by Marty Hart-Landsberg

This is the fourth and final post in a multipart series aimed at cutting through the fog of AI hype in order to help us understand some of the dangers we face from AI use and highlight hopeful avenues of effective resistance.  Part I provides an overview of my argument.  Part II debunks the hype surrounding the multimodal generative AI systems produced by the leading AI companies.  Part III critically examines the latest AI-powered technology, AI agents.  This post highlights some current efforts to employ these AI tools in both private and public settings as well as possibilities for building a labor-community movement of resistance to the AI corporate offensive.

Dangers remain

Multimodal AI systems and AI agents may not revolutionize society or business operations like their proponents predict, but that does not mean we have nothing to fear from their use.  In fact, many companies and public agencies are already aggressively seeking to embed these AI systems into their operations, transforming work processes to the detriment of both workers and the quality of the goods and services they produce. Cutting through the AI hype was thus a necessary first step, making it possible for us to clarify the nature of the threat we face and to sharpen our thinking about how to build resistance and advance our own class interests. 

What follows are only a few examples of the ways companies and public agencies are pursuing the use and further development of AI systems.  In all cases their aim is to cheapen the cost of production by diminishing human agency with little regard for the well-being of those that use the goods and services they produce. 

Health care is one of the sectors where corporations, in concert with AI developers, are moving fast to establish a critical role for AI agents.  To this point, their focus has been on mental health, a rapidly growing market as illustrated by the popular use of general purpose chatbots for mental health support.  One effort involves the development of the PatientGPT chatbot, the result of a collaboration between K Health and Hartford HealthCare.  As Ars Technica describes:

PatientGPT works in two modes: a generic medical question-and-answer mode that may incorporate information about the patient, or a “medical intake” mode, in which a patient starts providing symptom information and the chatbot gets less chatty and starts going through clinical flowcharts. After the AI agent collects enough information in intake mode, it will provide a next step, including setting up a follow-up appointment with primary care or seeking urgent or emergency care. If the latter is recommended, the chatbot stops responding to further questions.

But no matter how sophisticated an agent may be, it still depends on a multimodal AI system to operate, and these systems, as previously discussed, have serious limitations that negatively affect their ability to record accurate information or provide appropriate recommendations for treatment.  We can start with their ability to process information:

A recent study found that human note takers create much better notes than AI-powered scribe tools. In some specific cases, the AI performed especially poorly compared to a human: when there was background noise; when the clinician and patient were wearing masks; and to a lesser extent when the patient had an accent, according to the American Medical Journal.

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Image by Kohji Asakawa from Pixabay