Selecting Sources

Source Selection

Based on my Plan of Action (previous post), I have decided to create a more specific list of the sources I will need to be using to address my hypothesis and the specific points I will be addressing.

Title of source Details Reasons for choosing and how I will use it
‘Big data in global health: improving health in low- and middle-income countries’. Bulletin of the World Health Organization Wyber, R. Vaillancourt, S. Perry, W. Mannava, P. Folaranmi, T. Anthony Celi, L. 2015. ‘Big data in global health: improving health in low- and middle-income countries’. Bulletin of the World Health Organization, [online] Volume 93(3), p. 133-208. Available at: http://www.who.int/bulletin/volumes/93/3/14-139022/en/ [Accessed 20 March. 2018]. This article looks specifically at how insights from big data can be used to offer personalised healthcare. I would like to explore what the consequences of this could be and whether the benefits outweigh the drawbacks i.e. private ownership of data poses a risk, as does a corrupt government handling a data and this could be used against individuals.
A Popular Algorithm Is No Better at Predicting Crimes Than Random People Yong, E. (2018). A Popular Algorithm Is No Better at Predicting Crimes Than Random People. [online] The Atlantic. Available at: https://www.theatlantic.com/technology/archive/2018/01/equivant-compas-algorithm/550646/ [Accessed 1 Jul. 2018].

 

I found the COMPAS algorithms used to predict recidivism a particularly interesting example of where algorithmic decision-making can have a huge impact on individuals. This is one of the several sources about the topic that I will use within my project as a case study. I have selected multiple sources in order to ensure a balanced viewpoint.
Artificial Intelligence Has a Bias Problem, and It’s Our Fault Dickson, B. (2018). Artificial Intelligence Has a Bias Problem, and It’s Our Fault. [online] PCMag UK. Available at: http://uk.pcmag.com/netflix/96336/feature/artificial-intelligence-has-a-bias-problem-and-its-our-fault [Accessed 1 Jul. 2018]. This explores the origins of algorithmic bias and crucially, how deep-learning does not equate to neutrality. I would like to include this because it explored the more technical aspect of why problems arise within the decision-making. This helped me get to grips with understanding why inequalities occur.
‘Automating Inequality’: Algorithms In Public Services Often Fail The Most Vulnerable Banks, E. (2018). ‘Automating Inequality’: Algorithms In Public Services Often Fail The Most Vulnerable. In: Edes, A. Bowman, E. 2018. ‘Automating Inequality’: Algorithms In Public Services Often Fail The Most Vulnerable. National Public Radio.

 

 

 

 

 

 

 

 

This podcast crucially highlights how essential it is that the programs must be free of human biases, otherwise all we are doing is automating our biases rather than eliminating them. This source specifically addressed the use of algorithmic decision-making within government and the real effects this has had. I would like to use this in conjunction with my research about current regulation and what is technically feasible to discuss how the present system whether the current system needs improvement and if so, how it can be improved.
Bias detectives: the researchers striving to make algorithms fair Courtland, R. (2018). Bias detectives: the researchers striving to make algorithms fair. [online] Nature. Available at: https://www.nature.com/articles/d41586-018-05469-3 [Accessed 1 Jul. 2018]. Most of the sources I read discussed ethics and case studies, while this really delves into why the bias that occurs happens from a technical perspective. Though the focus of my essay is not technical, I feel it is important to explain and justify the way these algorithms work in order to come to a conclusion about whether their use in society is inherently bad.
Big Data and Human Resources—Letting the Computer Decide? | Socially Aware Blog McLean, S., Stakim, C., Timner, H. and Lyon, C. (2015). Big Data and Human Resources—Letting the Computer Decide? | Socially Aware Blog. [online] Socially Aware Blog. Available at: https://www.sociallyawareblog.com/2015/04/03/big-data-and-human-resources-letting-the-computer-decide/ [Accessed 29 Jun. 2018]. One use of big data that has become very integrated into our society today is in HR. I want to look at whether this can be discriminating against specific groups and use this to inform my final judgement.
Big Data Helps UK National Health Service Lower Costs, Improve Treatments Forbes. (2018). Big Data Helps UK National Health Service Lower Costs, Improve Treatments. [online] OracleVoice. Available at: https://www.forbes.com/sites/oracle/2018/02/07/big-data-helps-uk-national-health-service-lower-costs-improve-treatments/#6527fb0758e3 [Accessed 27 Jun. 2018]. A very recent article detailing how insights from data analytics has specifically benefited the UK and the NHS along with the estimated saving it has enabled. I will use this as an example of how data analytics can be used to overcome health issues and how this would mean my hypothesis is invalid
Facebook and Mr Zuckerberg go to Congress: Podcast 364 Wired (2018). http://www.wired.co.uk/article/podcast-364. Facebook and Mr Zuckerberg go to Congress: Podcast 364

 

This provides yet another take on the story, but what I found especially interesting was hearing the evaluative points raised by the presenters about the significance of the effect Cambridge Analytica actually had and how widespread its impact really was.
Facebook has gotten so big that no one can understand it, and it could be a good reason to break it up Kovach, S. (2018). Facebook has gotten so big that no one can understand it, and it could be a good reason to break it up. [online] Business Insider. Available at: http://uk.businessinsider.com/is-facebook-a-monopoly-2018-4 [Accessed 1 Jul. 2018].

 

Thought this is not talking about the repercussions of big data and decision-making tools, it highlights an issue that could aggravate their effects. I would like to explore whether or not this is the case and how the issue of tech monopolies can be overcome.
Google and Facebook grab ad growth WARC. (2017). Google and Facebook grab ad growth | WARC. [online] Available at: https://www.warc.com/newsandopinion/news/google_and_facebook_grab_ad_growth/38599 [Accessed 28 Jun. 2018]. This is a useful chart showing the proportion of revenue that Google and Facebook generate which helps get an idea of their size in the market. I will use this when discussing what this means for information asymmetry and we as consumers.
How Big Data and AI are Driving Business Innovation. Big Data Executive Survey 2018 New Vantage (2018). How Big Data and AI are Driving Business Innovation. Big Data Executive Survey 2018. [online] Available at: http://newvantage.com/wp-content/uploads/2018/01/Big-Data-Executive-Survey-2018-Findings.pdf [Accessed 1 Jul. 2018].

 

ways big data combined with algorithmic decision-making has benefitted our society to be able to make a judgement about whether, on the whole, it has done more good than bad.

. showed me the different ways in which data can be harnessed to benefit society.

How Cambridge Analytica’s Facebook targeting model really worked Hindman, M. (2018). How Cambridge Analytica’s Facebook targeting model really worked. [online] The Independent. Available at: https://www.independent.co.uk/life-style/gadgets-and-tech/how-cambridge-analytica-s-facebook-targeting-model-really-worked-according-to-the-person-who-built-a8289901.html [Accessed 1 Jul. 2018]. This article provided a unique take on the Cambridge Analytica story, from the creator of the app. This had technical details about how much information about you is required to be able to make inferences about you, which I think would make for interesting discussion linking to information asymmetry and how this could disadvantage certain groups more than others.
How Can Big Data Technology Help Fight Poverty? Wang, C. (2018). How Can Big Data Technology Help Fight Poverty?. [online] Borgen Magazine. Available at: http://www.borgenmagazine.com/big-data-technology-poverty/ [Accessed 1 Jul. 2018]. To ensure a balanced, sustained discussion, I would like to discuss some of the ways in which this technology has the potential (or has been) harnessed to tackled social problems in order to then evaluate whether the benefits outweigh the drawbacks.
How payday lenders profit from our psychological vulnerabilities Crockett, M. (2013). How payday lenders profit from our psychological vulnerabilities. [online] The Guardian. Available at: https://www.theguardian.com/science/head-quarters/2013/sep/03/payday-lenders-psychological-vulnerabilities-wonga [Accessed 1 Jul. 2018]. I will include this as it provides a more psychological perspective as to how and why financial lenders prey upon our vulnerabilities which may not otherwise be explicit.
IBM uses big data to predict outbreaks of dengue fever and malaria Takahashi, D. (2013). IBM uses big data to predict outbreaks of dengue fever and malaria. [online] VentureBeat. Available at: https://venturebeat.com/2013/09/29/ibm-uses-big-data-to-predict-outbreaks-of-dengue-fever-and-malaria/ [Accessed 1 Jul. 2018]. This will be useful for arguing against my hypothesis, as it talks about a very useful instance in which IBM used big data tools to predict outbreaks of dengue and malaria and real-time processing to save lives.
Infographic: 25 Percent of Global Ad Spend Goes to Google or Facebook Statista. (2018). Infographic: 25 Percent of Global Ad Spend Goes to Google or Facebook. [online] Available at: https://www.statista.com/chart/12179/google-and-facebook-share-of-ad-revenue/ [Accessed 28 Jun. 2018]. A useful chart illustrating what proportion of the market is owned by different tech companies. I will use this to support my discussion about tech giants and how this links to information asymmetry
Is ‘Big Data’ Actually Reinforcing Social Inequalities? Chen, M. (2014). Is ‘Big Data’ Actually Reinforcing Social Inequalities?. [online] The Nation. Available at: https://www.thenation.com/article/big-data-actually-reinforcing-social-inequalities/ [Accessed 29 Jun. 2018]. This highlights how although algorithmic decision-making is seen to be objective, it in fact ‘can reinforce and mask prejudice’
Machine Bias Angwin, J., Larson, J., Mattu, S. and Kirchner, L. (2016). Machine Bias. [online] ProPublica. Available at: https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing [Accessed 1 Jul. 2018]. This was the original study carried out into COMPAS, which provides one of the several views on the algorithm I would like to discuss in terms of what is fair and what is technically feasible.
My Facebook Was Breached by Cambridge Analytica. Was Yours? Meyer, R. (2018). My Facebook Was Breached by Cambridge Analytica. Was Yours?. [online] The Atlantic. Available at: https://www.theatlantic.com/technology/archive/2018/04/facebook-cambridge-analytica-victims/557648/ [Accessed 1 Jul. 2018].

 

I will discuss various viewpoints on the story to be able to make a thorough and reasoned judgement in terms of whether a. the software did what it claimed to do b. this falls in line with my hypothesis.
Privacy, Poverty, and Big Data: A Matrix of Vulnerabilities for Poor Americans Mary Madden, Michele Gilman, Karen Levy, and Alice Marwick, Privacy, Poverty, and Big Data: A Matrix of Vulnerabilities for Poor Americans, 95 Wash. U. L. Rev. 053 (2017).

 

This was a very comprehensive study into what the impact of the digital era is on those from poorer backgrounds. I came across some truly shocking statistics which I wish to include and discuss the reasons for them in my project.
The Evolution Of Data To Life-critical Data Age 2025: The Evolution Of Data To Life-critical. https://www.seagate.com/files/www-content/our-story/trends/files/seagate-wp-dataage2025-march-2017.pdf Date [Accessed 17 Mar. 2018]. There were lots of statistics included in this research, in the form of charts and diagrams which I found very useful visualisations of what is going on and would benefit my discussion.
The filter bubble: what the Internet is hiding from you. Pariser, E. (2011). The filter bubble: what the Internet is hiding from you. London, Viking/Penguin Press.

 

Pariser discusses the rise of personalised content and how it leads to individuals being exposed to very different views of the world which is an interesting concept. I would like to look at his views on personalization.in terms of whether it perpetuates inequality, specifically discussing information asymmetry and which groups are more adversely affected by this.
The shady data-gathering tactics used by Cambridge Analytica were an open secret to online marketers. I know, because I was one Samuel, A. (2018). The shady data-gathering tactics used by Cambridge Analytica were an open secret to online marketers. I know, because I was one. [online] The Verge. Available at: https://www.theverge.com/2018/3/25/17161726/facebook-cambridge-analytica-data-online-marketers [Accessed 13 Apr. 2018].

 

This adds a different perspective on the Cambridge Analytica scandal, pointing out the fact that these techniques have been used for years now, but the fact that this can occur online is what seems strange to us. I want to explore this in relation to information asymmetry while considering how effective it really was.
The tech titans must have their monopoly broken – and this is how we do it Cable, V. (2018). The tech titans must have their monopoly broken – and this is how we do it | Vince Cable. [online] The Guardian. Available at: https://www.theguardian.com/commentisfree/2018/apr/20/tech-monopoly-apple-facebook-data-extreme-content [Accessed 1 Jul. 2018]. While addressing the hypothesis, I want to look at whether certain issues within the tech market make the problem worse. For example the monopolistic nature of Facebook gives them as an organisation more power over our data. I want to look at the effects of this on different groups.
The World Wide Web: Past, Present and Future Berners-Lee, T. (1996). The World Wide Web: Past, Present and Future. [online] W3. Available at: https://www.w3.org/People/Berners-Lee/1996/ppf.html [Accessed 1 Jul. 2018]. Written by the creator of the world wide web himself, I think it would be interesting to compare the reasons for the foundation of the web to begin with to what it has become now and whether it has changed for the better or the worse.
UK police are using AI to inform custodial decisions – but it could be discriminating against the poor Burgess, M. (2018). UK police are using AI to inform custodial decisions – but it could be discriminating against the poor. [online] Wired.co.uk. Available at: http://www.wired.co.uk/article/police-ai-uk-durham-hart-checkpoint-algorithm-edit [Accessed 10 Apr. 2018]. This is a very recent way in which the UK police force has begun to use big data in their operations so I thought this would provide an interesting example that is closer-to-home. It also is an important example of how systems built without bias can go on to exhibit bias which will make for an interesting point of discussion within my project.
UK subprime lenders retreat from payday loans | Financial Times Smith, I. (2017). UK subprime lenders retreat from payday loans | Financial Times. [online] Financial Times. Available at: https://www.ft.com/content/4da29534-f9e1-11e6-bd4e-68d53499ed71 [Accessed 1 Jul. 2018].

 

This article addresses what has changed in the market for payday loans since the financial crisis of 2007/8. I would like to take into account and discuss the regulation that is in place to protect consumers against exploitative practices and whether it is effective.

 

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