So how can we use generative AI responsibly?
The first step is to choose our use cases with care. A lot of the panic and excitement over generative AI fundamentally misses—or misunderstands—what it can and cannot do. Generative AI is not the same as artificial general intelligence, an artificial intelligence that can accomplish any task a human can perform. It’s a model, albeit a clever one, that copies what we give it. To avoid making hasty investments that don’t pan out, executives need to understand exactly what they’re dealing with.
Narayanan and Kapoor identify three kinds of tasks for which LLMs are useful: tasks where it’s easy for users to double-check the bot’s work, where factual truthfulness of the material is irrelevant, and where a subset of the training data can act as a verifiable source of truth (such as translation jobs).
Businesses are already starting to put LLMs to work on these tasks. Some of the smarter and more popular use cases for generative AI include crafting personalized marketing messages for advertising and sales, performing code reviews, parsing complex legal documentation, and performing data analytics, according to research from McKinsey. These use cases play to LLMs’ strengths. None of these tasks requires a bot to understand what’s real and what’s not.
Even responsible use cases require guardrails, however.
This year, Singapore’s newly launched AI Verify Foundation produced a white paper on the risks posed by generative AI. The paper stresses the importance of good governance, arguing that humans must take a practical, risk-based approach to trust and safety when it comes to AI. Further, to mitigate bias and encourage responsible use, developers should be open about how they build and train their models and should regularly invite third parties to check their work. This is similar to the concept of privacy by design, in which privacy and security are incorporated into the very foundation of the technology’s development rather than tacked on at the end.
Further, companies will need to understand the nature of data that they want to include in language models, including to what extent AI-produced content should play a role. According to the white paper, you could fall into an iterative spiral, where AI generates content based on previously produced AI content, which in turn was originally hallucinated by AI. The researchers warn against pulling from multiple iterations of generative cycles that end up confirming bias and reinforcing untruths.
Ultimately, it comes down to putting humans at the center of the development process. Such human-centered design will enable businesses to create AI models that serve people, not each other.
Developers should consider taking a page from Wikipedia and creating ways for LLMs to show their work. Although this isn’t a perfect solution—as we know LLMs can make up sources—creating a paper trail makes it easier for humans to double-check the bots.