Big DATA and AI in Healthcare

BIG DATA Critical ESSAY (20.000 characters including spaces + Single space + any font 11) –

Big Data and AI have revolutionized the health business. Discuss this statement with reference to both opportunities and challenges.

***This examination will ask you to critically engage with a specific question or topic of the course. To achieve excellence the students will need to apply the theoretical insights learned from the literature to the analysis of specific empirical examples. Students are expected to read/use between 4 to 8 articles on their specific topic to reference in the essay (Please see below) – (Please note that the mark is not depending on how many texts you read but how well you critically engage with texts in your essay) – the teacher.

Some info on possible structure (Notes from professor)

There are students that focus on one specific example (theory, practice, theory practice, …), but there are other students that do a classical approach. Start with theory, go to analysis of a case study. Both are good. The more you narrow it down, the better the mark.

Its important to show that the theory is understood, and then do critical engagements (compare theories seen in class – see the resources below), and also do Analytical depth (take in new and own examples, build your idea).

Structure Maybe?:

Introduction: a bit of Analytical depth maybe? – (in this essay I will focus on blablabla… own idea, start big, narrow it down) tell what I want and what I am doing. Not explaining things, will do that in the main body.

Main body: different subparts. One about X (Maybe opportunities?), one about Y (Maybe Challenges?, … Each paragraph should contain one idea à Statement sentence, then support with theory or with evidence and critically discuss it. Then next paragraph. “Best essays are the one she can read when she only reads the first sentence of each paragraph”- the teacher

Conclusion: Summarise, tell again my answer. Which perspectives are most useful, and possible future directions (things I didn’t mention in the main body but similar direction offcourse).

Mandatory References to use (All of them 4)

If you cannot access to any of them, let me know I can send you the pdf version.

Barassi, Veronica. 2017. BabyVeillance? Expecting Parents, Online Surveillance and the Cultural Specificity of Pregnancy Apps. Social Media + Society. https://doi.org/10.1177/2056305117707188

Libert, Timothy. 2015. “Privacy Implications of Health Information Seeking on the Web.” Communications of the ACM 58, no. 3: 68–77. https://doi.org/10.1145/2658983 – Please find in the document attached!

CB Insights. 2018. “How Google Plans to Use AI to Reinvent the $3 Trillion US Healthcare Industry.” New York: CB Information Services. https://www.cbinsights.com/research/report/google-strategy-healthcare/.

Grundy, Quinn, Kellia Chiu, Fabian Held, Andrea Continella, Lisa Bero, and Ralph Holz. 2019. “Data Sharing Practices of Medicines Related Apps and the Mobile Ecosystem: Traffic, Content, and Network Analysis.” BMJ 364 (March): l920. https://doi.org/10.1136/bmj.l920

References interesting to use

References to use (Use at least 2 from the list below apart from the previous 4) –

If you cannot access to any of them, let me know I can send you the pdf version.

Moreover, you may also use other external resources (Cite them):

• The teacher really likes Foucault – Reference him if possible…

• Barassi 2019. “Datafied Citizens in the Age of Coerced Digital Participation:” Sociological Research Online, June. https://doi.org/10.1177/1360780419857734 (Links to an external site.).
• Barassi Veronica 2018. “Home Life Data and Children’s Privacy.” Call for Evidence Submission Information Commissioner’s Office. London UK: Goldsmiths University of London. http://childdatacitizen.com/cdc/wp-content/uploads/2018/09/‘HOME-LIFE-DATA’-AND-CHILDREN’S-PRIVACY-1.pdf. (Links to an external site.) 
• Barassi, Veronica, and Patricia Scanlon. 2019. “Voice Prints and Children’s Rights.” London: Goldsmiths University of London. http://childdatacitizen.com/voice-prints-childrens-rights/ (Links to an external site.) 
• Barocas, Solon, and Andrew D. Selbst. 2016. “Big Data’s Disparate Impact.” SSRN Scholarly Paper ID 2477899. Rochester, NY: Social Science Research Network. https://papers.ssrn.com/abstract=2477899 (Links to an external site.).
• Beer, D. (2016). How should we do the history of Big Data? Big Data & Society. https://doi.org/10.1177/2053951716646135 (Links to an external site.)
• Bory, Paolo. 2019. “Deep New: The Shifting Narratives of Artificial intelligence from Deep Blue to AlphaGo.” Convergence, February, 1354856519829679. https://doi.org/10.1177/1354856519829679 (Links to an external site.).
• boyd, danah, and Kate Crawford. 2012. “Critical Questions for Big Data.” Information, Communication & Society 15 (5): 662–79. https://doi.org/10.1080/1369118X.2012.678878 (Links to an external site.).
• Bradbury, Alice, and Guy Roberts-Holmes. 2016. “‘They Are Children…Not Robots, Not Machines’. The Introduction of Reception Baseline Assessment.” London: UCL Institute of Education. http://www.betterwithoutbaseline.org.uk/uploads/2/0/3/8/20381265/baseline_assessment_2.2.16-_10404.pdf (Links to an external site.).
• Broussard, Meredith. 2018. Artificial Unintelligence: How Computers Misunderstand the World. Cambridge, Massachusetts: The MIT Press. (Introduction) https://ebookcentral.proquest.com/lib/ubstgallen-ebooks/reader.action?docID=5355856. (Links to an external site.) 
• Eisenstat, Yael. 2019. “The Real Reason Tech Struggles With Algorithmic Bias.” Wired, February 12, 2019. https://www.wired.com/story/the-real-reason-tech-struggles-with-algorithmic-bias/ (Links to an external site.).
• Elmer, Greg. 2004. Profiling Machines: Mapping the Personal Information Economy. Cambridge, Mass: The MIT Press. (Introduction or chp 1). https://ebookcentral.proquest.com/lib/ubstgallen-ebooks/reader.action?docID=3339786 (Links to an external site.).
• Friedman, Batya, and Helen Nissenbaum. 1996. “Bias in Computer Systems.” ACM Trans. Inf. Syst. 14 (3): 330–347. https://doi.org/10.1145/230538.23056 (Links to an external site.).
• Gangadharan, Seeta P. (2012). Digital inclusion and data profiling. First Monday, 17(5). https://firstmonday.org/article/view/3821/3199. (Links to an external site.) 
• Gates, Kate (2011). Our Biometric Future: Facial Recognition Technology and the Culture of Surveillance. NYU Press. (Chp 2) https://www.degruyter.com/document/doi/10.18574/9780814733035/html (Links to an external site.). 
• Gitelman, Lisa. 2013. “Raw Data” Is an Oxymoron. Cambridge, Massachusetts ; London, England: MIT Press. https://direct.mit.edu/books/book/3992/Raw-Data-Is-an-Oxymoron. (Links to an external site.) 
• Hintz, Arne, Lina Dencik, and Karin Wahl-Jorgensen. 2018 Digital Citizenship in a Datafied Society. Polity Press. https://ebookcentral.proquest.com/lib/ubstgallen-ebooks/reader.action?docID=5613296 (Links to an external site.).
• Kitchin, Rob. 2014. The Data Revolution: Big Data, Open Data, Data Infrastructures and Their Consequences. 1 edition. Los Angeles, California: SAGE Publications Ltd. https://books.google.ch/books/about/The_Data_Revolution.html (Links to an external site.).
• Madden, Mary, Michele Gilman, Karen Levy, and Alice Marwick. 2017. “Privacy, Poverty, and Big Data: A Matrix of Vulnerabilities for Poor Americans.” Washington University Law Review 95 (1): 053–125. https://openscholarship.wustl.edu/cgi/viewcontent.cgi?article=6265&context=law_lawreview (Links to an external site.).
• Solove, Daniel J. 2004.  Download Solove, Daniel J. 2004.The Digital Person: Technology and Privacy in the Information Age. NYU Press. (Introduction)
• Thompson, J. (1995)  Download Thompson, J. (1995)The Media and Modernity. Cambridge: Polity. Chapter 4: The Transformation of Visibility. https://books.google.ch/books/about/The_Media_and_Modernity.html (Links to an external site.).
• Williamson, Ben. 2017.  Download Williamson, Ben. 2017.Big Data in Education: The Digital Future of Learning, Policy and Practice. SAGE (Introduction) 
• Zarkadakis, George. 2015  Download Zarkadakis, George. 2015. In Our Own Image: Will Artificial intelligence Save or Destroy Us? New York, NY: Rider. (Introduction)

Overview of the topic seen in class (support with PPT Slides in the other document)

Health data has become a big business; one of the most profitable ones, and one of the most problematic. The introduction of AI innovation and big data analytics in health can lead to many positive transformations. In 2017 researcher in Netherlands  for instance demonstrated that 7 deep-learning algorithms had  achieved better diagnostic performance of breast cancer than a panel of 11 pathologists participating in a simulation exercise. Despite there are many important benefits of big data and AI in health, when we think about health data, we need to consider how big tech companies are trying to achieve a monopoly over the sector. In this class we will look at the fact that Big tech companies are playing an active role in the datafication of the health sector, by tapping into EHRs, introducing new AIs and data infrastructures, and by cutting deals with health services. At the same time we will also see how these companies are harnessing large quantities of health data through their platforms and services and by buying this health data from others. Hence we will question the implications of building business models and companies, which can monitor the health life of individuals from the moment they are conceived to the moment in which they die.