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This bias often stems from training data that reflects historical and systemic inequalities. For example, AI systems used in hiring, law enforcement, or healthcare may disproportionately disadvantage certain racial groups by reinforcing existing stereotypes or underrepresenting them in key areas.
It is difficult for people to determine if such decisions are fair and trustworthy, leading potentially to bias in AI systems going undetected, or people rejecting the use of such systems. This has led to advocacy and in some jurisdictions legal requirements for explainable artificial intelligence. [69]
Therefore, we call on all AI labs to immediately pause for at least 6 months the training of AI systems more powerful than GPT-4. This pause should be public and verifiable, and include all key actors. If such a pause cannot be enacted quickly, governments should step in and institute a moratorium.
Patrick Soon-Shiong, the owner of the Los Angeles Times, is sparking backlash with a decision to add a “bias meter” to articles the news organization publishes and other editorial decisions.
AI and AI ethics researchers Timnit Gebru, Emily M. Bender, Margaret Mitchell, and Angelina McMillan-Major have argued that discussion of existential risk distracts from the immediate, ongoing harms from AI taking place today, such as data theft, worker exploitation, bias, and concentration of power. [139]
Los Angeles Times owner Patrick Soon-Shiong, who blocked the newspaper’s endorsement of Kamala Harris and plans to overhaul its editorial board, says he will implement an artificial intelligence ...
"Right now, AI is like a steam engine, which was quite disruptive when introduced to society," he said in a recent video. He then used a different metaphor, saying it will evolve in a few years to ...
Biases in AI algorithms and methods that lead to discrimination are causes for concern among many activist organizations and academic institutions. Recommendations include increasing diversity among creators of AI algorithms and addressing existing systemic bias in current legislation and AI development practices. [40] [42]