Let me stick with the healthcare setting and turn to science using the example of a venture that my students and I are building: Jana Care, a mobile-enabled chronic disease diagnostic platform. We fight chronic disease with a piece of paper, a drop of blood, and your smartphone. It sounds simple, but to make it happen, there is some pretty neat applied science going on. A small device that attaches to any smartphone comes with a testing strip. The strip can be customized to test for a range of biomarkers.

To develop the strips and the device, we bring together chemistry, of course, but also material science, physics, optics, electronics and computer sciences. Whereas normal glucose monitoring can be done by many over-the-counter devices, diabetics often need to check other measures of their well-being (e.g. HbA1c levels) for which they currently have to go to a clinic or hospital which, for the most part, are inaccessible in the developing world and expensive in the developed world. We are able to do this instantly on any smartphone, very cheaply. So, thanks to science, potentially hundreds of millions in diabetes-afflicted regions such as India, China, and the Middle East, can access diagnostic care. The reduction in lost productivity hours, not to mention improving human lives and increasing longevity, is incalculable. This is the ultimate social and economic inclusion. Jana Care is now developing tests for kidney failure, liver disease, heart failure, and diversifying into animal health.

Here’s another example from the broad area of computational science. Aspiring Minds, a company I co-founded about a decade ago, uses artificial intelligence to create recruitments tests to match individuals with organisations. It now operates out of Delhi, Beijing and San Francisco. Here, the research is in machine learning (as distinct from other parts of artificial intelligence, for example, symbolic manipulation) to find patterns in data that can inform decision-making. The analytics of creative algorithms is married with the science of psychometrics to develop cognitive and non-cognitive assessments.

How does this algorithmic science facilitate inclusion? It turns out, it does so very directly. In fact the company was founded with the aim of resolving what is sometimes called the ‘Lost Einstein’ problem, that is, the idea that we can locate talented youth in under-trawled pastures. There is no credible way to ascertain the quality of talent in non-mainstream locations, which is a big reason mainstream organisations all recruit from tried-and-tested familiar spots – urban areas, elite schools and so on. Talent in vast swathes of society goes unrecognised and unheralded. This missing markets (for talent) problem is the one we set out to solve with the aid of better algorithms.

Today, in multiple countries, we test over five million kids annually and facilitate hiring by thousands of mainstream companies. For example, in India, we find that not being from an elite college normally – without our tests – reduces your chance of finding a job by 24 per cent and results in 20 per cent lower salary, once we control for your aptitude and measured success. As a measure of the efficacy of Aspiring Minds’ tests, their use results in 79 per cent of those securing jobs through us coming from non-elite colleges, as compared to 58 per cent in the open market. Further, the salaries of those placed through our tests depend only on the test outcome, and not on other parameters (gender, race, urban versus rural location, etcetera), so the possibility of exclusion through common forms of discrimination is markedly reduced.

Narayana Health is in a science-intensive setting, but it is not engaged in advancing science per se, despite its incredible run. The Jana Care and Aspiring Minds examples, however, embrace science much more directly in attempting to tackle significant developing country problems. They show that even small companies can engender dramatic inclusion with an engagement with pure science. Imagine if large, incumbent, perhaps even ‘old school’ companies, not to mention universities and often state-run laboratories in the developing world were to embrace science directly!

The metrics we conventionally use, while informative, might also inadvertently discourage R&D spending as a proportion of GDP (India at 0.8 per cent, compared to China at 2 per cent, the US at 2.7 per cent, Japan at 3.4 per cent and Israel at 4.2 per cent), patents filed (in 2017 in the USPTO, the US filed 293,904 applications, China filed 29,674, and India filed 9,222) and peer-reviewed science and engineering articles published (US and China were at 400,000 in 2016, while India was at 100,000). But we have to start somewhere. As important as raising the spending level is learning to spend wisely.

We should stop equating poverty or under-development with science-unfriendliness. It does not have to be that way. As the examples above suggest, mine is not a Panglossian view. We don’t have to have only large capital-intensive versions of science after all. And, evidence of the linkage between science and social inclusion is already around us if we care to look for it. Science, for example, has led directly to the precipitous decline in costs of computing and mobile telephony which affects the developing world daily.

Science can provide the spark that triggers inclusion.