AWS’s Responsible AI Lead Explains How to Use AI Ethically

Amazon Web Services' Responsible AI lead explains how organizations can use AI ethically.
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Are we, as individuals and working professionals, aware of the risks and implications of using artificial intelligence? What should we consider when implementing AI across our organization?

For this week’s issue of The AI Economy,” I interviewed Diya Wynn, who heads up Amazon Web Services’ Responsible AI team. She shares details about her role within Amazon’s cloud computing unit and provides tips on how companies can ethically use AI.

The Prompt

“In order to take full advantage of the capability with AI and do that in a responsible way, it requires organizations to think about this broader than just the technology alone, but the culture of responsibility and people process along with the technology.”

That’s Wynn explaining to me how she established AWS’ first practice focused on responsible AI four years ago. She had been at Amazon well before that, helping organizations migrate to the cloud or execute “organizational change or operational readiness” related to their cloud infrastructure or investment. Wynn assists AWS customers in her current role by “understanding where there might be potential risks and implications that they need to consider to build this culture and to look at best practices and to align their people.”

Who’s Responsible For Being Responsible?

“The first thing that I often say to people is the responsibility is everyone’s responsibility. So it’s not one individual’s job entirely to own the charge of responsible implementation, development, design and use of AI. It really is an imperative for everyone in the organization to be brought into,” Wynn remarks. Sure, getting company leaders to be in strategic alignment is ideal, but it’s important to include workers passionate about responsible AI.

“Empowering the developer is, I think, useful, in addition to having that top-down strategic alignment to help drive the organizational prioritization of responsible AI. But, it truly does take everyone. The product manager needs to incorporate what is fair and then look at requirements around equity as part of the design. You need your people who are curating data to ensure that they have a comprehensive set of data that is going to be reflective of all communities. You need people to be actually testing and evaluating against those initial design requirements. There’s a role for everyone, and then there is how we use it. It also requires a degree of that. I’ve seen it happen in both ways.”

A Strategy for Thinking About Responsible AI

What should organizations consider when it comes to using AI ethically? Wynn shares a four-part strategy that Amazon and AWS use that enterprise companies might find helpful:

Have a people-centric approach

Make sure you’re going beyond the technology and thinking about the people in the process and culture. Putting customers at the center can help you understand AI’s challenges and risks for underserved or underrepresented populations, so incorporating diverse voice perspectives is necessary.

Think holistically about how services are built

Integrate responsible AI into the entire machine learning lifecycle instead of inserting it at the end before deployment.

Help customers transform responsible AI from theory into practice

How can companies take all the academic studies, research, standards, and expertise and turn them into real services and operationalize responsible AI best practices? For AWS, teams across the company have developed tools such as bias and toxicity filters in Amazon Q Developer, formerly known as CodeWhisperer, and other guardrail tools enabling customers to build gen AI apps responsibly.

Advance the science of responsible AI

There is no “set it and forget it.” The work of responsible AI is never done. As AI development accelerates, so too must researchers and practitioners in order to ensure the technology isn’t used as otherwise intended.

No backing down

“We made a commitment to building our services responsibly. And that commitment is being realized in the services we’re developing and providing for our customers,” Wynn asserts when asked if her team is making a difference inside Amazon. She cites the development of Sagemaker Clarify and Service Cards as early work her team has helped bring to life along with establishing a framework and approach for delivering gen AI services complete with guardrails.

“There’s no backing down in terms of the commitment to responsible AI,” Wynn emphasizes.

With AI proliferating across all sectors and industries, her team is making it a point to be where conversations around AI are taking place, including some events that might make you scratch your head and wonder why Amazon would be there in the first place. But the reality is that Wynn’s team isn’t there to promote the models and AI technologies Amazon is doing—they want to be a part of the conversation to hype up using AI in a smart and safe manner.

She closes out our interview with this insight: “Because the fast ways in which AI is having an impact on the way we work, the tools and technology we use, and the way we live…the people we connect with, the things that we believe, it is infiltrating every area of our lives as well as business. And because of that, I believe, in some ways, the drive towards this responsible conversation is because the stakes are that high, and that is touching everything in every way. We have an opportunity to build and do these things that have a greater benefit, not just to the business to help their bottom line but also to the customer and to society.”

Today’s Visual Snapshot

New research reveals investors still have an appetite for AI startups. Image credit: Crunchbase
New research reveals investors still have an appetite for AI startups. Image credit: Crunchbase

According to new research from Crunchbase, investors are still keen to invest in AI startups. Venture funding increased in Q1 2024 from the previous quarter, with $12.2 billion going to startups in 1,166 deals. That’s a 4 percent quarter-over-quarter uptick.

However, when viewed annually, the publication notes a 25 percent decrease. There is an asterisk for Q1 2023, though, since that’s when Microsoft invested more than $10 billion into OpenAI.

Interestingly, Q1 2024 only had a single $1 billion funding round, a marked difference from last year, when it saw three.

Quote This

“We have very smart ML people in Bing, in the vision team, and in the speech team. But the core deep learning teams within each of these bigger teams are very small, and their ambitions have also been constrained, which means that even as we start to feed them resources, they still have to go through a learning process to scale up. And we are multiple years behind the competition in terms of ML scale.”

— Microsoft’s Chief Technology Officer Kevin Scott, in an email (PDF) to CEO Satya Nadella and Bill Gates, expressing concern about Google’s AI progress in 2019.

The email was published as part of the U.S. Department of Justice’s antitrust case against Google and highlighted Microsoft’s urgency to make a multi-billion dollar investment in OpenAI.

This Week’s AI News

🏭 Industry Insights

🤖 Machine Learning

✏️ Generative AI

🛒 Commerce

☁️ Enterprise

⚙️ Hardware and Robotics

🔬 Science and Breakthroughs

💼 Business and Marketing

💰 Funding

⚖️ Copyright and Regulatory Issues

💥 Disruption and Misinformation

End Output

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