3 Ways to Tame ChatGPT

3 Ways to Tame ChatGPT

This year, we’ve seen the introduction of powerful generative AI systems that have the ability to create images and text on demand. 

At the same time, regulators are on the move. Europe is in the middle of finalizing its AI regulation (the AI Act), which aims to put strict rules on high-risk AI systems. Canada, the UK, the US, and China have all introduced their own approaches to regulating high-impact AI. But general-purpose AI seems to be an afterthought rather than the core focus. When Europe’s new regulatory rules were proposed in April 2021, there was no single mention of general-purpose, foundational models, including generative AI. Barely a year and a half later, our understanding of the future of AI has radically changed. An unjustified exemption of today’s foundational models from these proposals would turn AI regulations into paper tigers that appear powerful but cannot protect fundamental rights.

ChatGPT made the AI paradigm shift tangible. Now, a few models—such as GPT-3, DALL-E, Stable Diffusion, and AlphaCode—are becoming the foundation for almost all AI-based systems.  AI startups can adjust the parameters of these foundational models to better suit their specific tasks. In this way, the foundational models can feed a high number of downstream applications in various fields, including marketing, sales, customer service, software development, design, gaming, education, and law. 

While foundational models can be used to create novel applications and business models, they can also become a powerful way to spread misinformation, automate high-quality spam, write malware, and plagiarize copyrighted content and inventions. Foundational models have been proven to contain biases and generate stereotyped or prejudiced content. These models can accurately emulate extremist content and could be used to radicalize individuals into extremist ideologies. They have the capability to deceive and present false information convincingly. Worryingly, the potential flaws in these models will be passed on to all subsequent models, potentially leading to widespread problems if not deliberately governed.

The problem of “many hands” refers to the challenge of attributing moral responsibility for outcomes caused by multiple actors, and it is one of the key drivers of eroding accountability when it comes to algorithmic societies. Accountability for the new AI supply chains, where foundational models feed hundreds of downstream applications, must be built on end-to-end transparency. Specifically, we need to strengthen the transparency of the supply chain on three levels and establish a feedback loop between them.

Transparency in the foundational models is critical to enabling researchers and the entire downstream supply chain of users to investigate and understand the models’ vulnerabilities and biases. Developers of the models have themselves acknowledged this need. For example, DeepMind’s researchers suggest that the harms of large language models must be addressed by collaborating with a wide range of stakeholders building on a sufficient level of explainability and interpretability to allow efficient detection, assessment, and mitigation of harms. Methodologies for standardized measurement and benchmarking, such as Standford University’s HELM, are needed. These models are becoming too powerful to operate without assessment by researchers and independent auditors. Regulators should ask: Do we understand enough to be able to assess where the models should be applied and where they must be prohibited? Can the high-risk downstream applications be properly evaluated for safety and robustness with the information at hand?

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