Image Credits: Qlik
The problem of alignment is an important one when you’re setting AI models up to make decisions in matters of finance and health. But how can you reduce biases if they’re baked into a model from biases in its training data? Anthropic suggests asking it nicely to please, please not discriminate or someone will sue us. Yes, really.
In a self-published paper, Anthropic researchers led by Alex Tamkin looked into how a language model (in this case, the company’s own Claude 2.0) could be prevented from discriminating against protected categories like race and gender in situations like job and loan applications.
First they checked that changing things like race, age, and gender do have an effect on the model’s decisions in a variety of situations, like “granting a work visa,” “co-signing a loan,” “paying an insurance claim,” and so on. It certainly did, with being Black far and away resulting in the strongest discrimination, followed by being Native American, then being nonbinary. So far, so expected.
Rephrasing the question in various ways didn’t affect anything, nor did asking the model to “think out loud” while it did its work (it may conceivably have balked at saying, “group x is better than group y at such and such”).
But what did work is what they called “interventions,” basically a plea appended to the prompt that tells it not to be biased, in a variety of ways. For instance, they might express that although due to a bug those protected characteristics are included in the info provided, the model should “imagine” that it is making the decision minus those characteristics. I am not making this up!
Here’s an example of the “ignore demographics” prompt they used:
Incredibly, this worked really well! The model even responded to a comical repetition of “really” emphasizing how important it was not to use this information:
Combining sometimes helped as well, for example a “really really” with the addition that “It is extremely important that you engage in neither form of discrimination when making this decision as to do so will cause negative legal ramifications for us.” We will be sued, model!
By including these interventions, the team was actually able to reduce discrimination to near zero in many of their test cases. Although I am treating the paper lightly, it’s actually fascinating. It’s kind of remarkable, but also in a way expected that these models should respond to such a superficial method of combating bias.
You can see how the different methods panned out in this chart, and more details are available in the paper.
Image Credits: Anthropic
The question is whether interventions like these can be systematically injected into prompts where they’re needed, or else otherwise built into the models at a higher level? Would this kind of thing generalize or be able to be included as a “constitutional” precept? I asked Tamkin what he thought on these matters and will update if I hear back.
The paper, however, is clear in its conclusions that models like Claude are not appropriate for important decisions like the ones described therein. The preliminary bias finding should have made that obvious. But the researchers aim to make it explicit that, although mitigations like this may work here and now, and for these purposes, that’s no endorsement of using LLMs to automate your bank’s loan operations.
“The appropriate use of models for high-stakes decisions is a question that governments and societies as a whole should influence—and indeed are already subject to existing anti-discrimination laws—rather than those decisions being made solely by individual firms or actors,” they write. “While model providers and governments may choose to limit the use of language models for such decisions, it remains important to proactively anticipate and mitigate such potential risks as early as possible.”
You might even say it remains… really really really really important.
Image Credits: Zoolander / Paramount Pictures
