From individual to societal data: taking on bigger, badder problems

We have all heard the saying that “knowledge is power”. And in today’s modern economy, data is the new knowledge, which makes data power. We see it evidenced in the collective $1.3T market capitalization of Google and Facebook, whose pixels and cookies track us all over the internet. These massive data collectors began with an focus on individuals. Now, as we collect data about communities, societies, and supply chains, those holding the data will have growing power to impact not just individuals, but whole populations. 

The power of system-level data

Not only are today’s innovators collecting data about individuals, but they are collecting data about populations and processes. For example, Biobot Analytics hopes to transform sewers into public health observatories for whole communities by sampling wastewater from strategic points in a sewer system. Such collective samples can reveal issues as significant as an opioid epidemic, in neighborhoods as small as a few thousand people. Data tracking also promises to improve the fidelity of supply chain processes. Blockchain has been seen as a high potential technology for stemming the circulation of counterfeit drugs as well as upstream labor abuse.

This begs the question, how great is this latent potential? Are we reaching an inflection point where we no longer need to play whack-a-mole, and can finally clean up the messy problems that have previously upended communities, especially in the area of public health?

With great power comes great responsibility

Certainly the intentions of these technologies are to protect citizens, from counterfeit drugs, from themselves in the case of opioid detection. The question becomes how to ensure that the intended benefits manifest and unintended consequences do not.

We have all also heard the saying that power corrupts. Knowing this, we are forced to ask the question, how might the power of data be used corruptly in our own society? If recent technology deployments are any indication (e.g. AI blocking female doctors from the women’s locker room), we must ask, will we ultimately just re-manifest the problems of society using data?

We’ve observed the rise of “Big Brother” social monitoring in places like China, where social infractions as banal as jaywalking are caught by sophisticated monitoring, and have repercussions. Outside of monitoring, we’ve seen the weaponization of predictive algorithms in prison sentencing, resulting in worse outcomes for minorities. 

Given these patterns, we must imagine how cases like opioid overuse detection could be handled in the worst case. If an opioid crisis is detected, how might treatment differ in a poor versus a rich neighborhood? Will the doctors be the police targets in the wealthy neighborhoods, and the residents targeted in the poor places?

Writing society’s story

This — bias perpetuation — does not have to be how the story goes. Data is being used to empower many under-resourced communities. For example, an AI predictive model was able to increase the successful identification of corroded pipes in Flint Michigan from 20% to 97%, enabling the city to afford remediation of an additional 2,000 homes. Data can powerfully determine how we direct our limited resources to otherwise overwhelming problems. 

Knowledge is power, and while deep knowledge afforded by data can help solve problems by exposing them, it does not guarantee that those acting upon them have the best solutions. Impact is dependent on the social systems we operate in — how these analytical tools are used and how their analyses are received. We must ensure that those who can access and act upon community data are as effective at testing their own assumption and biases as they are at pinpointing social problems.