Jennifer Haase on human-AI co-creativity, uncommon ideas, creative synergy, and humans outperforming (AC Ep83)

“We humans often tend to be very restricted—even when we are world champions in a game. And I’m very optimistic that AI will surprise us, with very different ways of solving complex problems—and we can make use of that.”

– Jennifer Haase

About Jennifer Haase

Dr. Jennifer Haase is a researcher at the Weizenbaum Institute, and lecturer at Humboldt University and University of the Arts Berlin. Her work focuses on the intersection of creativity, Artificial Intelligence, and automation, including AI for enhancing creative processes. She was named as one the 100 most important minds in Berlin science.

Website:

Jennifer Haase

Jennifer Haase

LinkedIn Profile:

Jennifer Haase

What you will learn

• Stumbling into creativity through psychology and tech
• Redefining creativity in the age of AI
• The rise of co-creation between humans and machines
• How divergent and reverse thinking fuel innovation
• Designing AI tools that adapt to human thought
• Balancing human motivation with machine efficiency
• Challenging assumptions with AI’s unconventional solutions

Episode Resources

Websites & Platforms

• jenniferhaase.com

• ChatGPT

Concepts & Technical Terms

• Artificial Intelligence (AI)

• Human-AI Co-Creativity

• Generative AI

• Large Language Models (LLMs)

• ChatGPT

• GPT-4

• GPT-3.5

• GPT-4.5

• Business Informatics

• Psychology

• Creativity

• Divergent Thinking

• Convergent Thinking

• Mental Flexibility

• Iterative Process

• Everyday Creativity

• Alternative Uses Test

• Creativity Measures

• Creative Performance

Transcript

Ross Dawson: Jennifer, it’s a delight to have you on the show.

Jennifer Haase: Thanks for inviting me.

Ross: So you are diving deep, deep, deep into AI and human co-creativity. So just to hear—just back a little bit—sort of how you’ve embarked on this journey. I mean, love to—we can fill in more about what you’re doing now. But how did you come to be on this journey?

Jennifer: I would say overall, it was me stumbling into tech more and more and more. So I started with creativity.

My background is in psychology, and I learned about the concept of creativity in my Bachelor studies, and I got so confused, because what I was taught was nothing like what I thought creativity was—or how it felt to me.

It took me years to understand that there are a bunch of different theories, and it was just one that we were taught. But that was the spark of the curiosity for me to try to understand this concept of creativity. And I did it for years.

Then, by pure luck, I started a PhD in Business Informatics, which is somewhat technical. The lens of how I looked at creativity shifted from the psychological perspective more into the technical realm, and I looked at business processes and how they are advanced by general technology—basic software, basically.

Then I morphed—also, by sheer luck—I morphed into computer science from a research perspective. And that coincided with ChatGPT coming around, and this huge LLM boom happened two, three years ago.

And since then, I’m deeply in there. I just fell, fell in this rabbit hole.

Ross: Yeah, well, it’s one of the most marvelous things. So the very first use case for most people, when they first use ChatGPT, is: write a poem in the style of whatever, or essentially creative tasks. And pretty decently does those to start off—until you sort of started to see the limitations at the time.

Jennifer: Yeah, and I think it did so much. It’s so many different perspectives.

I think we—as I said, I studied creativity for quite a while—but it was never as big of a deal, let’s say. It was just one concept of many. But since AI came around, I think it really threatened, to some part, what we understood about creativity, because it was always thought of as this pinnacle of humanness—right next to ethics.

And I think intelligence had its bumps two or three decades ago, but for creativity, it was rather new. So the debate started of what it really means to be creative.

I think a lot of people also try to make it even bigger than it is. But I think it is as simple as—a lot about creativity is, for example, in terms of poets—poetry is language understanding, right? And so LLMs are really good at it. And it’s just the case. It’s fine.

I think we can still live happy lives as humans, although technology takes a lot over.

Ross: Yes. So humans are creative in all sorts of dimensions. AI has complementary—let’s say, also different—capabilities in creativity.

And in some of your research, you have pointed to different levels of how AI is supporting us in various guises—through being a tool and assistant, through to what you described as the co-creation. So what does that look like?

What are some of the manifestations of human-AI co-creativity, which implies peers with different, complementary capabilities?

Jennifer: Yeah, I think the easiest way to look at it is if you imagine working creatively with another person who is really competent—but the person is a technical version of it, and usually we call that AI, right? Or generative AI these days.

So the idea is that you can work with a technical tool from an eye-to-eye level. Really, the tool would have a—well, now we’re getting into the realm of using psychological terms, right—but the tool would have a decent enough understanding so it would appear competent in the field that you want to create.

I think the biggest difference we see to most common tools that we have right now—which I would argue are not on this level yet—tools like ChatGPT and others, they follow your lead, right? If you type in something, they will answer, sometimes more or less creatively.

But you can take that as inspiration for your own creativity and your own creative process. And that really holds big potential. It’s great.

But what we are envisioning—and seeing in some parts already happening in research—I think this is the direction we’re going to and really want to achieve more: that we have tools that can also come up with ideas, or important input for the creative problem.

Not—when I say on their own—I don’t mean that they are, I don’t know, entities that just do. But they contribute a significant, or really a significant part of the creative process.

Ross: So, I mean, we’ll come back a little bit to the distinctions between how AI creativity contrasts to human creativity. But just thinking about this co-creative process—from your research or other research that you’re aware of—what are the success factors? What are the things which mean that that co-creation process is more likely to be fruitful than not?

Jennifer: I think it starts really with competence. And I think this is something, in general, we see that generative AI just became extremely good at, right?

They know, so to speak, a lot and tailor a lot of knowledge, and that is very, very helpful—because we need broad associations, coming from mostly different fields, and connect that to come up with something we consider new enough to call it creative.

That is a benefit that is beyond human capabilities, right? What we see right now those tools are doing—that is one part. But that is not all.

What you also need is the spark of: why would something need to be connected? And I think that is especially where raising the creative questions, coming up with the goal that you want to achieve something too, is still the human part.

But—it doesn’t need