June 25, 2024
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The impact of generative AI on Black communities
Generative AI can potentially exacerbate the racial economic gap, possibly increasing it by $43 billion annually, but it could also help bridge this gap if used thoughtfully. AI impacts Black workers disproportionately, as they are overrepresented in jobs with high automation potential. To mitigate these effects, efforts should focus on reskilling and promoting non-automatable skills.
The bad news remains soberingly clear while the good news seems possible and not as probable. Nevertheless, we can’t opt-out of engaging with any form of AI tools. Staying in AI reality keeps us grounded and better able to navigate flighty AI trends.
The Bad News
Generative AI’s percolation in every industry emulates how Big Tech’s apps have been used in everyday activities. Generative AI tools are large, clunky and produce exorbitant amounts of outputs, just like the Trills aka Big Tech. Generative AI tools, and the Trills’ services, focus their attention on creating more algorithms to decipher relevant outputs from noisy, irrelevant and useless outputs. Generative AI tools consume exponentially more water and electricity than other automated decision systems created by the Trills.
Generative AI tools also digitally embed structural discrimination based on ethnicity, gender identity and all the -isms, just like the Trills. And similar to the Trills, generative AI movement helps to widen the economic gap between Black and white communities. It’s not shocking and mostly irritating that the shadow of enslavement extends over 160 years.
The Good News
This McKinsey article isolates a key takeaway that could help close the Black economic wealth gap by suggesting that “[f]ocusing efforts on developing nonautomatable skills such as these will better position Black workers to develop the increased resilience needed to weather the rapid changes that gen AI will bring.”
And if this statement sounds familiar, it is. Similar sentiments were expressed back in May 2023 through a Medium article.
The open-ended question is how do we tap into our unAIable skills, given 77% of companies are either using or exploring the use of AI while only 34% of companies provide their team members with data skills training.
So many will invest their time and money to learn the most commonly-used generative AI tools in your industry. You’ll earn the badges, certifications and/or degrees only to not obtain the economic increase you’d expected. That’s because you’ve learned AIable skills. You’ve learned what the AI tools are being fed using these massive LLM training datasets.
Consider learning the limits of AI tools instead. We can then more easily identify, exploit and monetize the unAIable skills only us humans possess. AI can’t run effectively without the people’s oversight. Becoming a knowledge gap filler in our expertise increases our ability to build a more economically-sustainable life. For example, in the case of the teaching profession, teaching remains the baseline in instructional delivery with a coaching component giving that next level of insights. This coaching component could help guide your learners in developing their mis/disinformation vetting skills as they engage with instructional content online. Just some food for thought.
Read the Entire Article Here! |
"The sophistication of coding environments, the availability of open source software libraries, and accepted practices of “borrowing” others’ code make the mechanics of code writing easier. But these factors further obscure the range of uneven social constructs that marginalize communities using data and numbers." pg 114
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