6 Ways to Build AI That Incorporates Integrity, Diversity, and Ethics

AI has the power to transform industries — but it can also propagate biases if it’s not designed, developed, and deployed with ethics in mind. Vatsal Ghiya, CEO and co-founder of Shaip, lists down a few crucial steps to ensure your AI initiatives meet ethical standards.

We’ve all seen what happens when AI development goes awry. Consider Amazon’s attempt to create an AI recruiting system, which was a great way to scan résumés and identify the most qualified candidates — provided those candidates were male. When data scientists replicated the company’s existing hiring practices, they unwittingly replicated (and automated) its biases as well. They’ve since scrapped the project, but it remains a potent reminder of the power of AI to magnify bias and harm certain groups.

Technology leaders at Facebook made a similar fumble when the company enabled advertisers to target audiences by gender, race, and religion — all protected classes, according to the U.S. Constitution. The algorithm showed nursing jobs primarily to women, ads for janitorial positions to men in minority groups, and limited real estate ads to audiences of mostly white individuals. Facebook viewed the algorithm as the first step in an ongoing development process. The U.S. Department of Housing and Urban Development responded with a lawsuit.

As more of these cases make headlines, both consumers and companies are increasingly wary of the power of AI. The technology can transform industries, automate mundane tasks, and improve peoples’ lives — but it can also propagate biases and magnify discrimination if its design, development, and deployment do not incorporate integrity, diversity, and ethics. To help ensure your own AI initiatives meet ethical standards, follow these crucial steps:

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1. Define Ethical AI

To prioritize ethics in AI, you need a specific and actionable definition of ethical AI that all relevant stakeholders can subscribe to. A firm definition will serve as a screen, allowing you to quickly filter out what contributes to your goal and what doesn’t. As you formulate a definition, consider data transparency, the importance of diversity, and the integrity of the data that goes into your AI or machine learning solutions.

2. Build Ethics Into Development

If ethics is an afterthought in your products, they’ll never meet meaningful standards for ethical AI. You should build ethical AI into the product development framework. Make time for ongoing process reviews that help incorporate the latest best practices and regulatory guidelines.

3. Create Cross-Functional Expert Groups

Designing, developing, and deploying responsible ML and AI requires input from experts during each of these stages. When a team is composed solely of one group of stakeholders, they’re likely to prioritize their own needs at the expense of others. For example, a somewhat homogenous development team designing a hiring tool to process résumés might unknowingly prioritize individuals with similar backgrounds and education levels, introducing a bias in the tool. With input from design and deployment experts, you bring in more opinions that help tailor a solution without sacrificing ethical goals.

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4. Collaborate With Customers

Customers might know what they hope to achieve with AI implementation, but they could be utterly ignorant of ethics in AI. Collaborate with customers to ensure that they envision solutions that prioritize ethics in addition to other business outcomes.

5. Be Transparent

Machine learning algorithms can be incredibly complex, and while you don’t have to give away your company’s secret sauce to everyone, you should be as transparent as possible about what data is used, how it’s used, and for what purpose. Customers might not need to know every technical detail, but they should know the ingredients to your AI recipe — and how and why those ingredients come together.

6. Empower Your Employees

Employees shouldn’t be guided only by business objectives, or they’ll feel pressured to design solutions that prioritize profit at the expense of ethics. Several household names have already made this mistake, and in some cases, their reputations have suffered irreparable damage. Instead, empower your employees to design responsible products that positively reflect your company’s motives and values.

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After years of slow evolution, the ubiquity of processing power and data are driving a breakthrough in AI, and technology leaders and data scientists are poised to create the next generation of solutions to countless problems. As they do, it’s critical that they prioritize ethics, including data integrity, diversity, and transparency, to build tools that stand the test of time.

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