What Happens When We Compare Ourselves to Generative AI?

Source: https://itsoli.ai/

What happens when we compare ourselves to generative AI?  What conclusions do we reach about our own capabilities? Taly Reich and Jacob Teeny have examined these questions in a new paper titled, "Does Artificial Intelligence Cause Artificial Confidence? Generative AI as an Emerging Social Referent."  The scholars conducted several experiments in which they exposed people to precisely the same work, but told some that it was performed by AI and others that it was completed by human beings.  Interestingly, the  researchers found that people exhibit greater self-confidence (with regard to completing a creative task) when they believe that the work they observed was completed by a generative AI model rather than fellow humans. Teeny offered some explanation in this feature from Kellogg Insight:

"From a practical standpoint, self-confidence is such an important driver of innovation, and past research shows so much of our behavior is driven by the simple perception that we are capable of doing that behavior, whether it’s to undertake a piece of creative work or apply for a dream job... Much of our self-perceptions are based on how we compare ourselves to others. If we’re exposed to people we believe are really good at something, we may think, ‘Oh, I’m not as good at that [task] as I thought I was.’ But if we’re exposed to people who do something poorly or who we believe are less skilled, we think, ‘I’m actually pretty good at that.’”

Of course, simply having more self-confidence does not mean people actually will perform well on a subsequent task.   In one of their studies, Reich and Teeny show that those who had compared themselves to an AI model did no better at a creative task than the individuals who compared themselves to other humans.  

Can AI Enable Us To Focus More on the Work We Love to Do?

Source:  Doyenhub Software Solution

Most of the attention on AI these days seems to focus on how it can make us more productive, as well as how it may even displace some workers whose tasks can now be automated.   However, a new study examines a slightly different question.  It explores whether AI can change the nature of work.  Might we engage in different activities as a result of the impact of AI, and might we even have more time to do what we truly love to do? 

Manuel Hoffmann and his colleagues have published a new working paper titled "Generative AI and the Nature of Work."  They examined the results of a natural experiment associated with the use of GitHub Copilot, a generative AI tool for software developers.  The findings illustrate an interesting shift in the work that developers were doing.  First, the generative AI tool enabled software developers to spend more time coding and less time on administrative tasks related to project management.  Second, they found that developers engaged in more exploratory work with the introduction of this AI tool.  In other words, developers conducted more experiments and spent less time on established projects.  The scholars summarize the key results as follows:

Copliot eligible developers engage with an additional 15 new repositories on average relative to ineligible peers. Beyond simply interacting with a new set of repositories, we also find evidence that generative AI enables developers to gain exposure to a wider range of technologies. In Panel B, we can see that eligible developers increase their cumulative exposure to new programming languages by 21.79% relative to the baseline. We also estimate a version of this cumulative programming language exposure measure weighted by the median salary reported by software developers who use that language.21 Access to Copilot induces developers to experiment with programming languages that command a 1.41% higher salary relative to a baseline of $119,371 (an increase of $1,683).

I found this paper to be quite thought provoking.  It makes perfect sense to me.  If AI is making us more productive, then what are doing with that new time have on our hands? Are we just doing more work in a given period of time, or are we sometimes using that "new time on our hands" to engage in innovation work?  Are we learning new skills, trying out new ideas, creating new products and services, and inventing better work processes?   These outcomes may not only be beneficial for the organization, but rewarding and fulfilling for us personally.  

Gender Divide on AI Adoption

Are men more likely than women to adopt generative artificial intelligence tools in their work?  Indeed, that is what scholars Nicholas Otis, Solène Delecourt, Katelynn Cranney, and Rembrand Koning have discovered. They recently published a working paper titled "Global Evidence on Gender Gaps and Generative AI."  Here is an excerpt from their paper:

The findings above document that gender gaps in generative AI are nearly universal. We find women use generative AI less than men in data from 18 studies covering 143,008 people from across the world as well as data on who uses top generative AI websites and apps. Moreover, equalizing access does not appear to fully close the gap, even when presented with the chance to use generative AI, women are less likely to use this new technology than men. Akin to efforts to equalize female labor market participation and pay (England, Levine, and Mishel, 2020), it appears that social, cultural, and institutional frictions have led to a gendered gap in generative AI adoption.

Why does this gender gap exist?  HBS Working Knowledge recently interviewed Rembrand Koning about the research.  The article, by Michael Blanding, reports that, "the research suggests women are concerned about the ethics of using the tools and may fear they will be judged harshly in the workplace for relying on them."  

Why is the gender gap finding so consequential?  Two important ramifications must be considered.  First, will the differential usage lead to the exacerbation of gender biases?  In their paper, the scholars write:

This disparity has the potential to be significant. As generative AI systems are still in their formative stages, the under-representation of women may result in early biases in the user data these tools learn from, resulting in self-reinforcing gender disparities (Cao, Koning, and Nanda, 2023). Such biases in user data—similar to those that have previously led to racial disparities in generative AI performance—could result in generative AI systems that reinforce gendered stereotypes and in tools that are less effective at the tasks more often performed by women (Koenecke et al., 2020; Guilbeault et al., 2024).

Secondly, the lower adoption rates by women may affect their career opportunities and future compensation, according to these scholars.  Thus, further work will be needed to understand what's driving the gender gap, and how it may be affecting employees and their careers.