AI Could Actually Help Rebuild The Middle Class

(Feb 12, 2024) AI Could Actually Help Rebuild The Middle Class
https://www.noemamag.com/how-ai-could-help-rebuild-the-middle-class/

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• The reason is expertise. Becoming an air traffic controller requires years of education and on-the-job apprenticeship — it is a scarce skill. Conversely, in most U.S. states, working as a crossing guard requires no formal training, specialized expertise or certification. An urgent need for more crossing guards could be filled by most air traffic controllers but the reverse would not be true.
• The unique opportunity that AI offers humanity is to push back against the process started by computerization — to extend the relevance, reach and value of human expertise to a larger set of workers.
  • ﻼF: what value on expertise in the trades?; financial / cultural / societal across time & location
• While one may worry that AI will simply render expertise redundant and experts superfluous, history and economic logic suggest otherwise. AI is a tool, like a calculator or a chainsaw, and tools generally aren’t substitutes for expertise but rather levers for its application.
  • ﻼF: AI is not a tool like a chainsaw; we are not subsumed in a reality mediated by chainsaws
    • AI is not a too like a calculator. Calculators provide explicit knowledge [what] and offers objective data that we can believe. AI delivers implicit knowledge and outsources the “why” of it all. The “why” is how we cultivate wisdom, experience, and expertise.
• By shortening the distance from intention to result, tools enable workers with proper training and judgment to accomplish tasks that were previously time-consuming, failure-prone or infeasible. Conversely, tools are useless at best — and hazardous at worst — to those lacking relevant training and experience. A pneumatic nail gun is an indispensable time-saver for a roofer and a looming impalement hazard for a home hobbyist.
  • ﻼF: How do workers develop “proper judgment” without direct experience? This “proper judgment” is that of judging the AGI output without regard to how that output was assembled. Automation bias will ensure that we do not sufficiently question the assemblage; it will simply become “the new gospel” [as Zubov points out below].
• The erroneous assumption that the future is determined by technological inevitabilities — what Shoshana Zuboff terms inevitabilism — deprives citizens of agency in making, or even recognizing, the collective decisions that will shape the future.
• [In the past] Artisans spent years acquiring at least two broad forms of expertise: procedural expertise, meaning following highly practiced steps to produce an outcome; and expert judgment, meaning adapting those procedures to variable instances.

Although artisanal expertise was revered, its value was ultimately decimated by the rise of mass production in the 18th and 19th centuries. Mass production meant breaking the complex work of artisans into discrete, self-contained and often quite simple steps that could be carried out mechanistically by a team of production workers, aided by machinery and overseen by managers with higher education levels. Mass production was vastly more productive than artisanal work, but conditions for rank-and-file workers were typically hazardous and grueling, requiring no specialized expertise beyond a willingness to labor under punishing conditions for extremely low pay.

• Whereas skilled artisans were almost necessarily adult men — reflecting the years of apprenticeship required to master their trades as well as restrictive gender norms — early factories made abundant use of children and unmarried women. The skilled British weavers and textile workers who rose up to protest mechanization in the 19th century — the eponymous Luddites — are frequently derided for their supposed naive fear of technology.
• As the tools, processes and products of modern industry gained sophistication, demand for a new form of worker expertise — “mass expertise” — burgeoned.
• As computing advanced, digital machines proved more proficient and much less expensive than workers in mastering tools and following rules. This eroded the value of mass expertise, just as the technologies of the Industrial Revolution eroded the value of artisanal expertise.

But not all tasks follow well-understood rules. As the philosopher Michael Polanyi observed in 1966, “We can know more than we can tell,” meaning that our tacit knowledge often exceeds our explicit formal understanding.

• that there exist many tasks that human beings intuitively understand how to perform but whose rules and procedures they cannot verbalize — is often referred to as Polanyi’s Paradox.
  • expert judgment
• As computerization advanced, the earnings of workers with four-year college and especially graduate degrees like those in law, medicine and science and engineering, rose steeply. This was a double-edged sword, however: computers automated away the mass expertise of the non-elite workers on whom professionals used to rely.
  • Thus,rather than catalyzing a new era of mass expertise as did the Industrial Revolution, computerization fed a four-decade-long trend of rising inequality. 

Like the Industrial and Computer revolutions before it, Artificial Intelligence marks an inflection point in the economic value of human expertise.

• Pre-AI, computing’s core capability was its faultless and nearly costless execution of routine, procedural tasks. Its Achilles’ heel was its inability to master non-routine tasks requiring tacit knowledge. Artificial Intelligence’s capabilities are precisely the inverse.
  • In a case of cosmic irony, AI is not trustworthy with facts and numbers — it does not respect rules. AI is, however, remarkably effective at acquiring tacit knowledge.
• AI’s capacity to depart from script, to improvise based on training and experience, enables it to engage in expert judgment — a capability that, until now, has fallen within the province of elite experts. 
• [In the future, AI’s] primary role will be to advise, coach and alert decision-makers as they apply expert judgment.

Thesis: [a future where AI ] would support and supplement judgment, thus enabling a larger set of non-elite workers to engage in high-stakes decision-making….enable a larger set of workers possessing complementary knowledge to perform some of the higher-stakes decision-making tasks currently arrogated to elite experts like doctors, lawyers, coders and educators.

Most people understand that mass production lowered the cost of consumer goods. The contemporary challenge is the high and rising price of essential services like healthcare, higher education and law, that are monopolized by guilds of highly educated experts.

• But AI has the potential to bring these costs down by reducing scarcity — that is, by empowering more workers to do this expert work.

Nurse Practitioners

• are elite decision-makers. Their work combines procedural expertise with expert judgment so they can confront one-off patient cases where the stakes for judicious decision-making are extraordinarily high.
  • What makes the NP occupation relevant here is that it offers an uncommonly large-scale case where high-stakes professional tasks — diagnosing, treating and prescribing — have been reallocated (or co-assigned) from the most elite professional workers (MDs) to another set of professionals (NPs) with somewhat less elite (though still substantial) formal expertise and training.
    • Electronic medical records and improved communication tools enabled NPs to make better decisions.
• From contract law to calculus instruction to catheterization, AI could potentially enable a larger set of workers to perform high-stakes expert tasks. It can do this by complementing their skills and supplementing their judgment.
• ChatGPT did not eliminate the role of expertise. While the best writers remained at the top of the heap using either set of tools, ChatGPT enabled the most capable to write faster and the less capable to write both faster and better — so the productivity gap between adequate and excellent writers shrank.
• In all three instances, AI tools supplemented expertise rather than displaced experts. This occurred through a combination of automation and augmentation. The benefit of automation was paid in time savings.
  • ﻼF cheap adequate novice v expensive expert
• contraindicated: a specific situation in which a medicine, procedure, or surgery should not be used because it may be harmful to the person.
• AI can extend the reach of expertise by building stories atop a good foundation and sound structure. Absent this footing, it is a structural hazard.
  • ﻼF C&C paving; plan, plan, build

The leading technology companies of our era have faltered in delivering autonomous driving. Why? It’s not because operating a steering wheel, accelerator and brake pedal is a stretch for robots. It’s trivial. What remains profoundly challenging is interpreting and responding appropriately to a world of unpredictable pedestrians, ever-changing road hazards and inclement weather. Seen in this light, the cognitive and physical dexterity required to install a breaker box, prepare a meal or catharize a patient, appear awesome. 

• London cab drivers, for example, train for years to memorize all the streets of London — but smartphone navigation apps have made this hard-earned expertise technologically obsolete and economically superfluous.
• Tools can and do encroach on their users’ expertise. But the opposite is just as often true. Recall the air traffic controllers from earlier. Absent radar, GPS and two-way radios, these highly trained experts could do little more than squint at the sky.
  • In economic parlance, navigation apps automated the expertise of London cabbies. But radar, GPS and two-way radios did the opposite for air traffic controllers. Innovation in this case did not automate, it created a new type of expert work.

History and scholarship demonstrate that the technologies that societies develop and how they use them — for exploitation or emancipation, broadening prosperity or concentrating wealth — are determined foremost by the institutions in which they were created and the incentives under which they are deployed.

See: The missing voices of faith in tech development

• AI poses a real risk to labor markets, but not that of a technologically jobless future. The risk is the devaluation of expertise. A future where humans supply only generic, undifferentiated labor is one where no one is an expert because everyone is an expert.
  • ﻼF 🐔

OpenAI’s mission is to ensure that artificial general intelligence (AGI)—by which we mean highly autonomous systems that outperform humans at most economically valuable work—benefits all of humanity.

https://openai.com/charter/

…we should ask not what AI will do to us, but what we want it to do for us

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