Using Data Science for Social Good
In the tangle of code and relentless momentum of technology, it is often too easy to lose sight of the greater purpose of our work.
While we all carry different motivations with us, I believe that we are unified by the desire to influence with our work and contribute meaningfully in some way. As we continue to learn and move forward with our careers, we also seek to understand how to be able to use our skills to think creatively and critically so as to solve real-life problems.
Data science, as a field, tends to get obscured by so many buzzwords (“AI!” “Deep learning!” “Big data!”) that relate to what we can do, without a lot about why we would do it in the first place. It’s a growing field, with so much potential, but I think it’s important to shift the narrative. Yes, machine learning, but why? Yes, big data, but what for?
How can we leverage data science and its versatile tools to solve real-life problems? What are the implications for social ‘good’? Technologies progress and develop, data becomes more prolific and useful. How can we, as data scientists benefiting from this momentum, help the rest of the world catch up?
As it seems that this is a currently limited topic of conversation on the web, here’s an overview of some available avenues for contribution. The would-be data science do-gooder needs to certainly have more initiative in order to find the appropriate avenues for contribution but it’s not impossible.
Volunteer with a socially-oriented data science program/organization
Some socially-oriented data science fellowships, typically in conjunction with non-profits and local governments, are available. This includes the Data Science for Social Good Fellowship at the University of Chicago and the Thorn Innovation Lab. These fellowships afford a closer insight into the myriad of problems that may be addressed through the application of data and analytic tools.
DataKind is a data-science organization that is solely focused on social good and offers various opportunities for volunteering, whether it’s through mentoring or using your data science skills to help solve a social problem in one of their DataCorps projects. DataKind also coordinates with many non-profits, hosting competitions and hackathons along the same vein.
Contribute via competitions
The main go-to for data science competitions, Kaggle hosts a myriad of competitions intended to test your data science skills. Some of these competitions focus on social problems.
A newer, socially-focused competition platform, DrivenData partners with various organizations. These organizations are typically non-profit, focused on difficult social problems with real-world impact.
Developers and data scientists are able to contribute solutions to help address these problems. Their solutions will be utilized by partnered organizations so that they can more effectively carry out their missions for good.
Other data science competitions and hackathons, oriented around doing ‘good’, are also occasionally held by other organizations.
Consider solutions to real-world problems that you encounter
While useful in framing and presenting problems, competitions aren’t completely necessary for problem solving. A resourceful data scientist can identify and work to solve social good problems on their own, with the data available to them. A great resource for data is the Gap Minder Foundation which provides statistics to understand global trends. Rather than obscuring statistics with emotions or drama, Gap Minder emphasizes objectivity to promote genuine understanding of our world so that we can work better to make it better.
Data for Democracy is a community comprised of e-volunteers who conceive and contribute their own solutions to problems. Anyone can contribute, everything is community-led, and submitted solutions have the potential to drive effective interventions to a diverse set of issues.
Be thoughtful in your professional work
Data science positions exist and continue to emerge across all sectors, from the private to the public and non-profit sectors. There are so many opportunities to make a meaningful social impact in your professional endeavors. Within the private sector, there are certainly companies that develop innovative solutions to greater societal problems. For example, healthcare and other science-oriented (see: climate change) fields continue to evolve rapidly, with growth that that is essentially driven by that of data and technologies. Deep learning and other analytic tools can help improve things such as health diagnostics or solutions to air pollution, which would have significant implications for population health.
Data science work is also available for organizations that are oriented towards serving the public good. Governments are beginning to recognize the importance of data in understanding their citizenry, particularly its use in implementing effective, evidence-based interventions and policies.
Similarly, non-profit organizations also continue to require data scientists to help achieve their missions.
In closing out this section, I don’t want to neglect two particularly interesting organizations: Bayes Impact and the aforementioned DataKind. Like DataKind, Bayes Impact is a socially-minded data science organization that leverages data to solve the world’s problems. Bayes Impact partners with other larger non-profits to solve issues relating to health, unemployment, and justice. Both organizations offer great opportunities for a fulfilling and progressive data science career.
Conclusions
As data scientists, we have the skills to do things that matter. We have the resources, as messy and incomprehensible as they may seem, to truly make a difference. No matter where our paths lead, I believe that it’s integral that we work towards becoming thoughtful citizens and data scientists, to always consider how our work impacts society.
