Tag Archive for: privacy

Feeding the Cuckoo

Large Language Models (LLMs) combine words that frequently appear in similar contexts in the training dataset; on this basis, they predict the most probable word or sentence. The larger the training dataset, the more possible combinations there are, and the more ‘creative’ the model appears. The sheer size of models such as GPT-4 already provides a competitive advantage that is hard to match: There are only a handful of companies in the world that can combine exorbitant computing power, availability of big data and an enormous market reach to create such a product. No research institutions are involved in the current competition, but the big tech companies Microsoft, Meta and Google are. However, few players and few models also mean a “race to the bottom” in terms of security and ethics, as the use of big data with regard to LLMs most often also means that the training data contains sensitive and confidential information as well as copyrighted material. In numerous court cases, the tech giants have been accused of collecting the data of millions of users online without their consent and violating copyright law in order to train AI models.

Internet users have therefore already helped to feed the cuckoo child. Google published this fact indirectly by updating its privacy policy in June 2023: “use publicly available information to help train Google’s AI models and build products and features like Google Translate, Bard, and Cloud AI capabilities.” Less well known, however, is the fact that the big tech companies also train their models, such as Bard, with what users entrust to them. In other words, everything you tell a chatbot can in turn be used as training material. In Google’s own words, it sounds like this: “Google uses this data to provide, improve, and develop Google products, services, and machine-learning technologies.” One consequence of the design of LLMs, however, is that the output of generative models cannot be controlled; there are simply too many possibilities with large models. If the LLM was and is trained on private or confidential data, this can lead to these data being disclosed and confidential information being revealed. Therefore, the training data should already comply with data protection regulations, which is why there are repeated calls for transparency with regard to training data.

Consequently, in its Bard Privacy Help Hub, Google warns users of the model not to feed it with sensitive data: “Please don’t enter confidential information in your Bard conversations or any data you wouldn’t want a reviewer to see or Google to use to improve our products, services, and machine-learning technologies.” This is interesting insofar as the AI hype is fuelled by terms such as ‘disruption’, but at the same time it remains unclear what the business model with which big tech companies want to generate profits in the medium term should look like – and what exactly the use case should look like for average users. One use case, however, is the generation of texts that are needed on a daily basis, namely well-formulated application letters. However, if you upload your own CV for this purpose, you’re just feeding the cuckoo again. And that is not in our interest: After all, privacy is (also) a commons.