Diversity and inclusion in AI

At the start of the dot-com era, the innovators and experts who built computers, software, and systems had something in common: They could all write computer code. From JavaScript to C++, the esoteric-sounding computer languages took specialized learning, experimentation, and programming experience. The result was that, while computer engineers, programmers, and developers found lucrative and exciting careers in technology, the field was largely inaccessible to people with other skills. For the most part, they were not needed.

Today’s AI era is different. AI, by definition, requires broad subject-matter expertise because it is intended to approximate human thinking and decision-making. When you automate intelligence, you are, in fact, automating human experience. For AI to reach its full potential, it typically needs input from people with a diversity of backgrounds, lived experiences, and real-world experience. In this, AI can be a vehicle for diversity, opening doors of opportunity for people in every industry and walk of life.

To achieve these massive benefits and develop and deploy AI tools to our greatest advantage, we will need everyone taking part—but that doesn’t mean everyone ought to become an AI expert or PhD-wielding data scientist. To the contrary, the need is for people to come from all fields to apply their knowledge and labor.

We need people who are passionate about math and those who are not. There is need for testers; for people to label data sets; to consider ethics, philanthropy, enterprise strategy, and all the industry roles that will be (or already are) affected by AI. And we need the perspectives and guidance of all genders, of every ethnic heritage, and from all socioeconomic strata.

The coming together of different backgrounds, experiences, and ideas is fodder for trustworthy AI

Consider an AI tool that reviews a patient’s hospital intake assessment, consults all relevant medical literature, and produces a recommendation to a doctor on the likely malady and best course of treatment. That is a complex system, to be sure, and it requires data scientists and AI engineers to piece together the algorithms, data, and infrastructure that permit valuable recommendations. Yet, if the only decision-makers on AI development are those with narrow expertise in encoding AI, the resulting tool may fall short of its potential value.

Development requires input from medical professionals, hospital administrators, experts in patient advocacy and information privacy, and many others. This broad participation is necessary for potentially every AI system.

One cascading benefit is that drawing so many people into the effort creates opportunities and promotes diversity and inclusion in an emerging professional field, something with which the technology industry often struggles.

Growing AI to its greatest potential

Every person can be affected by AI. Jobs will change, new services and products will emerge, and benefits will compound. Yet, for AI to provide equal value, the teams developing, deploying, managing, and using AI should reflect the diversity of the population it serves. Even as diversity in data science is an ongoing challenge, we have an opportunity to build and encourage diverse nontechnical teams around data science.

AI is a unique technology in this respect. It is perhaps the only technology field that not only provides opportunity for nontechnical professionals, but actually requires their input. Knowing that, we can invite the legions of people needed to grow AI to its greatest potential—and do so early in the AI era. This can set a standard and expectation for the AI field going forward. AI can be for the benefit of all, and everyone has a role to play.

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AI is perhaps the only technology field that not only provides opportunity for nontechnical professionals, but actually requires their input.

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Beena Ammanath

Executive Director of the Deloitte AI Institute | Deloitte Consulting LLP