Much has been made over recent years of DEI’s destructive capability when implemented in the workforce, be it public or private. Promotions and positions based not on merit or experience, but rather on measuring criteria such as race, gender, or variations thereof, have led to many a ruinous situation in which those utterly unqualified for a job have nevertheless been handed the reins, with predictably disastrous consequences. That said, while DEI has been thoroughly discussed and discredited, a new twist on the DEI trick has crept into the workplace mostly unnoticed. Namely, slanted AI, slanted in this case meaning AI based on assumptions not unsimilar to those that fuel DEI.
AI is not nearly as mysterious as some make it to be. It is the accumulation of human-created knowledge and material, be it scientific or artistic, rolled into a massive database to which prompts are sent and from which amalgamations are created. AI, regardless of how it is used or misused, is not a creative force. It is a rapid-fire paraphrasing summarizer, the modern-day equivalent of schoolchildren from days gone by furiously poring through an encyclopedia while calculating just how much rewording would be necessary to hopefully fool their teachers into thinking that the wee lads and lassies had made any effort toward genuine research on next week’s assignment.
The main issues with AI are how, in addition to enabling extreme laziness in that it is far easier to type in a few queries and claim the response as original content than actually doing the work writing and research necessitates, is the old adage that has been true ever since computers started becoming a tool rather than a curiosity. Namely, garbage in, garbage out. While one bad bit of information should not, at least in theory, corrupt an entire examination of any given subject, enough bad info will spoil the soup. Making this even more insidious is how easy it is to hide bad data deliberately introduced to produce false results. Witness in recent times how the deliberate obscuring of COVID’s origins, and the immediate rush to push untested vaccines rather than treatment of the actual illness, led to deaths, societal breakdowns, and economic destruction from which we have yet to recover fully.
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The assumption that a machine-measured generalization somehow must trump (no pun intended) actual hands-on experience by people who have devoted themselves to living a task, directly learning from the experience, and, through implementing this knowledge, assisting in the betterment of all involved, is as lazy as letting any given AI service write a term paper. However, in today’s business world where the mantra is when in doubt, do everything on the cheap and never mind how doing so loses dollars while saving pennies, we are seeing an increased usage of standardized tests for employment created not to measure actual knowledge or experience, but rather how closely someone’s “self-evaluation” matches what the averages based on who knows what match the proclaimed standard established by someone whose work and life experience does not extend past the search field on Google’s home page.
An especially egregious example of this is suddenly informing an established workforce that, to a one, they need to reapply for the jobs they are already demonstrably doing quite well to see if they meet an AI-derived standard. Apparently, the expression “the proof is in the pudding” has never been properly absorbed by anyone’s water and power-guzzling data center. Aside from the decidedly unfunny, yet laughable all the same, nature of self-evaluations designed not to gather any authentic data, but rather play an extended game of gotcha via deliberately contradictory questions, the damage done to morale when employees who had previously been doing the job, in many cases quite well, suddenly being reassigned to lesser tasks with corresponding cuts in pay and status cannot be overstated. When key members of a team are sent to the bench without cause, the whole team suffers.
The late humorist and social commentator Laurence Peter coined what became known as the Peter Principle: “In a hierarchy, each employee tends to rise to his or her level of incompetence.” The same can be said for AI-led personnel sifting. In its alleged goal of uniformity and testing for excellence across an organization, this hideous, faceless, soulless monstrosity has instead actively crushed the human spirit and disregarded human endeavor in favor of pop psychology blather and an utter lack of workplace knowledge that could not tell the difference between a cash register, cowcatcher, or corner office, let alone the personnel best qualified to use same properly.
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