We can begin using the large amounts of data we are storing and will begin to capture in the coming years to measure the effects of many of life decisions. We can then measure our decisions and define morality on the effects of the decisions we make. #BigDataMorality 

@dfuhriman is CEO of Bern Medical, a startup providing business analytic services to healthcare.

This device from MC10 represents the future of capturing and monitoring more data than ever before.
From 1956. Has enough memory for two songs or one photo.
The human population of the earth is approaching 7 billion, which means we collectively experience 7 billion years worth of human history annually. In 2010 then Google CEO Eric Schmidt claimed that more data is created every two days than had previously been from the dawn of man until 2003.[1] Looking forward, a recent Digital Universe study, sponsored by EMC claims that by 2020- 40 zettabytes of data will exist in the world.[2] Perhaps only 3 people in the world know what a zettabyte is, but if each byte was a grain of sand, it means that by 2020 we will have more bytes than grains of sand on every beach on every continent in the world, multiplied by 57.

With so much data being generated and collected about our shared human experience, we now know more about life than we have ever known before. The name “Big Data” has been attributed to the vast amounts of digital data that we can easily collect, store and analyze. We are using the data and computing power to save lives, live longer, make businesses more efficient, conserve earth’s natural resources, and of course make better movie recommendations on Netflix. We need to start using “Big Data” to understand and define morality.

Morality is the differentiation between right and wrong. With Big Data, we can now understand better the effects of decisions. We can determine that which is right based on the outcome. If the outcomes are good, then the actions, intentions and decisions are right. If the outcome is poor, then the actions, intentions and decisions are wrong.

Historically understanding morality has meant turning to two main fountains of knowledge. One is religious morality, which centers on interpretation of religious text. The second is secular morality, which derives its morality from logic, reason and intuition. Both of these types of morality have relied on our own observations to understand the world and identify truth. However, a fragmentary sampling of man’s experience and imperfect recollection weakens our knowledge. For further perspective, we turn to written records, both religious and secular, that give us an insight into the past, predicts the future and show us the experiences of those that have come before us. But, that still leaves us to interpret and apply them differently.

Big Data allows us to understand so much more of the human experience. We can capture actions and their effects on the individual and society. We can begin to leverage the annual stories of 7 billion humans on earth and understand our interconnected relationships. We begin to experience time differently. We can see effects of decisions days, years, and decades later- so we can really get a correct measurement of the results. This is what I call: Big Data Morality. The premise is simple; we should hold as moral those things that improve society and we should consider those things immoral, which damage or harm society.

Big Data Morality defines that which improves society by borrowing from the unalienable rights as defined in the United States Declaration of Independence.

We hold these truths to be self-evident, that all men are created equal, that they are endowed by their Creator with certain unalienable Rights, that among these are Life, Liberty and the pursuit of Happiness.

Moral behavior protects for the individual and the whole of society unalienable rights. Immoral behavior harms the individual, others, and society. The effects of our choices are truths but are not self-evident, thus the confusion in historical methods of defining morality.

We are not yet capturing all the data we need, but soon we will track more data about or bodies, our environment, our economy and our decisions. In development now are electronics that monitor even our biological processes. These devices will generate a treasure trove of information. Stretchable electronics, like those being developed by MC10 can be attached to the skin and measure heart rate, brain activity, body temperature, and hydration levels.

We can combine these new sources with data that we are already know and have used for decades, such as age, education level, geography and other demographic factors. Huge amounts of data are captured in silos of corporate databases, such as cell phone location, internet browsing history, credit card purchases, banking transactions, college transcripts, medical records and other unstructured data, such as emails, text messages, internet search history, and images. The government owns a lot of data too, which is currently at various stages of openness: arrest and incarceration records, healthcare payments, weather, census, budgets for law enforcement offices, and tax returns.

Pornography is one issue where we have a lot of data already and could make significant progress in understanding its effects. Every online viewing episode is captured in a database. If we could aggregate the data across users, usage trends would emerge such as time between episodes, duration of each episode, content viewed and how the type of content changes over time. We could add in other data sources such as demographics, medical records, arrest records, and education transcripts to discover how usage trends of pornography correlate with other behaviors. If we had access to millions of users’ biological processes as captured through a product from MC10, we could understand more deeply the physiological effects of pornography and discover if it changes anything about us. Armed with this rich dataset we create and test a hypothesis to the causation. Does it help relieve sexual anxiety and make us better members of society? Or does it change something about us that makes us worse? Once we measure the effects on society we will have a clear moral agreement.

Sometimes we see the effects in the data, but don’t have a clear understanding yet of the causes. Between $40-$60 billion will be spent annually treating preventable obesity related diseases in the US by 2030.[3] Obesity is the leading cause of preventable death and the immediate causes of obesity are usually excessive food intake and lack of physical activity.[4] But, is there something that is causing pathological eating? A Brookhaven National Laboratory Study measured dopamine levels in obese individuals and discovered their brains produced less dopamine.[5] Dopamine is a drug the brain produces that is responsible for reward-driven learning, highly addictive drugs, such as cocaine, act directly on the dopamine system.[6] The conclusion was that obese individuals consume more food to compensate. However, what is causing the brain dopamine pathways to be different for obese individuals? Big Data will help answer this over time and will lead to the behavior of the obese individual or individuals in their life that altered the pathways. By definition, immoral behavior is causing these cascading effects.

Big Data Morality is falsifiable. This means that we can use the data sets to test and prove hypotheses on causes and outcomes were there is moral disagreement, such as charity, abortion, sexuality, drug and alcohol use, welfare, gun control, gambling, and others. Never before have we been able to understand so clearly how things play out over time, cut through the noise of individual experience and understand the signal of collective experience, or understand new behaviors so quickly. It synthesizes secular and religious morality. It separates truth from dogma, philosophies, opinions, beliefs and fragmented human experiences.

Knowing what is right and wrong, however, is not the same as choosing between right and wrong.  

[1] http://techcrunch.com/2010/08/04/schmidt-data
[2] http://www.emc.com/about/news/press/2012/20121211-01.htm
[3] http://healthyamericans.org/report/100
[4] http://www.cdc.gov/pcd/issues/2012/11_0220.htm
[5] http://tauruspet.med.yale.edu/staff/edm42/courses/ENAS_880_2011/papers/GJ-Wang-Lancet-2001-obesity.pdf
[6] http://en.wikipedia.org/wiki/Dopamine



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