Interestingly, the training of ChatGPT involved input from those living in extreme poverty worldwide.
In Brief
Some experts in the field of AI ethics have criticized OpenAI for their decision to use Sama for training the ChatGPT model, arguing this choice takes advantage of low-wage workers.
New documents have surfaced indicating that ChatGPT's development was significantly aided by individuals from some of the world's most economically challenged areas. documents OpenAI Corporation began a collaboration with Sama, a company that employs millions from regions battling extreme poverty.
Sama, a social organization that hires millions of individuals from economically disadvantaged regions like Kenya, Uganda, and India, has faced scrutiny over its working conditions. Many workers have reported experiencing long hours and meager pay, yet OpenAI defends its alliance with Sama by emphasizing the crucial job opportunities it brings to impoverished communities.
The choice by OpenAI to outsource its ChatGPT training to Sama has drawn criticism from AI ethics advocates, who believe it exploits those working for lower wages. coded the ChatGPT For just $1.32 an hour, these workers reviewed vast amounts of text from the internet, looking out for potentially harmful content.
A number of Sama's employees have reported that their mental well-being took a hit due to the nature of their work. Although OpenAI acknowledges the use of Sama for outsourcing, they argue that this practice has helped many lift themselves out of poverty.
"We shouldn’t overlook the fact that both ChatGPT and other generative models are the result of extensive supply chains involving human effort and curated data,\" pointed out AI ethicist Andrew Strait.
- The predecessor to ChatGPT, known as GPT-3, showcased remarkable capabilities, especially in linking phrases coherently. However, it faced challenges due to its propensity to make offensive, violent, and prejudiced statements.
- The source of the AI's training stemmed from an extensive collection of text sourced from the Internet, which is filled with a wide array of human expression. Cleaning problematic data from this massive dataset proved nearly impossible, as it would take countless people years to comb through it thoroughly.
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