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Will the enormous amount of data generated by nearly every human on the planet be used only to generate wealth, or for social good?
In 2011, data scientist Jeff Hammerbacher, infamously lamented, "the best minds of my generation are thinking about how to make people click ads. That sucks."
It turns out, ads were just the beginning.
More than 2.5 quintillion bytes of data are produced every day, which combined with rapidly advancing analytics technology, could be used to improve billions of lives - yet the industry is only living up to a fraction of that potential.
Evidence abounds of the data revolution's dark side. We've heard a lot about companies following our every move online so they can sell us things. There are many reports of countries using new technologies to monitor their citizens and manipulate election results.
The bottom line is this: data and humanity - and data and business - are now intertwined, and there is no going back.
The important question is whether the data generated by nearly every human on the planet will be used only to generate wealth, or be used for social good.
The problem is too little of the artificial intellegence (AI) technologies, data tools and talent - that PWC predicts will create $15.7 trillion in global economic value by 2030 - are benefiting the social sector.
For example, legions of scientists and engineers are developing artificial chatbots to help corporate executives manage their calendars. Yet far fewer people work on projects like building algorithms to help immigration officials find the best placements for incoming refugees, so they can find jobs more quickly and move their lives forward.
This is where the growth of social enterprise offers a lesson. Social enterprises in the US employ more than 10 million people and make revenues of $500 billion annually, using business models that generate both financial and social returns.
At the same time, according to a recent survey by Deloitte, social enterprise leaders struggle to create systems that sustain social impact. They also spend a lot of time worrying about how to responsibly use the large amounts of data being created each day by consumers, citizens and their employees.
Enter data scientists, who can support our businesses and social organisations alike. Many data scientists are yearning to use their talents for good.
The same year Hammerbacher decried their brilliance being wasted on ads, some of his peers launched DataKind, a global non-profit meant to connect data science talent with social enterprises and organisations to harness the power of data and AI for the good of humanity.
DataKind has since deployed thousands of expert volunteers to work on more than 250 projects around the world. It has helped social enterprises in India provide pay-per-use solar power to rural households that lack reliable electricity. It has created algorithms that predict human rights violations and helped communities save water and money in drought-stricken California.
These successes prove how data science can help humanity. But the scale of this work pales in comparison to the millions of data scientists and engineers doing for-profit work without positive social impact. The field of data science needs to rebalance, but to do that credibly requires the corporate and social sector working more closely.
Today, everyone generates data, but not everyone benefits from it. Data science can and should be marshaled as a tool in the global fights against poverty, disease, hunger and climate change. The Rockefeller Foundation is working to address these challenges. We hope others will consider using data for a higher purpose, too.
Read the original article on Thomson Reuters Foundation.
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