Virtually all scientists come from academic institutions, such as universities. However, most universities only train research scientists, with little space for non-research career paths, this is especially the case for “pure sciences” like physics, biology, chemistry, or math. Usually there are a few sessions about other careers, programs to transfer into, other degrees such as journalism or teaching, but usually this is the exception rather than a possible option. Most scientists we can identify today as impact scientists, are more the result of a path that started in universities, perhaps includes some graduate or postgraduate experience, but then felt their path was not leading in the right direction of impact, so they had to steer off into the unknown. It often includes a phase of uncertainty or reconversion, and definitely was not something recommended when studying. Thankfully, this is changing. This change was probably helped both by the “Data Science” revolution, but also the millennial generation, known for being particularly minded about social impact[i], getting to university age. This section explores some of these efforts from the educational space to create the profile of a scientist much closer to impact, not only focused on research or academia.
One of the most prominent examples of educational efforts is precisely the class where Jane Chane started working on the Embrace company we wrote about in the last chapter. At Stanford, the university where it happens, it is called the “D School,” or “Hasso Plattner Institute of Design at Stanford University.” Besides fostering the creation of Jane’s solution Embrace, they are also known for the creation of “d.light,” a company that creates a very low-cost solar-powered artificial lights. This company started in 2006, the year before Jane took the class. By 2017 they had “sold close to twenty million solar light and power products in 62 countries, improving the lives of over 82 million people,” according to their website.
“D School” has become a hugely demanded class at Stanford. Increasingly globally-minded students, also knowledgeable in complex science and technology skills, look to make a difference in the world. They are aware of the potential they might be able to have, but feel unable to connect their skills with these multi-dimensional complex challenges. The process in this class, as described by David Kelly in a 2013 New York Times profile[ii], is first to boost their “creative confidence” so that they are aware of this potential. They then learn “ideation processes,” where rapid prototyping and user feedback become key strategies to incorporate their scientific and technical skills into this creative confidence.
Another place catering to this impact science demand is a class called “How to change the world” at the University College in London’s Department of Science, Technology, Engineering, and Public Policy. This class occurs once a year and lasts for two weeks. In 2017, when I met its director, Dr. Jason Blackstock, it attracted 700 students across the sciences to include engineering, chemistry, computer science. They are all met by 65 experts from the public and private sector, and presented with global challenges, such as safe water drinking in refugee camps, or reducing carbon dependence in everyday products[iii]. The first week is structured to push them from the overall context of the challenge towards a concrete part they are uniquely positioned to make an impact, gathering in groups with different skills; which might include partnering with external experts. The design of this class favors groups with a wide range of skills and experiences, and with a clear goal. In the end, not only do they experience the process of realizing the value of the scientific and technical skills, but also how hard it is to integrate this into the complex reality of global challenges, with their political, cultural, or strategic dimensions. The underlying strategy of the university is to foster impact by easing the links and partnership building skills of students between academia, policy-making, and industry.
As hinted by the example of these two schools, every year there is more attention to widen the mindset of how science influences society. Obviously via academia and research, but there is much more space to drive positive impact in the world with scientists. One way of looking the difference between academia or research, and impact science is to see the focus of these new schools on “pulling” from the specific impact they seek to have, rather than “pushing” academically trained scientists or academic knowledge into impact. One of the key differences is that when you push, you need to know more or less which direction and what knowledge you are going to be pushing. When you pull, the goal is much clearer by definition, and it is much easier to explore and guide what is it you need to consider or incorporate to get the solution. You could push, for example, the knowledge of new lithium batteries into climate change, and we should. You can also think of energy use and seek how pull solutions, either being new batteries, consumption incentives, or work with biggest consumers. We tend to think of, and train for, scientists and their skills in the first case, but not so much for the second case, even when their skills are equally applicable.
Moreover, this is not just about getting scientists to explore other domains, to reach out into problems whose knowledge domain seems far away, or to incentivize interdisciplinary scientists. There are far too many combinations of disciplines in science to cover all combinations that might get to a particular solution. This is still a “push” mindset. This is about radically flip the equation, from knowledge-based value, to skills-based value. To create a work space when, given a concrete set of very pragmatic and convoluted problems, we can cluster experts and stakeholders together, and then combine in equal terms the inputs from each one, including impact scientists, so we can get to a solution. This is exactly what can explain the creation, and explosive growth of “Data Science,” as we pointed out in the Introduction section of this book. Data scientists pull from the questions they need to answer. They use formal mathematics, models, hypothesis—but all with the mindset of pulling this work into an answer. In fact, one of the biggest risks for a data scientist, or working with big data, is not having a concrete question and spending too much time looking for interesting questions one might solve with the data. While this is great for academic curiosity, the incentive of the private sector is probably in supporting decision making, not supporting curiosity. This mindset on skill-based value is so dominant, that there is a boom in schools of data science where they directly teach these formerly academic skills. While most of these cater the specifics of tech companies and start-ups, there is a wide range of those who seek wider impact with data science. To name two examples: “Fast.ai” and “Data Science for Social Good”. “Fast.ai” is a company dedicated to teaching machine learning so that is understood by as many people as possible, with a special emphasis on social problems and diversity. It is led by Rachel Thomas, a PhD in mathematics turned data scientist, and Jeremy Howard a self-thought machine learning expert and data scientist. The second example, “Data Science for Social Good,” is a course at the University of Chicago which also teaches data science, but specifically for its application on social problems, with a strong emphasis on working with real projects with partnerships with NGOs, local governments, or international institutions such as the World Bank. This project is led by Rayid Ganhi, a former data scientist of the Obama campaign who saw the power of data science skills and decided to work with scientists to focus this potential into positive social impact.
Thank you for the interest reading this far!
This book is available for purchase on Amazon. You could also go there and give it a nice review ;)
For feedback: [email protected]