Interview Questions / Responses:

1.How would you describe the issue of digital accessibility and equity in AI?

From my view, digital accessibility and equity in AI means making sure the tools, data, and interfaces we develop are usable and fair for people no matter their background, technical skill, or resources. It’s not just about “can they click it,” but “do they understand what it’s doing,” “can they use it comfortably,” and “is it built with them in mind.”

2.Why do you think this issue matters for organizations like government agencies and universities?

Agencies and universities serve diverse communities with different levels of access and capability, so if we deploy AI systems without thinking about who’s left behind, we widen the gap instead of closing it. Plus, when public institutions lead by example, they can set the standard for inclusive AI rather than reinforcing an uneven playing field.

3.What’s some background or context that helps explain how this issue developed?

Most AI tools were originally built for enterprise or research users, not for general staff in public service or education. Over time, this left gaps in digital literacy and confidence. In government and higher education, where budgets, priorities, and skill levels vary widely, that difference in readiness has created a clear divide in who can make effective use of AI tools.

4.What kinds of impacts do you think this issue has, and on which groups of people?

The impact shows up among employees and students who don’t have consistent training or support, as well as in smaller agencies and schools without dedicated AI resources. When tools are confusing or poorly introduced, some people benefit while others get left behind. That limits innovation and makes digital transformation uneven.

5.What do you see as the main causes of unequal access to AI tools or knowledge?

I’d point to three major causes: variance in technical literacy (both individual and institutional), uneven resource distribution (hardware, connectivity, training), and tool design that assumes a “standard user.” If you assume a high-tech background, you exclude many :(

6.From your experience leading the AI Enablement Team, what steps could make AI tools easier and more equitable for people without strong technical backgrounds?

We’ve found that plain-language documentation, role-based training tied to real job scenarios, and modular learning paths make the biggest difference. In government, we’re not building AI systems from scratch—we’re helping agencies adopt existing tools like Gemini or Copilot responsibly. The goal isn’t to teach everyone advanced AI concepts, but to give clear, practical guidance so employees know what these tools can do safely and effectively in their daily work. Meeting users where they are makes adoption smoother and more inclusive.

Interview Questions / Responses:

1. How would you describe the issue of digital accessibility and equity in AI?

It’s really about who gets to use these tools and who doesn’t. AI has a lot of promise, but if only people with resources, training, or time can use it well, then we’re not being fair. Accessibility isn’t just about the interface—it’s also about giving people the understanding and confidence to use the tools responsibly.

2. Why do you think this issue matters for organizations like government agencies and universities?

Because we serve everyone. If AI is rolled out in a way that only helps the people already comfortable with technology, we fail our mission. It’s also an ethics question: institutions should think about fairness, privacy, and transparency when they decide how to use AI tools.

3. What’s some background or context that helps explain how this issue developed?

A lot of AI came out of the private sector where the focus was on speed and innovation. When those tools reached public institutions, they met users who needed more support and clearer rules. That’s when the gaps started showing up—people wanted to use AI but weren’t sure how or if they even should.

4. What kinds of impacts do you think this issue has, and on which groups of people?

It hits people who have fewer opportunities to learn new tech, like smaller schools, departments, or staff without training budgets. If we don’t fix that, AI ends up helping the people who already have an advantage. That’s the opposite of what we want technology to do.

5. What do you see as the main causes of unequal access to AI tools or knowledge?

Training and awareness are big ones. There’s also the lack of clear guidance, people don’t always know what’s safe, ethical, or allowed. And sometimes the tools themselves are just built for experts, not everyday users.

6. From your experience shaping policy and governance for AI, what advice would you give educational institutions looking to integrate AI in an inclusive and ethical way?

Start with the “why.” Don’t add AI just because it’s new, add it because it solves a real problem and fits your community. Have clear policies, bring students and teachers into the process early, and talk openly about the ethics—bias, fairness, and data use. If people understand the purpose and boundaries, they’re more likely to use AI the right way.