Shaping Expertise, Bias and Responsibility in the Age of AI With Roboticist Dr. Ayanna Howard

Full Transcript

Dr. Cristi Ford:

Artificial intelligence isn’t just a tool. It is really a reflection on us. It mirrors our norms, our values, and even our biases. When flawed data shapes algorithms, it can unfortunately reinforce systematic inequality. In this episode of Teach & Learn, we’re going to talk about how technology and humanity converge and how we can work toward a more equitable future.

Dr. Emma Zone :

Welcome to Teach & Learn, a podcast for curious educators brought to you by D2L.

Dr. Cristi Ford:

Each week we’ll meet some of the sharpest minds in the K-20 space. Sharpen your pencils, class is about to begin.

Joining me today is a trailblazing roboticist who blends engineering with social impact. Dr. Ayanna Howard’s career spans NASA, the academy, and private sector. She is currently serving as the dean of the College of Engineering at the Ohio State University and has been recognized by Forbes as one of America’s top 50 women in tech. From navigating her journey as one of few Black women in robotics to challenging how we define expertise when AI is a collaborator, Dr. Howard’s voice is both a warning and a call to action. Dr. Howard, thank you so much for being here today.

When we first met this summer, I was so excited to hear you speak at our D2L Executive Summit and hear your keynote, and I just knew I needed to have you on the podcast. So thank you for making the time today. I’m really excited about the conversation we’re going to have. We’re going to talk a lot about bias. We’re going to talk a lot about your career and your history and the work that you’re doing at the Ohio State University.

But before I jump into any of that, I’d love to touch on what you reminded me of or what I remember from your talk during your keynote, where you talked about how AI challenges our concept of expertise. And really, you talked about how it redefines the relationship between humans and machines. So maybe we can start off by talking about how do we think, rethink expertise in a world where AI is not just a tool, but is also, as you shared with us in your keynote, a collaborator in learning? How do you see this shift really impacting maybe the students you serve? How is it impacting educators and ultimately institutions?

Dr. Ayanna Howard:

Yeah, so I think when I think about artificial intelligence, on one hand you can say, “Well, new technology has always disrupted the workforce.” So automation would come in and manufacturing became more automated, of course, displacing workers.

AI is actually slightly different because the type of work it is displacing is what we used to call knowledge work. So it’s coding, it’s accounting, it’s in some cases management. So when I think about artificial intelligence and how we think about the future, it’s not about a replacement function. The value of AI is not in replacing all of the work. The value in AI is using it as a tool so that it becomes a coworker with us. So it enhances our work, it allows us to do things that are more humanistic, creative, think about curiosity versus repetitive. Coding, believe it or not, I love coding, coding is very competitive, but it’s also repetitive depending on what you’re doing. So thinking about us as educators, how do we teach students then to learn with AI instead of despite AI?

Dr. Cristi Ford:

I love this because as I’ve listened to numerous reports, and you know that when we have these technology hype cycles, there’s a lot of conversation specifically around AI reducing and eliminating jobs. But when you talk about this, you talk about this differentiation. So for listeners who are worried about being an educator in this realm of AI, can you just maybe share a little bit more about what you said about this opportunity to co-collaborate as opposed to maybe feeling like this new tool and technology is going to replace the educator voice or space?

Dr. Ayanna Howard:

Yeah. So one of the, I would say, dilemmas we have as educators is that we teach from how we learned. So how did you learn? It could have been, when I grew up, math was about repetition. You had to learn your timetables and memorize it. That was how you did learning, and now the calculator can do it much faster and better than I can.

So when I think about how do educators adapt and address this new world of AI, it really is thinking of how do I use AI in my pedagogy? How do I change the way that I teach? How do I change the way I do assessment and testing? Because I think about how I’ve talked to students and a lot of them will say, “Well, I learn a lot of stuff from YouTube,” or from TikTok or from other things versus the classroom. Or, “I just talk to my friend group and we study together and that’s how I learn.”

And I think as educators, AI is this element of all the things that are out there, much easier to decipher, much easier to grab. So I think we need to figure out what is it the students are using? How are they using it? And how do I change how I teach so that I am pushing students to do problem solving, I’m pushing students to do critical thinking, I’m even pushing students to question what the AI is doing? I think that’s the role as educators, but we also then have to learn how to use these tools ourselves.

Dr. Cristi Ford:

Yeah. I really appreciate that framing. And I appreciate when you’re talking about things like critical thinking, problem solving, having digital fluency and literacy around these tools and technology, it makes me think about one of the things that you talked about in your keynote in terms of thinking about having a more equitable future. As we know that AI is shaping who has access to information and opportunity, how do we make sure that the skills development in the AI era doesn’t widen some of the existing gaps that we already have seen in digital literacy and other areas?

Dr. Ayanna Howard:

So I think this is the point where universities need to basically take a stand and really think about how they’re bringing AI into the entire structure, the entire curriculum. What I worry about right now is that there are some universities, such as Ohio State that are leaning into AI literacy for all students, not just computer scientists and engineers, but all students, from the student that is in music, to the student that’s in humanities, to the student that is in medicine, like how do you bring AI to your discipline and your tool? But it also requires us to provide basically workshops and pedagogy for the instructors themselves.

So I think this is really this whole element of all K through 12 as well as colleges need to say, “You know what? We are going to embrace AI and we are going to figure it out and co-think about it with our teachers.” And the answer or the pushback of, “Well, no,” from teachers or instructors, that’s not an acceptable. This is a non-negotiable.

I think what that allows us to do is we can also then share information. If I, as a university, figure out what works and what doesn’t work, can I share that with my fellow university to say, “Look, this is how we did it. This is what was great for this type of discipline, not so great for others”? I think if we do this together, we can learn together as an organization, as education, basically the ecosystem so that we can move forward. And I really think it is a non-negotiable.

Dr. Cristi Ford:

Yeah. As I’m listening to you, what I harken to in talking to faculty across the globe around this, and even myself as a faculty member, I just wonder how faculty are grappling with their own identity. So I think about the fact that as a psychologist, there are theories and frameworks that have been around for decades and centuries. So for a long time as a faculty, static expertise was the way that I showed mastery. And now when I’m listening to you, it’s more about dynamic expertise and really moving along the continuum to not just focus on the theories and frameworks, although those are valuable and helpful, but having dynamic expertise in my classroom and then helping my students develop the kinds of skills to be able to meet the moment of where the workforce is going. So hearing you talk about that is really profound.

So I’d love to maybe move to talking a little bit about your book. Somehow, somewhere between sending rovers to Mars, developing robots to help humanity, molding the next generation of engineers, you had time to write a book. You wrote a book entitled Race & Robotics: How to Be Human in the Age of AI. And as I have spent some time with the book, it takes a deeply personal and research-driven look at how bias gets embedded into AI. I guess my question for you before we talk a little bit about your book is, was there some experience or moment when you realized that this book needed to be written?

Dr. Ayanna Howard:

Yeah, thanks for the question. So I don’t consider myself an author or an artist, but I do consider myself an educator. And one of the things that I realized when the conversation around bias was coming up with respect to artificial intelligence was that it was always a negative. And the fact is I don’t think we can ever eradicate bias and that’s a fact. So from my lived experience, from my own research, thinking about it’s not the bias itself that’s bad. In fact, as a woman, when I go to the doctor, I actually want them to be biased. I want those drugs tested on women. That’s a bias. But I don’t want harmful outcomes to come from that bias.

So really, the book was motivated to reframe the conversation, primarily for the future generation of engineers in computer science, but also the world to understand that, one, AI is already going to be used. It’s out there. What do you have as a person that’s using these tools to use it, understanding that there is bias in it, but how do you use it so that you don’t have the harmful impacts from that bias?

So that was really the motivation. It was me being an educator, realizing that the messaging was totally, I would say, in some cases, wrong, and what should I do whenever you see something that’s done incorrectly as an educator? You want to correct. So that was the reasoning.

Dr. Cristi Ford:

Well, and I appreciate your framing around bias not just being seen as a negative, there are negative implications and that there are opportunities to think about bias from a growth mindset or from an asset-based approach in terms of thinking about maybe research to your example and how bias is there and exists, but there is a dichotomy. It’s not all negative, it’s not all positive. So as you think about what you shared and your lived experiences of Black women in robotics in the broader AI conversation, I guess I wonder how has your personal journey shaped the way you approach building equitable AI systems?

Dr. Ayanna Howard:

Yeah, so I have a philosophy that I really think that we, as leaders, need to provide space for all voices to be heard. And when we think about this concept of diversity, it’s not just based on race, gender, it’s based on experience. It’s based on thinking differently. That’s really what it’s about because if I’m creating a product, I want to make sure that it’s being useful and used by the world, not just the US, but the world. So I need to make sure that there are individuals that are at the table that have that lived experience. So that’s really what it is and what I’ve learned through my lived experience is that, one, having the voices, but also making room so that those voices are heard and are integrated into whatever product you’re designing.

Dr. Cristi Ford:

Yeah. I resonate with that and I appreciate your focus on the global context because I sometimes think in North America and Western civilizations, we are not clear about the bias we have in the ways that we educate and are thinking, that the translation in other contexts, in other parts of the world that we need to make take a step back and really think about how we can recognize those voices as well.

So you’ve written about AI systems that can’t recognize female voices, or misidentify people of color. Why do you think these kinds of biases persist in 2025? And how do we dismantle them in design and development?

Dr. Ayanna Howard:

Yeah, so one of the reasons they exist is, again, I think that we don’t have enough voices coming that are at the table. So I’m going to create from my own lived experience. That’s not wrong. It also means that I will not see when there are errors that impact others because that’s not the way I’m looking at the system. Does it work for me? Does it work for my friends?

Dr. Cristi Ford:

Yeah.

Dr. Ayanna Howard:

Okay, it’s working. So I think that’s one of the things why we still have it, is that we have … I’m an engineer, I’m a computer scientist. My discipline is that. The way I think about the world is through that lens. So the tools I’m going to make and the tools I’m going to create, even though they have a human-centered point of view, empathy is probably not going to be high on the list of the products that I create. It’s just because that’s not the way I was necessarily trained. So that is the reason is that we don’t have enough voices at the table that have differences of experience and opinion.

Dr. Cristi Ford:

That’s really good.

Dr. Ayanna Howard:

What can we do? And what we can do is, one, try to ensure that our educational system trains sufficient number of voices. But it’s also bringing in the stakeholders and other groups. It’s going out and saying, “You know what? There is a community representative. There is a community organization.” They may not be programmers or engineers or computer scientists. Is there a way that we can provide space so that we co-create a tool that might be used by that community? And what does that look like?

And the problem, I would say, is as a company, it means that the cycle for development is going to take longer. But the output means that you have something that will work for more people.

Dr. Cristi Ford:

As you talk about the lived experiences, I can resonate with that because as a psychologist, empathy is important to me, right? But I may not have a systems thinking approach as I think about this work in the ways that you do, so I really echo your point around making sure that there are multiple voices at the table. And as I talk to institutional leaders, maybe we can talk a little bit about Ohio State around this, but one of the voices that’s really critical is the student voice. So as you talk about the opportunities to have these voices in the space, I guess I’m wondering, how are you doing that or how are you seeing that happening at the Ohio State University?

Dr. Ayanna Howard:

So it’s working in different aspects and different elements, thinking about the student voice. And the student voice is both the undergrad, it’s the professional students, it’s the PhDs, the master’s, the graduate students. And it’s also working professionals. So when we think about the student, and it’s also online students and it’s business leaders. So the students are part of, I would say, the demand function.

As an example, at Ohio State, we have a new Center for Computing Education, which offers AI literacy courses, AI courses that is digestible from students from any major, any discipline. So part of developing those courses though is we have focus groups of like, “Well, how do you think about computing? How do you think about artificial intelligence? Where do you get your information?” So that even as we’re teaching, we’re also updating and adapting based on the voice of the students. That’s the way that we are approaching it, at least in engineering, Center for Computing Education. Other colleges are doing it slightly different. But I think bringing in the voices of the student is going to be dependent on which students and what is expected in that community because the voices in engineering are going to sound a lot different than, say, the voices in physical education.

Dr. Cristi Ford:

Yeah. That’s good. I want to talk about the future, just looking to the future. And I want to go back and make sure our listeners really honed in on the point that you made around bias being good, there are being opportunities to think about it from this dichotomy of not just being all bad. But your work offers a warning about AI bias, but also this uplifting vision of empowerment. So what gives you hope right now about the future of AI in education?

Dr. Ayanna Howard:

So what gives me hope is that I still see what I would say those who are advocating pop up their heads every so often and give voice, which means, oh, so people are actually caring about this in some form or fashion. So that’s what gives me hope.

I see some states, for example, trying to think about regulation. And having 50 states have different regulations about AI I think is a bad idea, but because we don’t have a national strategy around this, having a state pop its head up and say, “You know what? We need to think about our citizens in some form or fashion, along with the companies that are in our state so that we don’t have too huge of a detrimental impact on them and them doing business here.”

So I see hope because I see some movement of there are people out there that actually care. Some companies will say, “Okay, maybe we will pull back this product because it didn’t quite work the way that we thought it was supposed to work.” So that’s what gives me hope is that I think we’re all trying to get to a point where AI works exactly the way we want for as many people as we want. Motivation of why is different, but at least we are all trying to get to that end goal.

Dr. Cristi Ford:

We’re all trying to sing off that same sheet of music and get us in the right direction. I love that. So as I’m listening to your hopeful projections of the future, I guess I wonder if you could give one concrete action, maybe for an educator who’s listening, a policymaker, maybe someone who’s a dean like yourself, or maybe even a technologist to take today to really be thinking about what are the ways they can ensure more equitable AI? What might that be? What would that call to action be for those folks?

Dr. Ayanna Howard:

Oh, actually, one that’s pretty simple is if you’re using any of the generative AI tools, it could be the Claude or the Gemini or Copilot, there is a way that you could provide feedback when the system provides you an incorrect result. Don’t just ignore it. A lot of us were like, “Oh, that’s not the right answer. I’m going to change my prompt,” or, “Oh, I’m going to use another tool.” Actually provide some feedback. It’s called customer service back to the company.

And if enough of us do it around one item or one thing, the company actually does listen. The company takes all of this data, tries to parse through it. And if there is something that is a, what we call an impulse function, oh, there’s a lot of people saying the same thing about this thing that’s incorrect, they will go and fix it. And that’s a simple thing, just give some feedback.

Dr. Cristi Ford:

I love that. And that’s something that we always are excited about getting at D2L. We have a Product Idea Exchange avenue for our customers and clients to be able to do just that. So what you will find that when we make improvements to the technology, we are looking for what we call, we call them the PIE items. We’re looking for the highly rated PIE items, how to solve those challenges, how to really adjust the technology. So I echo that plus one in that call to action.

I guess before we sign off, I’m going to sign off by asking you to finish a phrase. But before we do that, is there anything else that, given the work that you’re doing at Ohio State, given the work that you’re seeing in your scholarship, is there something else that you wanted to share with the audience here today?

Dr. Ayanna Howard:

Yeah, so I think one of the things that we can do for ourselves is try to disrupt our own jobs. So this is where that creativity comes in. I might think of my task. Maybe I’m an accountant, maybe I’m an educator, maybe I’m administrator, thinking of, “How would AI disrupt my job?” Let me actually try to disrupt my own job because you know exactly what you do. And then lean into that and say, “Okay, let me think about this.” That means that this could happen and it will, it’s just a matter of time. So start then retraining yourself.

So that’s kind of the path, but the first step is to use your creativity, use your understanding of your discipline and your knowledge base and say, “How could AI disrupt my job?” And then figure out ways that you can disrupt yourself, this is what companies do all the time, so that you can remain relevant in whatever job is of the futures.

Dr. Cristi Ford:

That’s good. Let me ask for a personal advice. As I take that advice to heart, would it be a good place to start in areas where there’s replication? Are there certain areas that may be better or low hanging fruit in terms of how I think about taking this to task and disrupting the work that I’m doing?

Dr. Ayanna Howard:

Yeah, the two lowest hanging fruits are the things that you hate to do.

Dr. Cristi Ford:

Okay.

Dr. Ayanna Howard:

Maybe it’s grading. So the things that’s just like, “Oh my gosh, this is part of my job, but I just don’t like it.” One, there’s a win-win because you might figure out, “Hey, it’s actually easier.” And then the other is things that you do over and over and over again, that’s repetitious. Some form of, I create a syllabi every semester, and even though I tweak it, it’s kind of the same operations. You can actually give AI the book that you’re using and say, “Create a syllabi for this course.” Oh my gosh. So things that you do over and over and over again throughout your career, see what AI does.

Dr. Cristi Ford:

That’s good. That’s good. And to your point, for those that may be skeptical listening, I don’t think you’re saying that in terms of the grading or the syllabus creation, that the output is the final destination, but it is allowing you to move forward and brainstorm and to iterate on some things. But as the educator, you still have the final say in terms of how that work moves forward.

Dr. Ayanna Howard:

Yes. And in fact, once you start using some of these tools, you’ll find out they’re not 100%.

Dr. Cristi Ford:

Right.

Dr. Ayanna Howard:

And again, that’s when I think about how do we teach our students, understanding even when it’s going to fail comes with practice, comes with understanding, comes with you being an educator using the tool yourself and saying, “Well, I know where it’s going to fail.” You know what? That’s what I’m going to give my students so that they can fail and then I can teach them how to fix it with their own knowledge.

Dr. Cristi Ford:

That’s really good. And I think it’s the iteration that happens. I was thinking about using ChatGPT 5.1 versus the older models and really having to push back on the agent, saying, “No, that’s not what I asked you to do. Go back and do X, Y, and Z.” And really allowing myself and thinking with other faculty that this is an iterative process. So what you thought you knew six months ago, the models have changed. So also continuing to increase your fluency, I think is a really big point.

But you’ve given us some really good fodder. Before we sign off, I’m really interested, I’m starting to do this thing where I’m asking my guest to finish this sentence. So my sentence for you is being human in the age of AI means …

Dr. Ayanna Howard:

Leaning into your curiosity and creativity.

Dr. Cristi Ford:

Okay. That’s fantastic. Dr. Howard, really thank you for spending some time with me. Thank you for all the great work that you’re doing at the university. Thank you for your book. Thank you for just sharing your expertise with our listeners today.

To learn more about what Dr. Howard has to say about identifying and fighting bias, pick up a copy of her book, Sex, Race, & Robots, today. Please check out another podcast, Ingenuity hosted by Dean Ayanna Howard, to hear more stories of innovation within the Ohio State University College of Engineering.

And thank you to our dedicated listeners and curious educators everywhere. Remember to follow us on social media. You can find us on X, Instagram, LinkedIn, or Facebook @D2L, and subscribe to the D2L YouTube channel. You can also sign up for the Teaching & Learning Studio email list, for the latest episodes like this one or articles and masterclasses.

And if you’ve liked what you’ve heard, remember to rate, review, and share this episode. And remember to subscribe so you never miss what we have in store. You’ve been listening to Teach & Learn, a podcast for curious educators brought to you by D2L.

Dr. Emma Zone :

To learn more about our K-20 and corporate solutions, visit d2l.com. Visit the Teaching & Learning Studio for more material for educators by educators, including masterclasses, articles, and interviews.

Dr. Cristi Ford:

And remember to hit that subscribe button and please take a moment to rate, review, and share the podcast. Thanks for joining us. Until next time, school’s out.

Resources Discussed in the Episode

• America's Top 50 Women In Tech
• Exploring the promises and challenges of AI with Ayanna Howard '93
• Sex, Race, and Robots author Ayanna Howard discusses how to identify, fight bias
• Audible Original: Sex, Race, and Robots
• JPL Engineer in a Class of Her Own
• Hosted by Dean Howard, Enginuity podcast to focus on engineering ingenuity
• Enginuity Podcast Hosted by Dr. Ayanna Howard