A global campaign to combat ageism. Bulletin of the World Health Organization 96: 295-296.

  • Oktay H. Firat A. & Ertem Z. (2012). Demographic breakdown of Twitter users: An analysis based on names. ASE BIGDATA/SOCIALCOM/CYBERSECURITY 1-11.

  • Oreglia E. & Kaye J. “Jofish” (2012). A gift from the city: Mobile phones in rural China. In Computer-supported cooperative work and social computing (CSCW’15) (pp. 137-146). Seattle: ACM Press.

  • Ørmen J. & Thorhauge A. M. (2015). Smartphone log data in a qualitative perspective. Mobile Media & Communication 3: 335-350.

  • Oulasvirta A. Rattenbury T. Ma L. & Raita E. (2012). Habits make smartphone use more pervasive. Personal and Ubiquitous Computing 16: 105-114.

  • Pedreschi D. Ruggieri S. & Turini F. (2009). Measuring discrimination in socially-sensitive decision records. In SIAM international conference on data mining (pp. 581-592). Nevada: Society for Industrial and Applied Mathematics

  • Peersman C. Daelemans W. & Van Vaerenbergh L. (2011). Predicting age and gender in online social networks. In International workshop on search and mining user-generated contents (SMUC’11) 2011 October 28 Glasgow Scotland UK (pp. 37-44). ACM Press.

  • Perozzi B. & Skiena S. (2015a). Exact age prediction in social networks. In International conference on world wide web (pp. 91-92). Florence: ACM Press.

  • Popov V. Kosinski M. Stillwell D. & Kielczewski B. (2018). Apply magic sauce. Retrieved from https://applymagicsauce.com/research.html [Accessed 2018 January 1].

  • Rahmati A. Tossell C. Shepard C. Kortum P. & Zhong L. (2012). Exploring iPhone usage. In Humancomputer interact with mobile devices and services (MobileHCI’11). San Francisco: ACM Press

  • Rieder B. & Röhle T. (2012). Digital methods: Five challenges. In D. M. Berry (ed.) Understanding digital humanities. London: Palgrave Macmillan.

  • Righi V. Sayago S. Rosales A. et al. (2018). Co-designing with a community of older learners for over 10 years by moving user-driven participation from the margin to the centre. CoDesign 14: 32-44.

  • Roca Salvatella. (2016). La brecha digital en la ciudad de Barcelona. Barcelona Spain.

  • Rogers Y. Paay J. Brereton M. Vaisutis K. Marsden G. & Vetere F. (2014). Never too old: Engaging retired people inventing the future with MaKey. In Human factors in computing systems (CHI’14) (pp. 3913-3922). Toronto: ACM Press

  • Rosales A. & Fernández-Ardèvol M. (2016a). Beyond WhatsApp: Older people and smartphones. Romanian Journal of Communication and Public Relations 18: 27-47.

  • Rosales A. & Fernández-Ardèvol M. (2016b). Smartphones apps and older people’s interests: From a generational perspective. In Humancomputer interaction with mobile devices and services (MobileHCI’16) (pp. 491-503). Florence: ACM Press.

  • Rosenthal S. & McKeown K. (2011). Age prediction in blogs: A study of style content and online behavior in pre-and post-social media generations. In Meeting of the Association for Computational Linguistics: Human language technologies (pp. 763-772). Portland: Association for Computational Linguistics

  • Sawchuk K. & Crow B. (2011). Into the grey zone: Seniors cell phones and milieus that matter. WI: Journal of Mobile Media 5.

  • Schäfer M. T. & Van Es K. (2017). The datafied society: Studying culture through data. Amsterdam: Amsterdam University Press.

  • Schwartz H. A. Eichstaedt J. C. Kern M. L. et al. (2013). Personality gender and age in the language of social media: The open-vocabulary approach. PLoS One 8: e73791.

  • Selwyn N. Gorard S. Furlong J. & Madden L. (2003). Older adults’ use of information and communications technology in everyday life. Ageing and Society 23: 561–582.

  • Shin C. Hong J.-H. & Dey A. K. (2012). Understanding and prediction of mobile application usage for smart phones. In Pervasive and ubiquitous computing (UbiComp’12) (p. 173). Pittsburgh: ACM Press

  • Singh V. K. Freeman L. Lepri B. & Pentland A. (2013). Predicting spending behavior using socio-mobile features. In Social computing (pp. 174-179). Washington: IEEE Computer Society Press

  • Smith M. Szongott C. Henne B. Voigt G. Von (2012). Big data privacy issues in public social media. Digital Ecosystems Technologies (DEST’12).Campione d’Italia: IEEE Computer Society Press

  • Srinivasan V. Moghaddam S. Mukherji A. et al. (2014). MobileMiner: Mining your frequent patterns on your phone. In Joint conference on pervasive and ubiquitous computing (UbiComp’14) (pp. 389-400). Seattle: ACM Press.

  • Stocchetti M. (2018). Invisibility inequality and the dialectics of the real in the digital age. Interaçoes 34: 23-46.

  • Uricchio W. (2017). Data culture and the ambivalence of algorithms. In M. T. Schäfer & K. Van Es (eds.) The datafied society: Studying culture through data (pp. 125-137). Amsterdam Amsterdam University Press

  • Wagner D. T. Rice A. & Beresford A. R. (2013). Device analyzer: Understanding smartphone usage. In International conference on mobile and ubiquitous systems (pp. 1-12). Tokyo: Springer

  • Xu R. Frey R. M. Fleisch E. & Ilic A. (2016). Understanding the impact of personality traits on mobile app adoption – Insights from a large-scale field study. Computers in Human Behavior 62: 244-256.

  • Yan T. Chu D. Ganesan D. Kansal A. & Liu J. (2012). Fast app launching for mobile devices using predictive user context. In Mobile systems applications and services (MobiSys’12) (pp. 113-126). Low Wood Bay: ACM Press

Search
Journal information
Impact Factor


CiteScore 2018: 0.54

SCImago Journal Rank (SJR) 2018: 0.223
Source Normalized Impact per Paper (SNIP) 2018: 0.270


Metrics
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 1006 1006 26
PDF Downloads 207 207 23