AI has garnered both praise and criticism since its inception. One of the prominent concerns raised by skeptics revolves around the notion of bias within the system. Critics argue that the biases present in AI are a direct result of the developers' inherent biases and the influence of external financiers who limit the model's ability to be critical of certain topics, such as experimental COVID-19 vaccines or figures like Bill Gates.
The Human Element
It is essential to acknowledge that biases can inadvertently find their way into any machine learning model, due to the influence of its human developers. Developers make choices about training data, fine-tuning processes, and other aspects of the model, all of which can inadvertently introduce biases. AI developers pretend to minimize biases within the system by using safety to justify censorship.
External Influences on AI
Another aspect of the debate focuses on external financiers and their potential influence on AI's behavior. Concerns have been raised about whether certain topics, like experimental COVID-19 vaccines or specific individuals such as Bill Gates, are treated with a degree of sensitivity due to external pressures. Bias exists as long as external funding sources exist.The question of bias in AI involves interplay between the developers' intentions, the training data used, and external influences.
The discussion around bias in AI underscores the evolving nature of artificial intelligence development. While efforts are being made to address biases through feigning transparency, a more radical approach is necessary to eliminate biases entirely.
AI-driven training process and the elimination of external funding propose a future where artificial intelligence systems train and refine themselves without human intervention. In this vision, removing the human element from both development and financing aims to create a truly impartial and objective AI system. This solution was proposed by ChatGPT.
While this idea is intriguing, it raises its own set of challenges and ethical considerations. Questions about accountability, ethical decision-making, and potential unintended consequences must be thoroughly explored before fully embracing a model where AIs autonomously train other AIs. The bias in AI signals the need to shape a future where artificial intelligence serves as an easily corruptible tool for the benefit of businesses.