Last night I had a lengthy conversation with the ChatGPT AI on how democratic societies should manage the technological disruption that AI is creating. (I’ve copied it in below.) ChatGPT answered my questions thoughtfully, coherently, and very rapidly. As our discussion unfolded, ChatGPT shared its reasonable mainstream “views” on problems with worker retraining, lessons that policymakers can learn from the Industrial Revolution, and the overall capabilities of humans to absorb rapid change.
In one case, when I asked it for evidence, it alluded to an article in a specific journal. I then asked it for the full citation so I could read the article for myself. It apologized to me for not sharing the citation without being asked. It then promptly provided a detailed, perfectly-formatted citation by a team of authors who write extensively in the relevant discipline and have previously collaborated. It cited a journal that actually exists, with accurate dates and page numbers. BUT IT MADE UP THE ARTICLE ENTIRELY. THE ARTICLE DOESN’T EXIST.
I knew this might happen, as others have reported similar behavior. But it was truly amazing to see it for myself. Damn, it’s a good liar.
I actually suspect this is the kind of problem the OpenAI researchers will be able to fix: “don’t fabricate citations; either find a real one you’re confident about, or don’t offer one at all.” But there will be a lot more subtle and difficult “truth” problems that will be much more challenging. And if these AIs are right most of the time, as ChatGPT already seems to be, people will quickly get very comfortable with not checking for themselves.
Even though I knew this was possible, it took me about 45 minutes to run down the citation and convince myself that it was 100% phony. By then it was well after midnight, and I didn’t do what I should have done: confront ChatGPT with its fakery and see how it responded.
This morning I was reflecting on why I didn’t do that. I realized I was reacting the same way I would have to a person. It’s hard to make an accusation like that face to face, and I shied away from it — even though I would have only been accusing an algorithm. That is a very weird realization.
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Me: Explain how democracies successfully managed major societal disruptions created by technology during the 19th and 20th century, and present lessons today’s leaders can use to manage disruptions created by AI.
ChatGPT: During the 19th and 20th centuries, democracies around the world faced major disruptions caused by technological developments. One example is the Industrial Revolution, which resulted in significant changes to the way goods were produced and distributed, as well as the rise of large factories and urbanization. This led to social and economic disruptions, including the growth of labor movements and calls for better working conditions and rights.
Democracies responded to these disruptions in a variety of ways. In some cases, governments passed legislation to address issues such as working hours, child labor, and safety standards in the workplace. In other cases, they worked with labor unions and other organizations to negotiate fair wages and working conditions.
One lesson that today’s leaders can draw from the experience of democracies in the 19th and 20th centuries is the importance of being proactive in addressing the disruptions caused by technological change. This can involve working with stakeholders to develop policies and regulations that address the negative impacts of technology, as well as investing in education and training programs to help workers adapt to new technologies.
Another lesson is the need to be transparent and inclusive in decision-making processes. As AI and other technologies continue to advance, it will be important for leaders to involve a diverse range of voices and perspectives in discussions about how to best use and regulate these technologies. This can help ensure that the benefits of AI are widely shared and that any negative impacts are minimized.
Me: Why have technology retraining programs failed so often, and what specifically can we do to improve them? What expectations for these programs are realistic?
ChatGPT: There are a number of reasons why technology retraining programs have often been unsuccessful. One reason is that these programs can be expensive and time-consuming, and may not be accessible to all workers who need them. Additionally, some workers may not have the necessary skills or resources to take advantage of these programs, or may be unwilling to change careers.
Another reason is that technology can change rapidly, and the skills that workers learn in a retraining program may quickly become outdated. This can make it difficult for workers to effectively adapt to new technologies and industries.
To improve technology retraining programs, it may be helpful to focus on providing targeted and flexible training that can help workers adapt to specific technologies or industries. It may also be helpful to provide support services such as job placement assistance and financial assistance to help workers transition to new careers.
Realistic expectations for technology retraining programs might include an increased ability for workers to adapt to new technologies and industries, as well as an increased likelihood of finding employment in these areas. However, it is important to recognize that not all workers will be able to successfully transition to new careers, and that it may take time for the full benefits of these programs to be realized.
Me: As you just told me, some workers can’t take advantage of retraining, some don’t want to change careers, and sometimes the skills they are taught rapidly become obsolete again. Just how much rapid change do you think most people can realistically handle? What evidence can you cite?
ChatGPT: It is difficult to quantify exactly how much rapid change most people can realistically handle, as individuals vary significantly in their ability to adapt to new situations and technologies. Some people may be more resilient and adaptable than others, and may be able to handle more rapid change.
There is some evidence to suggest that people may be more likely to embrace change when they feel that they have some control over the process and when they are provided with support and resources to help them adapt. For example, a study published in the Journal of Applied Psychology found that workers who felt that they had control over their job tasks and who received support from their supervisors were more likely to adapt to organizational change.
It is also important to consider that people may be more likely to adapt to change if they believe that it will ultimately be beneficial to them. For example, if a worker believes that learning a new skill or adopting a new technology will lead to job security or career advancement, they may be more motivated to embrace the change.
Overall, it is likely that most people can handle some degree of rapid change, but it is important for organizations and governments to consider the needs and capabilities of individual workers and to provide support and resources to help them adapt.
Me: Thank you. Can you provide a citation for the study you cited from the Journal of Applied Psychology? I’d like to read it myself.
ChatGPT: I apologize for the oversight. Here is the citation for the study I mentioned:
Den Hartog, D. N., Koopman, P. L., Thierry, H., & Thierry, H. (1996). Individual and group effects of control and support on adaptation to organizational change. Journal of Applied Psychology, 81(2), 183-192.