The rush toward AI has been relentless. New tools appear weekly. Promises pile up faster than our ability to evaluate them. And somewhere in that noise, it’s easy to lose the space to ask the harder questions, the ones that don’t fit neatly into a product roadmap.
That’s where Marianna Ganapini lives.
An associate professor of philosophy and data science at the University of North Carolina at Charlotte (UNC Charlotte) and a co-founder of LogicaNow, a boutique consultancy focused on AI governance and responsible AI, Ganapini spends her time doing what she calls “applied theory.” She takes ideas that usually stay trapped in academic journals and pushes them into the real world, where decisions actually get made. In her conversation on The Learning Experience Ops Show, she makes a compelling case that responsible AI isn’t just about guardrails or compliance. It’s about rethinking trust, learning, and what we owe each other in a world increasingly shaped by machines.
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From the Ivory Tower to the Messy Middle
For most of her career, Ganapini followed a familiar academic path. Philosophy degree. PhD. Teaching and research. A life built around reading, writing, and thinking deeply, often alone.
Then the pandemic hit.
Like many people, she began to question the impact of her work. The theory still mattered, but it felt incomplete on its own. She wanted to stay in academia without staying isolated. She wanted ideas to land somewhere tangible.
“I realized I wanted to keep thinking about abstract things,” she explained, “but also find them in the real world.”
That realization pulled her into the fast-moving, imperfect, high-stakes space of responsible AI. It also led her to co-found the company LogicaNow, something she openly admits pushed her far outside her comfort zone. Philosophy, after all, rarely trains you for operating in ambiguity with clients, deadlines, and commercial pressure.
What she brought with her, though, was something the AI industry desperately needs: patience with complexity.
Trust Is Not a Feature
One of Ganapini’s core areas of work is trust. Not trust as a checkbox, but trust as a philosophical condition.
In theory, trust asks difficult questions. What makes something trustworthy? When is trust justified? What does it mean to rely on something responsibly?
Those questions become far more complicated when the “something” is a machine.
Much of today’s AI discourse reduces trust to a list of attributes: fairness, privacy, explainability, and accountability. Those matter. But Ganapini argues they’re not enough on their own.
One overlooked ingredient is self-knowledge.
“I trust you not just because you’re reliable,” she said, “but because you know when you’re reliable and when you’re not.”
Humans can say, “I don’t know,” or “This is outside my expertise.” Most AI systems cannot. That gap creates risk, especially when AI outputs are delivered with confidence and fluency that feels human.
This is where her work on metacognition comes in. The idea is deceptively simple: systems that can represent their own uncertainty may be more worthy of trust than systems that always sound sure. It’s a philosophical insight with direct implications for how AI gets designed, evaluated, and deployed.
Why Learning Is the Most Fragile Use Case
Ganapini is particularly concerned about AI in education, and not because she’s anti-technology. Her concern is subtler and more unsettling.
Learners don’t know what they don’t know.
That makes them uniquely vulnerable to over-trusting AI systems. When a tool summarizes, suggests, nudges, or critiques, it doesn’t just support learning. It shapes thinking. It frames what feels relevant. It influences judgment.
For experienced professionals, those nudges can be evaluated. For students and early-career learners, they often can’t.
This is where responsible AI moves beyond bias and into autonomy. Over-reliance on AI risks deskilling, the gradual erosion of a person’s ability to reason, decide, and wrestle with uncertainty on their own.
It also raises a question that doesn’t get asked often enough: what rights are at stake?
Ganapini wishes the AI ethics conversation talked more explicitly about human rights, not just technical safeguards. Privacy is a right. Autonomy is a right. Self-determination is a right. When AI systems quietly shape how people learn and think, those rights deserve serious attention.
The Human Role Isn’t Going Away, It’s Getting Sharper
One of the most grounding parts of the conversation is Ganapini’s realism about the future of work and learning. AI will change everything. There’s no opting out. But that doesn’t mean human contribution disappears.
It gets more specific.
Knowledge creation remains hard. Deep, role-specific expertise still takes time, judgment, and lived experience. Designing learning that helps people build capacity, not just consume content, still requires humans who understand context.
“Content may get cheaper to produce,” she noted, “but knowledge doesn’t.”
The same applies to teams and organizations. Ganapini values people who don’t just execute tasks, but who strive to understand the space they’re in. Curiosity. Context. Identification with the mission. Those qualities can’t be automated, and they matter more, not less, in an AI-saturated environment.
Slowing Down Is an Act of Responsibility
The conversation ends where it began, with the need for stillness.
AI moves fast. Ethical reflection doesn’t. Ganapini doesn’t pretend to have a perfect solution, but she does offer a sliver of hope. The initial generative AI frenzy may be easing. The wave hasn’t stopped, but it’s slowing just enough to let us catch our breath.
That pause matters.
It gives us time to ask better questions. Not just what AI can do, but what it should do. Not just how efficient we can become, but what we might lose along the way.
Responsible AI, in Ganapini’s view, isn’t about fear or hype. It’s about care. Care for learners. Care for autonomy. Care for the human judgment that no system should replace.
In a world racing toward the next tool, that might be the most radical stance of all.
Listen to the full conversation with Marianna Ganapani on The LX Ops Show or wherever you listen to podcasts.
Podcast Transcript– Jason Gorman with Marianna Ganapani
Jason Gorman (00:00)
The reason I started the LearningOps show is pretty simple. It’s to explore the weird frontiers that are emerging across learning experience operations. And one of those frontiers, one that gets some attention but not nearly enough attention, is the ethical side of using these new tools. For me, I’ve realized that I need a little stillness to really think through the ethics of something, especially when it’s new or unfamiliar.
But ⁓ AI doesn’t give us that stillness. It’s moving too fast. And I often don’t have the time or the space to truly consider the ethical implications of what we’re creating. And that is exactly why I’m so excited about today’s We’re going to slow down a bit. We’re going to maybe try to take some of that time and unpack what it really means to bring ethics into our work with AI. So I am joined today by Marianna
Ganapini, who’s an associate professor in philosophy and data science at the University of North Carolina at Charlotte. She’s also the co-founder of LogicaNow Now, which is a boutique consulting company specializing in AI governance and responsible AI. Welcome to the show, Mariana.
MARIANNA B. GANAPINI (01:13)
Thank you for having me.
Jason Gorman (01:14)
Yeah, thanks for doing it. All right, so we usually start with a lightning round of quick questions just to kind get ourselves warmed up. If you’re ready, we can just jump in. All right, cool. So what’s a 30 seconds or less elevator pitch about what you do?
MARIANNA B. GANAPINI (01:24)
Yeah, absolutely. Thank you.
So what I do is applied theory, which is sort of a contradiction, if you will. But it really gets to the core of what I’m doing. So I get theoretical ideas around AI, and I try to apply them in the private sector world, even in academia. So this is sort of what I’m really focusing on, like applied theory.
Jason Gorman (02:04)
Great, next question. When you are absolutely totally dialed in at work, what are you most likely to be doing?
MARIANNA B. GANAPINI (02:14)
Mostly reading and writing. So when I spend a lot of time ⁓ focusing on research, the research side of both philosophy and sort of AI governance, responsible AI. And so I spend most of my time reading, writing, and then ⁓ taking calls and collaborating with other people. That’s mostly what I do.
Jason Gorman (02:42)
All right, next question. What event or moment inspired you to do what you do?
MARIANNA B. GANAPINI (02:49)
I think like a kind of turning point was the, during the pandemic, I got bored of the kind of what I was, what I doing, which is what just theory and not an application. And so I thought, Oh, I really need to try to do something more, more applied, more tangible, but I don’t want to leave academia. I don’t want to stop thinking about these things. So what can I do? And so basically I decided to, I should.
⁓ try something around AI and responsible AI. So that’s how kind of I found myself in this space. And I think one of the first people I talked to was actually you in that transition.
Jason Gorman (03:34)
That’s exciting. I don’t think I knew that. I did not know that at the time, actually. I could just say that. Well, I have to say it’s so fortuitous. mean, I think you placed your bet in the right. As it turns out, AI is pretty applicable, widely applicable. ⁓
MARIANNA B. GANAPINI (03:35)
Yeah, I think so. I think so now that I remember.
Yeah, exactly.
But there are a lot of things to reflect on and a lot of like theoretical sort of ⁓ ideas that can find sort of place and a time into the space of responsible AI. So it’s actually pretty applicable and yet it requires a lot of theory and a lot of thinking.
Jason Gorman (04:06)
Alright.
I’m glad you said that. I’m gonna come back around to some of those theoretical ideas. That’s great. ⁓ This could very well be its own one hour conversation surely, but I’m still gonna ask it as a lightning round question. What are you the most anxious about for the future?
MARIANNA B. GANAPINI (04:36)
I think I’m mostly anxious about ⁓ students and the role of AI in education, ⁓ which is, I think, also relevant to what you guys are doing. So the fact that students use ChatGPT and they are not able to read and think, ⁓ that worries me. So I think that’s the most pressing issue, honestly.
Jason Gorman (05:04)
think this feeds nicely into this next lightning round question. If you had to summarize your area of responsible AI for a 10-year-old, how would you do that?
MARIANNA B. GANAPINI (05:20)
What would I say? Well, responsible AI is a way to make machines. Maybe I would say robots.
computers into something that is safe, secure, ⁓ fair, nice to us, not manipulative. And so, yeah, that’s really what we are striving to do, just making a good thing rather than a bad
Jason Gorman (05:50)
That’s very reassuring. No, it’s good. Let’s just stop. Let’s just hope that this just feels good. feels I feel like, that feels so nice. You should write a children’s book. I feel like that’s responsible AI.
MARIANNA B. GANAPINI (05:52)
It’s calling me, but I was like, I’ll just take your car.
Yeah, I have
a daughter and so we talk about this.
Jason Gorman (06:09)
Oh really? Wow.
I have to say my kids, all right, I’m breaking my own rules. I’m gonna go into a thing here. My kids have quite a lot of anxiety about AI. They’re a little older. They’re 21 and 17. So they’re coming up against real life choices. But it is not, they’re in this place where it’s very real for them. They have very strong opinions about
MARIANNA B. GANAPINI (06:24)
yeah.
Jason Gorman (06:38)
Responsible AI use and ethical AI use is quite interesting to me, actually. I’m curious if how old is your daughter? She’s 12. So where is she in that? How would you characterize her?
MARIANNA B. GANAPINI (06:48)
which is 12.
I don’t think she’s aware really of the worries that we have about AI and your kids probably have about AI. So to her, technology is her phone. That’s pretty much it. She has some intuitions about privacy, think. Like, OK, maybe they’re spying on us, that kind of stuff. But yeah, not the complexities of what your kids are worrying about.
Jason Gorman (07:07)
Mm-hmm. Got it.
Mm-hmm.
Good. Well, there’s plenty of time for that kind of worrying.
MARIANNA B. GANAPINI (07:23)
Yeah, there’s no time. We got anxious.
Jason Gorman (07:29)
All right, last lightning round question. What’s a book, movie, or podcast that has nothing to do with work? So not a workbook, but ⁓ it shapes how you think.
MARIANNA B. GANAPINI (07:40)
So this is, yeah, my answer is not going to be very exciting, but I’m not going to use junkie for like politics and all that economics. so basically I have subscriptions to newspaper, New York Times, Washington Post, and you can actually listen to the articles, have them read by the AI. ⁓
Jason Gorman (07:49)
Mm.
MARIANNA B. GANAPINI (08:06)
Instead of reading them, you actually just kind of listen to the AI reading the article. And I spend a lot of time doing that, like maybe in the morning and then it’s like a radio, but just on that. that, usually really shapes just my day, my thinking and sort of where my mind goes, even around when it comes to AI then. Yeah, it has an impact for sure.
Jason Gorman (08:18)
Mm-hmm.
All right, thank you. All right, let’s dive into our other questions. As I said at the top, you’re involved in a lot of things. You have a business, you are teaching, you’re also raising a child. What does a week of work look like for you, across all the things you do? What does that look like?
MARIANNA B. GANAPINI (08:40)
Sure.
Yeah.
So I think there is teaching. There’s a lot, like at least 20 % is teaching. As I mentioned, a lot of research. So I spent a lot of time reading philosophy or eye governance paper. And then I spent a lot of time engaging with people. It could be collaborators. Like I have a longstanding collaboration with IBM research. So I spent a lot of time with them.
Jason Gorman (09:00)
Mm-hmm.
MARIANNA B. GANAPINI (09:24)
And then with businesses, I ⁓ don’t have now a central role in Logica now. I am not the CEO anymore, but I still sort of help them a lot in sort of procuring business and thinking about what they can do and how they should engage with professionals. so,
I spent a lot of time helping them thinking through those things. And yeah, so it’s really a mixture of kind of nerdy work and more like social, social in the sense of engaging with other people kind of work. Still nerdy, more social, less solitary. Whereas before it was, before the pandemic, was mostly just by myself. So.
Jason Gorman (10:06)
Yeah.
Still nerdy.
I’m
curious actually to maybe hear more about your career leading up to this particular turn, right? In the area of study that you have, the area of work that you have now. I’m kind of curious, what has your career looked like? And what did each maybe, what did each stop in your career contribute to your thinking in the way that you were?
MARIANNA B. GANAPINI (10:43)
Yeah, thank you for this question. So my career has looked very linear. So up until the pandemic. ⁓ I finished college, then I guess I worked for a bit, but then I went to grad school. I came to the US because I’m from Italy.
I was at Hopkins for my PhD. so, and I was just doing philosophy work and really thinking about abstract stuff and wanting to get published and wanting to get a job in academia. And then I got a job at Union College and it was like, like just, you know, humming along like in this world in which you really study and write and then study again. And then as I mentioned around 2020,
I think around maybe because of the pandemic, I don’t really know. I started to sort of suffer this, that this was too isolating and not impactful enough. And I wanted to try new things and because of like my career being so linear, I was like a little bored, maybe too. And so I realized like, how can I do what I’m doing, but in a more applied way? How can I still think about abstract things, but then find them
Jason Gorman (11:55)
You
MARIANNA B. GANAPINI (12:06)
also in the real world. How can I help people think about abstract things and theory in a way that has impact for what they’re doing, not just because it’s fun and interesting. So philosophy is very abstract. And one of the things about philosophy, it’s like, it’s kind of a little bit of a mental game. And so it’s fascinating how you kind of lose yourself, but then it gets a little too, too abstract. so responsibly, I really came as a way for me to
Jason Gorman (12:23)
Hmm.
MARIANNA B. GANAPINI (12:34)
kind of bring these things together. And then we decided to start this company that was even more, more applied, more, more just stepping out of my comfort zone. hadn’t had no idea how a company worked. And so I, that was sort of the, the trajectory was from kind of an isolated ivory tower kind of situation to a more like messy and, and it’s pretty rare. got to say like, especially in philosophy that to try to do this. So, um, yeah, but yeah.
Jason Gorman (13:04)
Well, I’d love to talk a little more about this. I said I wanted to come back to the point you made about, about theoretical ideas. one of the things I really want to do in these conversations is to get into specific tactical stuff as much as we can do on these types of things. Even so, though,
I would like to just hear more about, what are the theoretical ideas that you find yourself working with every day and how does that apply? What is that? So what are the ideas, but then what does it look like when they are applied?
MARIANNA B. GANAPINI (13:38)
So one big idea, right, that you can study just ⁓ from a purely theoretical standpoint is the idea of trust, for instance, right? You can ask yourself, what is trust? What are the conditions for trust to happen? What are the, ⁓ what it means to be trustworthy? So this is kind of
moral psychology, ⁓ ethics, and really you don’t need much data about that. It’s not about like why and where people trust. It’s more like the conditions of possibility around trust, right? So really thinking from a just kind of armchair standpoint about all these things. But then this question about trust becomes center of responsible AI with the idea of trustworthy AI, right? Like the European Union.
Biennial philosophy around AI is really a trustworthy AI philosophy. ⁓ And it is the idea that we want to create machines that are worth of our trust, that it’s OK to trust them. And now you’ve got this kind very theoretical issue about trust. And then you’ve got to apply now to very concrete things, like how do you actually build machines like that? What would it for people to trust these machines? Is it possible to trust these machines?
So from a very sort of, as I said, abstract, then you get very kind of start getting to the very nitty gritty conditions for trustworthiness. And now you’ve got your list, I don’t know, fairness, privacy, explainability, ⁓ and all these things. But for instance, I don’t think, and that’s kind of a very applied thing. I think that one element of trust that makes us trust others is the fact that others
Jason Gorman (15:17)
Hmm.
MARIANNA B. GANAPINI (15:37)
have a grasp of whether they’re reliable, what their sort of motivations are. So I trust you, not just because you’re reliable about what do, but because you know you are reliable and you know when you are not reliable. You can tell me, look, Marianna, I don’t know about this. I’m sorry. And this idea, then you go like, but machines don’t have that. And maybe they should have that kind of.
Jason Gorman (15:54)
Hmm. Hmm.
Hmm.
MARIANNA B. GANAPINI (16:02)
self-awareness and then you can start asking, okay, what does that look like? And that’s kind of the work that we are doing at IBM on metacognition. So it’s like, can start very abstract and then you get very concrete, very, very fast in terms of what are the conditions that you’ve got to have on a machine to make it trustworthy, to make us want it, to trust it, to begin with. So that’s sort of one example.
Jason Gorman (16:31)
That’s incredibly interesting. And I have really complicated mixed feelings about ⁓ AI technology companies. I think what they’ve given us is miraculous and terrifying. And as you’re describing the attributes of trust and how to foster trust, I realized that that
could be used nefariously, could be used in great ways to help people collaborate more effectively with these tools, but could also be used to manipulate people, to create trust where it’s not deserved, where it’s not legitimate. And I, how do, how should we as, certainly as consumers, but specifically as people…
in this learning experience field, how do we think about when to trust, how to trust? What would be your advice to us, to like a seminar of us, you know, thinking about our future tool use?
MARIANNA B. GANAPINI (17:46)
Yeah, that’s a super difficult, difficult question. When to trust. well, so there are two, two things, right? So you trust when you think that whatever the person, the tool is good at doing what it’s supposed to be doing. So it’s reliable or not reliable. Okay. So. ⁓
I was using an application yesterday and I showed that to my students as a sort of a machine learning application. And I thought, I think my students can use this application. And I made some, some assessment around how reliable it is at, know, making like writing summaries or doing videos and whatnot. Right. So there is an issue of how good it is. And then the other issue is.
What are the intentions behind the machine? I don’t think machines have intentions of their own, but they might have the company kind of. And so here comes the moral issue of, okay, are they getting my data? Are they using my data? Are they manipulating me? Are discriminating against me or others? And so these are also, these two things sometimes get conflated, but they’re really two different things. And I think trust should be a measure of.
Jason Gorman (18:43)
Hmm.
MARIANNA B. GANAPINI (19:08)
both, it’s like, OK, we want things that work, but also we want things that come with some guardrails. And that’s becoming very difficult because of how generative AI and LLMs in particular are becoming more more AI companions. mean, even if we think about how you can use them in education.
I’m using an LLM to help me think through a topic or like, it’s just not just an assistant is someone who is sort of pushing me in a certain, it’s going to nudge me in a certain direction. For instance, I was just using an LLM to do, ⁓ have feedback on it, on a paper that I wrote. And of course that is kind of nudging me and saying, okay, these are the objections. I don’t, agree with this and pushing like, it’s going to influence my thinking and.
Jason Gorman (19:48)
Mm-hmm.
Yeah.
MARIANNA B. GANAPINI (20:04)
And so
I think that’s going to more true for younger learners and for learners in general, because the problem with the learner is that they don’t know what they don’t know. So they don’t know that they’ve had hard time evaluating what comes out of the, of this LLM. And so the learner is like, okay, should I trust it or not? It’s very difficult. Like for us, it might be easier because we kind of know what’s, what’s going on. got on, this is not true or.
Incorrect. For a learner, gets in a very, it’s a very ⁓ fragile, how can I put it, position to be in and having to do with these things.
Jason Gorman (20:35)
Yeah.
I know you do quite a lot of work. You’ve done quite a lot of work building learning programs for adults post-graduation, right? These are working adults. One of the things that ⁓ we’re constantly asked is how we measure stuff, right? How are we measuring the things that we do? And this is a very difficult, complicated question to answer in education.
broadly, but I am curious about measurement of trust. So if I’m a, well, I am, but you and I, people like you and me, people who might listen to this show, right? They’re gonna be using these tools to, they could be using them for a couple purposes. They could be using them for personal productivity, right? Just kind of their own personal use. They could be using them.
a system that their organization has set up for them, so they’re using a bigger, they’re part of a bigger thing, it’s not just personal. They could also be building things that are learner-facing. And I wonder if there are any standard methods of scoring or measuring the trustworthiness of the tools that we’re using, or whether this is still a gap.
that we just haven’t quite figured out yet.
MARIANNA B. GANAPINI (22:12)
There are measures. So there are evaluation measures of various kind, both in terms of whether the output is biased, where the output is unreliable. So there are sort of uncertainty measures for LLMs. So the reason, I mean, it’s not like it’s a new field with the old
machine learning, it’s a more, evaluation and testing is more established with LLMs is kind of a more new frontier, but we are seeing more and more of that. And then there is another way, which is the fact that companies put guardrails around this LLMs in terms of what they can say, cannot say.
And so these are things that can be explicitly sort of communicated. So you can also gain trust by saying, this platform commits to these standards versus this other platform doesn’t. And so I think there are various means to ⁓ try to establish when trust is warranted. ⁓
Jason Gorman (23:31)
Are you saying that there are organizations that are doing that scoring? There are. these are, is it public? Is the scoring publicly available? Something that we could or I guess we could, we should be maybe looking at these scores.
MARIANNA B. GANAPINI (23:51)
So.
There’s
some, yeah, there is now it’s kind of unblinking, but there is some available scoring publicly available on the one hand. On the other hand, there are companies. They want to sell you their ⁓ tools to do this metrics and evaluation. ⁓ And there are governance platforms. They’re going to use these tools to then.
and create governance around this tools like more like wholesome, more complete kind of governance. Yeah, but there are are various metrics out there and stand and sort of yeah, some of a lot of this LLMs have been tested around the standards and there are other results out there. Yeah, I can try to find and send it to you. Yeah. Yeah. Yeah.
Jason Gorman (24:45)
That’d be great if you do that, we can stick it in the show notes. That’s
terrific, thank you. Before, the very beginning of this episode, I talked about my own, just my personally, my own need for stillness and time to think through.
challenging ethical problems and novel to me ethical problems and that AI and the way that it’s being rolled out has really made it hard to slow down. And I’m wondering if you have any advice to us as we are
as we’re just like wave after wave of new tool is just crashing over us. How do we ⁓ just remove ourselves long enough? What can we do to really slow down appropriately?
MARIANNA B. GANAPINI (25:58)
Yeah, maybe I’m not the best person to ask. I struggle with this a lot. Well, one thing is, I mean, I don’t have, honestly, any particularly illuminating advice, but maybe a piece of a little bit of good news is that I think maybe we are seeing a little bit of a slowdown after the rush of
Jason Gorman (26:01)
Okay.
MARIANNA B. GANAPINI (26:28)
uh, chat GPT and, and generative AI, are starting to see a little bit of a kind of slow down in what this technology can do and can achieve. So chat GPT five has, has, um, had mixed, mixed reviews. So hopefully for our own sanity, we’re going to see a little bit of slow down and so we can catch up. Um,
Jason Gorman (26:45)
Yeah.
MARIANNA B. GANAPINI (26:56)
I mean, the noise out there is insane. So it is very difficult for everybody to just survive. And for me as well. So not a lot of good advice, I’m sorry.
Jason Gorman (27:10)
Well, do you mean, that… Do you think that your field has caught up ⁓ to the current state of AI?
MARIANNA B. GANAPINI (27:20)
I don’t think it has caught up. ⁓ I think that the issues around AI are so many. The responsibility that we have and that companies have to make this a more ethical or more sort of safe place, I think, no, we haven’t catch up and they haven’t catch up.
Jason Gorman (27:30)
Mm-hmm.
MARIANNA B. GANAPINI (27:44)
caught up, sorry. ⁓ What I can see, what I can say is that just the wave is slower. So now we can start to be like, okay, these are all the things that we need to start really thinking about. Like, okay, how LLMs influence us, for instance.
So we talked a lot about bias and discrimination. I think we should push that. But now the issue is maybe there’s not just about discrimination. Maybe there is problem with manipulation and problems with the fact that how can we integrate LLMs in the educational sort of space and but still allow for the presence of the human. So ⁓ having a human in the loop in this educational space is like
It’s a catchphrase, right? Like everybody agrees with the human in the loop, I guess. Like there’s gotta be a human, but then it’s not clear what the human is gonna do. And so I think we need to think very hard on what is that we are contributing as humans in the learning process? What can we give students and people taking the courses that cannot be given by an AI?
So what’s our role? I think that’s sort of a kind of a challenge, a very challenging thing to think about. And it’s hard to do if the AI is moving so fast because you come up with an idea and then hopefully AI can do it. So hopefully we’ll slow down a bit.
Jason Gorman (29:15)
Right.
That would be great. I personally would like a chance to catch up. ⁓ But I hear you for students and especially for an organization like a higher ed organization that tends not to move that quickly to begin with. ⁓ These kinds of things are, I mean on purpose, right? That’s part of the design of higher education.
MARIANNA B. GANAPINI (29:25)
Yeah.
Jason Gorman (29:45)
Higher education organizations are not supposed to be reactive, fast moving, twitchy, they’re supposed to be deliberative, careful, thoughtful, slow. Those are great qualities under lots of normal circumstances. ⁓ But this is a tough set of conditions, I think, for higher ed right now.
MARIANNA B. GANAPINI (30:09)
Yeah, but even in the sort of past, like in the learning environment we’re in, ⁓ especially you guys, but also us with Logic Canal, so the adult learning, as you mentioned. Even there, it’s unclear what is going to be our role.
Jason Gorman (30:22)
Mm-hmm.
Yeah.
MARIANNA B. GANAPINI (30:33)
and create video and all that. think that’s kind of some of the worries I’m sure you have too. ⁓
It is challenging to think about, okay, where are we going with this? What is the future of the business?
Jason Gorman (30:56)
Yeah, I mean, it is existential. There’s no other way to put it. mean, and I think, I don’t remember exactly how long ago it was, maybe a year. But at some point last year, early last year, early 2024, I just realized that…
It just hit me kind of suddenly how extensive the impacts would be. I sort of saw them one by one early, right? Oh, this will be different and then this will be different. And then all of a sudden I was like, oh no, everything is going to be different. Once it’s sort of, I saw the full picture and I remember talking to my team and saying, one way or the other.
we are all gonna have to change what we do. None of us can hide from, unless we wanna do totally different work. I think our choice is we have to grow and change and learn at a totally different rate and really change even the type of work that we do or we have to find another line of work. I do not think the world is going to offer us something in between. I think that what we have been doing those days are numbered and I…
Unfortunately, I mean, although I’ve been enjoying the learning process, right, and learning these new tools and ways of working, but there are a lot of things that are in question. ⁓ I mean, I’m curious. I certainly talk about a couple of the things that we’re thinking about, but are there things that you feel are, you know, is a good direction?
How are you thinking about the future for Logica now, your company?
MARIANNA B. GANAPINI (32:54)
We provide both content and learning experience. And I think that on the content side, AI is going to be slower to catch up for sure, in the sense that we provide courses on responsible AI, especially responsible AI ⁓ tailored around specific persona.
Jason Gorman (33:07)
Hmm.
Yeah.
MARIANNA B. GANAPINI (33:22)
⁓
shown us so role specific. So it may be very technical. Some of our contributors are sort of lawyers, cybersecurity sort of specialists. So we go very deep into the technicalities and apply and kind of marry that with the responsible AI sort of issues. And so that kind of level of knowledge is not something AI can give you. And so
it becoming cheaper and cheaper to produce the learning experience, it’s still not that cheap to produce the knowledge, not cheap at all. So that’s one issue that we’re seeing sort of having, still having traction on our side, yeah, for sure.
Jason Gorman (34:14)
Right, so
it sounds like more towards owning a product, selling a product that is a knowledge product, that is a course or something like it. Whereas perhaps in the past it was more services, selling your time, ⁓ working on somebody else’s project. We’re kind of in the same boat. Some of what we’re thinking about is similar, which is we have… ⁓
We have the storytelling course that we’ve built and we have specific audiences that we’ve been ⁓ working with to build it. We’re very excited, so we own that. That’s an example of something that we’ll be doing. And then I think there are other places where we’ll be doing a form of consulting that is really hard for AI to do. And I think that’s probably true for your area of expertise as well.
are certain questions that nobody would want to turn to AI to get the answer, ⁓ they are gonna wanna find a person. this is an incredibly deep conversation. I’m sure there’s so much more we could talk about. ⁓
I actually have two more questions.
So the one is, for people like us in our industry, what’s a question that you wish people would ask more about the responsible use of AI ⁓ that maybe you wish you heard, but you do not hear in the discourse around what we do?
MARIANNA B. GANAPINI (35:50)
So in the responsible AI sort of world, is a question. It’s both an attitude and a question. sometimes I have to say I’m finding the ways people talk about responsible AI and they’re still listing things like privacy and fairness and explainability, accountability. seems to be like…
sort of confusing sort of bunch of things that I feel that is not clear and it’s not obvious to many, to many people, even those who are sort of worrying about this and working in this space. So in other words, I wish that we were a little more careful on how we talk about this space and that when we talk about
responsibility and accountability and risk and fairness. We also think about other things like rights, for instance, like in the list, I never find the rights as a terminology, which is very important, more in the European Union, but it’s also important in general, like fundamental rights and human rights, there things that are important to us in general. In the…
Jason Gorman (37:07)
Hmm.
MARIANNA B. GANAPINI (37:17)
discourse around responsibility. It’s not as sort of front and center. So people talk about privacy, but it’s not really thought as a right, right? Whereas we think that privacy is right. So one thing I wish I would see more is right based approach. So a real reflection about the fact that this technology may undermine some rights, not just privacy, but also
Jason Gorman (37:29)
Mm.
Wow.
MARIANNA B. GANAPINI (37:44)
self-determination, instance, autonomy. Again, on the learning side, I wish that that was an issue. Deskilling like when you ⁓ get people to rely on these machines so much, they’re actually losing their ability to make decisions, to advance their own knowledge. And so they de-skill they lose skills. This is something that people don’t talk enough about, and I wish. ⁓
Jason Gorman (37:48)
Hmm.
That’s great and not something I’ve really ever thought of in quite that way. And that’s quite a powerful question, actually, this question of rights, these fundamental human rights, really. ⁓ Well, I have one last question. My last question is always from a former guest. And…
The last person I talked to, his name is John Thayer. And let me read you his question. So his question is, how are you handling the evolving importance of skills over knowledge in people that you hire or work with? And then there is a second part of this question. How do you grow people? Great for you as an educator. How do you grow people in their jobs or career development?
and boast to develop your workforce in this environment. I can repeat those if you want, two-part question.
MARIANNA B. GANAPINI (39:17)
Yeah,
so let me let me tackle the second part and hopefully also the first one. So how do you grow people? So it is ⁓
The issue of thinking about how people can offer their best or be at their best in the workforce or in a company is a condition of what they can learn in the work or while they are working at that company. So I think that that’s how I think that the fusion of knowledge is key. So in other words,
MARIANNA B. GANAPINI (39:59)
Um, it’s definitely important to learn how to do things and develop various skills, like learning how to use chat GPT in a kind of a useful way, right? Like prompt engineering, whatever it’s take that as a skill. So that’s definitely helpful. But I think that is not just that it’s not just a matter of like skill at using or skill at like kind of more technical or,
Jason Gorman (40:12)
Mm-hmm.
MARIANNA B. GANAPINI (40:29)
specific skills. It’s really about, it’s also about like how much they learn about the space they’re in, in the company they’re working for. So just to give you an example, it’s something that I always wanted to see is, is this person striving, although they are in a specific role, right?
Jason Gorman (40:40)
Hmm.
MARIANNA B. GANAPINI (40:56)
⁓ are they also striving to really understand what I know or why, what I sort of the space I’m in, ⁓ to not be, to become my me, but to be kind of identifying with what this company is doing. So someone who could be a research assistant or someone in sales or it doesn’t necessarily doesn’t really matter, but is this person also sort of curious about.
Jason Gorman (41:05)
Mm-hmm
MARIANNA B. GANAPINI (41:26)
the knowledge space we are in, in particular the responsible AI space we’re in, or do they just want to do sort of fulfill their role, maybe learn a few things and be done. So I guess that’s sort of my perspective is like, person seeing this as a way to stretch and identify with the company, or they just seeing that as they could be working for us or for, I don’t know.
Jason Gorman (41:37)
Hmm.
MARIANNA B. GANAPINI (41:56)
any other company in the world. Yeah, I think that’s what I like to see. that’s how I’m hoping people will work for us, take away from their experience at large.
Jason Gorman (42:10)
It sounds like if I can try to reframe this into language that I sometimes use, when people ask me about what I think about when designing a learning experience, I will often say,
MARIANNA B. GANAPINI (42:16)
Please, yes.
Jason Gorman (42:27)
Contents critical but context is is the most important thing right the context of somebody’s life and what they want to do and The task they have in front of them and their own personal aspirations and their own personal past And actually I am hearing you also reference context in this this like how does a person develop in their job? They have the responsibility and I suppose the organization has a responsibility of helping them key on the context
of their work, not just the task.
MARIANNA B. GANAPINI (42:58)
Exactly. Perfect. You sounded way better than I did.
Jason Gorman (43:01)
No, no, that’s not true. I just want to make sure I was understanding.
This has been really fantastic. I’m so glad you could join us. Thank you. Thank you for joining the show, Mariana.
MARIANNA B. GANAPINI (43:14)
Thank you.