Sometimes I go for a walk,
Not knowing my where I want to go,
Only that I would finally end up where I started,
Perhaps, with some answers to my questions.

I walk between the unmoving homes,
Planted trees with their fallen leaves,
The occasional flicker of red and blue on my shadow,
Thinking about words for these poems.

I walk near the cars passing by,
Paved roads built on sand and toil,
The dim yellow and streaking lights,
Thinking about my place in the sky.

I walk amidst the music playing,
Millions of footsteps seem to accompany me,
The silence emboldened by white noise,
Thinking about words I never got around to saying.

Sometimes I go for a walk,
Not knowing my where I want to go,
Only that I would finally end up where I started,
With more questions than answers.

There are two types of people in the world, you and me,
A man whose actions reek of reckless disregard,
And a man whose patience for the world faded recently,
Facing off, one on one, no need for a guard.

Past is the time for discourse and debate,
You always were a man of action, never missing your shot,
Time to put that to the test, for no longer will I wait,
Today, now, we reach a resolution to this plot.

Boom – the bullet stuttered as it pierced you below the chest,
The only thought held in my head that moment: my daughter,
My life unraveled by my ambition for conquest,
You set me up to lose, and I followed like a lamb to the slaughter.

My legacy in shambles, my career now undone,
And historians still believe, simply being alive meant I won.

Lily pads lifted from the soil,
Dirt submerged in the skies above,
Stars grounded in the streams that flow,
Instincts moulded by a crumbling shadow,
A chaotic chorus of discordant screams and shattered dreams.

Wars waged, tides turn in and out,
Buildings collapse, jungle vines wind throughout,
Sulfurous fires burn, an eruption of clouds,
Dust settles, islands appear in a misty shroud,
Dawn to dusk, day after day,
A universal cycle forever repeating.

Amidst the unknown we search for answers,
Amidst all colour we find snowy space,
Amidst the void we create our own darknesses,
Amidst the world we strive to leave a trace
Amidst nothing, do we find ourself.

I stare into the mirror, 
A pair of eyes stare back, 
But behind these orbs, 
A reservoir of thought transpires, 
A coursing stream ever flowing with dreams and desires,
Carrying the potential to soar far beyond the azure,
Proclaiming our primate ancestors far inferior.  

For in a world, 
With competition raging between faceless entities in structures standing tall,   
Vying for the prized possession of all, 
The ever-sought after, prism of attention,  
Subterfuge, the means for retention,  
Targeted strikes and whirring machine guns, 
Riddling the battleground our mind has become,   
Shattering its capacity for focus, 
Into myriads of shards surrounding us, 
Fractions, reconstructed only to produce, 
A kaleidoscope with a distorted view, 
Dispersing the laser focus, now so few,  
Sowing the seeds of dysfunction and unrest. 
The sole casualty - individual awareness.

For in a world,
Where headlines govern our view on the protean landscape,
Celebrities unduly portrayed as heroes in capes,
Illusory support borne by validations discrete,
Constrict the temples of profound conceit.
Consequently, creating a fissure that broadens inchmeal,
Throngs perfervidly clinging to their dogmatic ideals,
Fabricating a community of concurrence,
Obliviously treading towards the void under false pretence,
This eternal abyss permeating with grief,
The sole casualty - individual belief.

 For in a world,
 Where the wastage of a second,   
 Perceived as a display of laxity, 
 Multi-tasking, a prerequisite of the bustling megacity,
 Silence, the apparent lack of productivity,
 Isolation with our own thoughts, an impossibility.
 A thousand clashing voices engaged in a yelling contest,
 This wondrous instrument caught in the midst, 
 Bombarded with disharmonious noises,
 Chaotic melodies coupled with innumerable choices, 
 With the characteristic voice drowned out and dire,
 Fading into the backdrop of this discordant choir.
 The individual's sense of consciousness and functionality,
 Sacrificed for a dysfunctional herd mentality.
 The sole casualty - individual liberty.

 For in a world,
 Devoid of independent beliefs, freedom and attention,
 What is the remnant of such a figure, 
 But the shell of their true character.
 Their mind a tabula rasa initially,
 Now, a loose-leaf blotted with cacography,
 A farrago of furore,
 Incapable of tolerant views evermore. 

 I stare into the mirror,
 A pair of eyes stare back,
 But behind these orbs,
 A cascade of ideas no more,
 Just the drought-ridden river bed,
 A wellspring of nightmares,
 Dreams now buried in the dirt,
 No different from the tribes that roamed the Earth.  

In this essay, I will systematically quantify the extent to which Big Data is a danger for democracy by establishing a comprehensive metric that lays out the cornerstones of modern democracy, and then further elaborate on the undue damage of Big Data in each of these spheres, and the overall impact engendered by employing the variety of applications of Big Data. A sine qua non of obtaining the extent to which Big Data is dangerous for democracy is a standardized metric laying down the principles of democracy in the modern era and those influenced by Big Data, and a characteristic based definition of Big Data.

A specific definition of Big Data goes against the vast, ever-expanding nature of Big Data, however, “it does depend on the size and heterogeneity of the data, and the scale and scope of analytic operations made possible by that size.” (American Association for the Advancement of Science, 2014) The primary characteristics of Big Data can be encapsulated by the 3 ‘V’s, namely Volume, Velocity, Variety (Laney, 2001). Big data represents the information assets characterized by such a high volume, velocity and variety to require specific technology and analytical methods for its transformation into value (De Mauro, et al., 2016). At its core, Big Data proves to refer to data sets characterized by huge amounts (volume) of continually updated data (velocity) in various formats which may include images, videos, textual or numeric data (variety) that can be analysed with the help of complex algorithms. In recent times, seemingly to add to the catchy 3 Vs, terms like veracity and variability have been thrown into the mix, referring to the consequence of dealing with data on such a large scale – incomplete, imperfect or error-prone datasets with unstandardized data. While this would greatly play into how Big Data erodes the foundation of democracy, it is not a defining attribute of Big Data but rather relates more to data uses. (Grimes, 2013) The streams of data obtained from the users of social media websites and mobile applications like Facebook or daily crime statistics of a metropolitan city can be easily classified as Big Data under this definition seeing that there is a constant influx of vast amounts of data pouring in, in various forms whether that is the zip code of place where a mugging took place or a geotagged photo that a person posted in addition to the readily available zettabytes of public or state records. The value of all this Big Data however, lie in the outputs that come because of the mathematical models and algorithms employed to crunch through all the data supplied.

Relative to Big Data, democracy is something far more deep-rooted in history, originating first in Athens in 430 B.C. and since then as the state has evolved, so has democracy. Therefore, to have a definition that encompasses the essence of democracy, the intrinsic traits that constitute the various forms of democracy must be established. By subsequently considering the impact of Big Data on each of these attributes, we can approach a conclusion that quantifies the danger for democracy that Big Data invites and its potential impact in the future.

The starting point of any democracy is ‘free and fair elections’. These four words imply that the authority of the government can only be derived from the will of the people as determined by credible elections held at periodic intervals based on universal, equal and secret suffrage. Firstly, Big Data shifts politics from the Overton Window where it ought to be, to a private affair, giving candidates the opportunity to engage in duplicity on a far broader scale and secondly, Big Data narrows down an election from the entire voting population of the country to merely a section of “persuadable voters”, poisoning the democratic process along the way.

Big Data collected from social media applications serve as fuel for microtargeted advertisement. Elections should be a crucial part of public discourse. Since the days of Aaron Burr, when open campaigning was introduced, the formula for marketing a candidate to the masses remained consistent – rallies, party manifestos, town halls, and billboards and later, televised debates and television advertisements. To an extent these methods were enacted as an argumentum ad captandum, however, simultaneously through these methods, the general policy and program of the candidate became subject to public discussion and scrutiny by the media, thus involving citizens in the decision-making process.

However, with the advent of social media applications and the influx of data accompanying it, political strategy has shifted from trying to appeal to everyone, to targeting the group of undecided persuadable voters and efficiently using the campaign budget for a greater payoff. Within this group of voters exist a sea of political beliefs, maybe some who are environmentalists or proponents of the gun ownership or some other specific belief. On algorithmically deducing these core beliefs of each individual persuadable voter, microtargeted advertisements can be delivered to them, presenting the candidate in the best possible light to receive that individual’s vote. In a single sentence, it’s about finding the one thing a person cares about, and then pushing a message that validates that belief. The 2012 Obama campaign was notable in this regard, as it marked the entry of the data science into politics, largely following the steps outlined in the above lines, which would later be honed by the 2016 Trump campaign (Bartlett, 2018).

The consequences of this type of hyper-personalized marketing spill over into the next principle of democracy, namely accountability. Assuming this type of data-driven political demagoguery is left unchecked, it could be plausible that every individual receives a different campaign pledge and advertisement from the same candidate. How does one go about holding a politician responsible and regulating such a system, when no two people receive the same advertisements? Unlike billboards and televised debates, which are the same whether viewed by an environmentalist or a pro-gun rights supporter, the specifically curated content that is being sent to that particular individual is not fact-checked and discussed by the media, and consequently politics gradually drains out of public conversation. Politicians no longer become accountable to entire public, but to the segment of the population that supports them.

Though the most prominent characteristic of a democracy is the right to franchise, democracy extends beyond this to entitle each citizen to participate in the civil and political life of society without facing any form of discrimination based on an individual’s personal characteristics. Only following the Civil Rights Movement in the US did legislation preventing the discrimination of citizens based on race, ethnicity and gender get introduced. Granted their effectiveness was limited as the law translated into reality inchmeal, over time, more comprehensive safeguards spread across a larger range of public settings of employment, education and access to public services. Big Data inadvertently opens a gateway to a path that seeks to undo all this, allowing architects of models that heavily rely on Big Data to camouflage their biases into “impartial” models that function like black boxes and sneak around the existing civil rights established by using unfairly weighted proxies.

Every mathematical model attempts to ascertain a significant output by determining the relation of the factors culminating in that result. However, when trying to compute the quantity of an intangible concept, possibly, how responsible a driver is or how “good” a potential employee could be, there are no straightforward numbers that point directly at the answer. By their very nature, models feed on data that can be measured and counted. Therefore, proxies for these incorporeal terms like responsibility or goodness are implemented. The room for error and by extension, a form of discrimination, enters at this age. In the same way a model trained using historical data pulled from times, where the preponderance of institutionalized racism was unquestionable, is racist, a model in which the relevance of factors is decided completely by the modeler, will inherit the biases of the modeler. Multiple models in different sectors like insurance, education, human resources and banking indirectly function collectively to form a pernicious feedback loop that punishes the poor and rewards the rich in what leads to a greater divide.

Take the example of one of the world’s largest democracies, the United States of America. Partially resulting from the institutionalized racism that haunts America’s history, the socio-economic condition of African-Americans today is dismal. White families today have nearly 10 times the net worth of black families and more than eight times that of Hispanic families (Dettling, et al., 2017). In this circulus vitiosus, unemployment, credit scores, insurance, education, poverty and crime are all interconnected. For instance, lack of decent schools could mean inability to get a job, possibly leading to debt and a lower credit score, increasing the poverty rate of that neighbourhood and creating an impetus for crime which in turn would reduce the quality of education for future generations. In this way, these models increase the correlation between race and neighbourhoods with worse socio-economic conditions. Thus, a seemingly unbiased factor while determining insurance like a zip code, becomes a stand-in for race. The use of such proxies become a method of sidestepping around the civil rights legislation established. Further, the results from an inherently flawed model like so cannot be questioned or regulated since majority of those affected are unaware of its internal workings. The outcome is that racial discrimination persists in subtler unregulatable ways, technically considered within the parameters of the law.

With the existence of rights, a system to enforce them and ensure that they are not infringed upon by the government or any other individual is necessary, and thus, rule of law finds itself as the fourth principle of democracy. 2000 words cannot capture the whole essence of what this phrase means, much less a paragraph, hence I will focus on the characteristics of rule of law pertinent to Big Data. One of the salient characteristics of rule of law is that it stresses that everyone is equal in the eyes of the law, however, Big Data has perverted this aspect.

In twenty-four state prisons in the US (O’Neil, 2018), a Level of Service Inventory-Revised (LSI-R) model is implemented, part of which prisoners have to fill in a questionnaire including questions around the circumstances of a criminal’s birth and upbringing, crime rate in the neighbourhood which contribute towards calculating the risk of recidivism. These risk scores have been used by judges in some states like Idaho and Colorado to guide their sentencing (National Centre for State Courts, 2013). These risk scores are unfairly calculated based on spurious correlations that have no bearing in an individual’s case. Simply because of high crime rate in the neighbourhood, sentencing a criminal for longer is the antithesis of a fairness. Similar to how the earlier models sidestepped civil rights using proxies, the LSI-R model unquestionably brings racial bias into the sphere of justice, contradicting the rule of law. Justice however, even in the age of technology has largely remained in the hands of humans given its nuanced nature and seeing that fairness is not quantifiable to a model.

Through this essay, I have attempted to outline the manner in which Big Data has ticked away at the very fundamental principles of democracy, namely – free and fair elections, accountability, civil rights and rule of law. Big Data undermines the votes of all the citizens by serving as ammunition for micro-targeted advertising that limits an election to a mere segment of the population (i.e. the “persuadable voters”), it shifts politics from a subject of public discourse to a private affair based on a game of numbers and data analytics and by use of proxies empowers modelers to sidestep civil rights laws. The danger Big Data poses to democracy lies in the fact that it fabricates a pernicious feedback loop that increases inequality due to inherent prejudices and taints the democratic process in such a discrete and unregulatable manner that the quality of democracy is gradually eroded.

References:

American Association for the Advancement of Science in conjunction with the FBI and UNICRI, 2014. National and Transnational Security Implications of Big Data in the Life Sciences. [Online]                                

Available at : http://www.aaas.org/sites/default/files/AAAS-FBI-UNICRI_Big_Data_Report_111014.pdf  [Accessed July 27, 2019].        

Laney, Doug, 2001. 3D Data Management: Controlling Data Volume, Velocity and Variety. [Online]

Available at : https://blogs.gartner.com/doug-laney/files/2012/01/ad949-3D-Data-Management-Controlling-Data-Volume-Velocity-and-Variety.pdf  [Accessed July 27, 2019].

De Mauro, Andrea; Greco, Marco; Grimaldi, Michele, 2016. “A Formal definition of Big Data based on its essential Features.” Library Review. 65 (3): 122–135

Grimes, Seth, 2013. “Big Data: Avoid ‘Wanna V’ Confusion.” InformationWeek. [Online]                           

Available at : www.informationweek.com/big-data/big-data-analytics/big-data-avoid-wanna-v-confusion/d/d-id/1111077  [Accessed July 27, 2019].

Bartlett, Jamie, 2018. The People vs Tech: How the Internet is Killing Democracy (and How We Save It) Ebury Press.

Dettling, Lisa J, et al., 2017. “Recent Trends in Wealth-Holding by Race and Ethnicity:                                                             Evidence from the Survey of Consumer Finances.” Federal Reserve [Online]                                                     

Available at : www.federalreserve.gov/econres/notes/feds-notes/recent-trends-in-wealth-holding-by-race-and-ethnicity-evidence-from-the-survey-of-consumer-finances-20170927.htm.  [Accessed July 30, 2019].

O’Neil, Cathy, 2018. Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy Penguin Books, pp. 222-223

Centre for Sentencing Initiatives, Research Division, National Centre for State Courts, 2013. “Use of Risk and Needs Assessment Information at Sentencing: 7th Judicial District, Idaho” [Online]                                

Available at : https://www.ncsc.org/~/media/Microsites/Files/CSI/RNA%20Brief%20-%207th%20Judicial%20District%20ID%20csi.ashx [Accessed July 30, 2019].