Are Blocking Chatbots Leading To A Surge In Far Right Training Data?

In this episode, Andrew Davis and Chris Branch discuss the concerning issue of data training and the bias it can create in AI models. They explore the challenges faced by AI models like ChatGPT in accessing data from different publications and the potential for bias in the information they are trained on.   The conversation delves into the clash between publishers and tech companies, the issues of plagiarism and copyright, and the dangers of bias in data training. They also discuss the replication of societal biases by AI models and the ethical dilemma of testing AI. The need for agreement and regulation in the AI industry is emphasized, as well as the potential fragmentation of AI platforms. The episode concludes with a reminder to be mindful of information sources and the ongoing battle for digital influence between media giants and AI firms.   Takeaways Data training in AI models can lead to bias if the training data is not diverse and representative. The clash between publishers and tech companies over data access and copyright issues is a significant challenge in the AI industry. Plagiarism and copyright infringement are concerns when AI models crawl data from websites. AI models can replicate societal biases if not trained to be aware of and overcome them. The ethical dilemma of testing AI involves the potential risks and accidents that may occur during the learning process. Agreement and regulation are necessary to ensure responsible and unbiased use of AI. AI is becoming increasingly ubiquitous, and it is important to be mindful of the potential fragmentation of AI platforms. Being aware of the biases and sources of information is crucial in navigating the AI-driven world. The battle for digital influence between media giants and AI firms will shape the future of AI.