This is the first article in what will be a three-part series:
“How to make your mark on the world as a talented, socially conscious data scientist.”
- In this article, we discuss how a socially-conscious data scientist might choose a domain to make the greatest impact.
- The next article will focus on how to maximize the impact you can make at your organization once you’ve chosen your path.
- In the final post, we will finish the series with a discussion around why the best data scientists are world class communicators.
This article was co-written by Marshall Lincoln and Keyur Patel. We are co-founders of the Lucid Analytics Project.
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Data science – especially AI & ML – is positioned to transform the world
Artificial intelligence, big data, and the rapid emergence of the ‘data economy’ represent both a fundamental shift in the way many industries are conduct business, and a massive boost to the global economy.
According to the McKinsey Global Institute, Artificial Intelligence alone could increase global GDP by about 16 per cent by 2030. In absolute terms, this represents additional economic activity on the order of $13 trillion, annually. To put this into context, McKinsey predicts that AI will contribute to global productivity increases at twice the annual rate of the digital revolution in the 1990s and 2000s; and four times the rate of growth engendered by the introduction of the steam engine in the second half of the 19th century.
Current forecasts show that those who need AI most will benefit least
Though the benefits of the AI revolution will be felt across every country on earth, they will not be shared equally. Nor will the risks and negative externalities of this revolution be imposed upon individuals, or countries, in proportion to its benefits.
As McKinsey states, “A key challenge is that adoption of AI could widen gaps between countries, companies, and workers.”
- Countries: It’s forecast that countries with an early lead in AI could capture as much five times more economic benefit than are late to the game – typically poor countries without the infrastructure and talent pool to innovate.
- Companies: Institutions that are able to capture all of the data in a given space can use that data to constantly improve their algorithms. This will enable them to create better products and capture more users – a self-reinforcing cycle that could imbue them with de-facto monopoly power.
- Workers: As AI automates many of the repetitive tasks performed today by humans, much of the workforce will be displaced. New jobs will be created, but their relative numbers – and the skill sets they require – are highly uncertain. On the other hand, those who design, develop, and implement these algorithms are tremendously valuable to the organizations who use them.
If you’re a data science practitioner or student reading this article, you may well stand to benefit from the AI revolution. You’re making what seems like a logical decision to secure your economic future – a decision that few would begrudge you. Yet it’s an uncomfortable truth that you stand to gain from will cause others to lose their livelihoods and incomes – often those who in a position where they can least afford to.
How can an ambitious, talented, and socially-conscious data scientist make a difference?
We don’t believe you should burn your laptops and abandon the domain – far from it. Data scientists possess skills that are rare and valuable – according to projections by the University of California, demand for data scientists exceeded supply by more that 50% in 2018. It’s how these skills are applied – and for what purpose – that matters.
Our argument is not that to make a difference to the world it is only worthwhile to do so on a massive scale. But data scientists are empowered with an especially potent set of skills that uniquely position them to tackle big, global challenges.
But how does an individual translate this potential into action? Where do you start? To understand where you can make a difference, you need:
- A framework for understanding where progress needs to be made;
- A yardstick by which to measure that progress; and
- To understand the ways in which advancements against those metrics can be meaningfully and sustainably effected.
Empowered with this knowledge, those who want to contribute to these changes can make their own informed decisions about how to use their unique skill sets to do so.
One such framework with widespread recognition and consensus is the United Nations Global Goals. In 2015, all 193 member countries of the UN ratified the 2030 “Sustainable Development Goals”: a call to action to “end poverty, protect the planet and ensure that all people enjoy peace and prosperity.” The 2030 Agenda positioned Science, Technology and Innovation (STI) as a key means of implementation of these SDGs.”
These SDGs – also known as the “Global Goals” – provide a framework by which organizations of all kinds, and governments, can align to a shared vision for a better future. Each of the 17 goals provide specific, measurable targets against which to evaluate success, and were deliberately designed to drive progress on an aggressive timeline.
Three years into a fifteen-year agenda, the general consensus is that: 1) we can still hit these targets, and 2) it will be extremely difficult – and progress must be accelerated.
The primary barrier to accelerating progress, however, is NOT one of the technological capacity to do so. The question is one of prioritization: how will we use the ingenious technologies increasingly at our disposal.
Of all the most promising technologies, artificial intelligence – and data science methodologies as a whole – rank among the very top. They promise economic development and transformation on a scale never before seen. Yet unless more data scientists choose to prioritize progress over pure profit, AI will fall short of its potential to transform the lives of those who need it most.
Evaluation Criteria
Data scientists possess a rare and potent set of skills that position them to wield tremendous influence in the world. How they use that influence – and what they prioritize in their own life – is a decision every individual must make. Some will choose to focus their talents as data scientists around making meaningful contributions in advancing sustainable human development.
If this is you, here are a set of questions you can ask yourself to help choose a company, an industry, or a cause to devote yourself to:
- Can your idea, product, or solution be evaluated against specific, measurable social goals – such as the targets and indicators articulated by the SDGs?
- What is the scale of impact: how many people will share in the benefits of your success?
- Who – specifically – is set to benefit from your success? Who loses out?
- Are you working on something that helps raise standards of living for a large number of people – or are you just finding a more effective way to make a small number of rich people richer?
- What are the risks of your idea being badly executed? What does the cost-benefit analysis look like – for example, are you introducing systemic risks into a fragile ecosystem?
- How can you focus building an environmentally sustainable business model?
For some examples of industries in which data scientists using AI can effect large-scale social good, see our article: How AI can help solve some of humanity’s greatest challenges – and why we might fail.
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In this article we explored the kinds of global, systemic problems that data scientists should look to solving, to make the most difference for the greatest amount of people.
In the next post in the series, we will examine the importance of asking the right questions, thinking big picture, and understanding how to use your tools as a data scientist as a means to an end – rather than the other way around.
