Companies today understand the power of data to improve their processes, to micro-target their advertising, and to identify and exploit market efficiencies. But data science can be directed to purposes besides profit. “Data for good” offers powerful opportunities to social justice warriors.

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Using Data Science for Social Good

Here are three reasons why those aspiring to create social change should embrace the power of data:

1. The analytics revolution is happening—with us or without us.

Our digital lifestyles are pumping out data at an unprecedented pace, and easy access to massive computational power makes it possible to scrape and crunch that data with incredible speed.

It’s a bonanza that is drawing all sorts–people who see the opportunity to make a buck, solve a problem, or save the world. Data is powerful, and it’s not going anywhere. Better to be involved in shaping the future than shaking our fists as it rolls us right over!

2. Data science methods can make unfair systems seem impartial.

For example, “predictive policing” uses data science to predict where future crime will occur. But there is an embedded bias in the data that feeds the predictive algorithms. Lower income and minority neighborhoods that are heavily policed are over-represented in the data. The more an algorithm directs police to these neighborhoods, the more crime is recorded there, creating a vicious cycle in which poor, black, and brown people are increasingly policed, arrested, and incarcerated.

As reported by the Human Rights Data Analysis Group, “predictive policing makes policing even more unfair than it already is.”

Social justice activists need to understand data-driven mechanisms of injustice in order to respond effectively.

3. Understanding data science gives us the insight to ask the right questions.

Data itself is value-neutral. It’s the questions we ask of the data that have ethical implications.

On the one hand, the question might be, “Can we use predictive analytics to identify the poor, undereducated, and/or desperate people most likely to buy our subprime loan product?” On the other hand, the question might be, “Can we use predictive analytics to identify the poor, undereducated, and/or desperate people most likely to fall victim to a predatory loan?”

The intention behind our questions has the power to shape people’s lives. People who care deeply about fairness can use the methods of data science to ask questions that identify and address injustice.

So, how can a social justice warrior get involved in “data for good?”

You could join a data science innovation team through DataKind, offering pro bono services to humanitarian organizations. Or you could participate in a modeling competition through DrivenData, working to crack a data science problem on behalf of a social impact organization. Civic Tech and Data Collaborative provides grants, coaching, and knowledge-sharing in support of city-level data solutions addressing the needs of low-income people.

DataLook showcases reusable data-driven projects for social good and supports their replication. A number of non-profit organizations, including Bayes Impact, Benetech, and Human Rights Data Analysis Group, use data science to address social problems ranging from poverty to human rights violations.

Finally, to learn more about data science and social justice, read Cathy O’Neil’s Weapons of Math Destruction and Virginia Eubanks’ Automating Inequality.

The same data science methods that companies use to boost profits can be leveraged to tackle critical humanitarian and social justice issues. We just need more social justice warriors to become data scientists!