An article from Privacy International (PI) exploring the Humans in the AI loop approach considers the often overlooked role of data labellers in powering large language models (LLMs). The article focuses on the exploitative labour ecosystem behind data labelling and the use of algorithmic decision-making models in digital labour platforms. In particular, the article highlights the lack of transparency and the challenges faced by workers in terms of job stability and wages, as well as the need for better governance and ethical practices in this sector.
In a related article, the IAPP discusses how adopting a human in the loop strategy for AI oversight should not be considered a catch-all solution for potential system risks. Humans are fallible, inherently biased, and can compound technological errors produced by AI instead of mitigating them. The article outlines the context in which humans in the loop can help to solve AI risks, providing that the loop is clearly defined and the right person is chosen for the role.
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