Suggestions for Better AI Criticism

Although I am acutely aware of the shortcomings of the current generation of AI models (transformer-models in particular), most of the criticisms of AI I stumble upon on the internet have become repetitive and lacking insight. I am not interested in picking on anyone here, I’m interested in reading more interesting criticisms. Therefore, I will provide a list of proposals for better AI criticism.¹

My suggestions pertain to criticism of the current abilities of AI, rather than criticism of their broader social consequences (supposed harm, replacement of human activities etc.). The criticisms I have in mind are of the blog-post length and formality, not academic. The list is neither complete, nor beyond dispute. Hopefully, it serves as a source of sharpening ideas. Here are my suggestions:

• In your criticism, be specific about the architecture, or the family of architectures. Not all neural network technologies are transformer models, or even more specifically GPT models. Seek to tie your criticism back to the specifics of the architecture. For example, “as a transformer, the model lacks a bias about the order in which to process a sequence and therefore…”. The more general the target of the criticism, the stronger the argument needs to be. Unless you have an excellent argument, I advise against dismissing neural networks in general.
• Seek to distinguish whether the shortcomings are due to architecture, data, training time, or another factor. If you cannot be sure about the source, avoid claims relying on what the source is. Fewer claims are possible when the details of the model are secret, as in the case of most OpenAI model. While this is frustrating, it limits what diagnoses are warranted.
• When there are good reasons to be critical of the hype surrounding AI technologies, avoid policing of emotional reactions. It is fairly unproductive to scorn people for being impressed by what current models can do, not least because by the expectation of two decades ago, the models perform impressively. Instead, provide insight into the models and their limitations, so that people can adjust their reactions according to the reasons provided.
• Avoid reductive claims as a standalone form of criticism, i.e. claims that models are “just x”. For example, the claim that language models are just statistical models for predicting the next/masked word is on its own rather uninteresting, unless it is embedded in a larger argument. If you go down the route of the larger argument, consider whether the reductive claim is true due to the model architecture and therefore general, or only due to a specific usage of the architecture. For example, in transformers the tokens can also easily be made to stand for other elements in a sequence than words. They do not have to be just a statistical model for predicting the next word.
• When criticising examples created by models, think in terms of distributions. From where in the distribution of model output are the examples taken? Are the samples cherry-picked, that is, are they examples of especially good performance? Then, stricter criteria for their evaluation are warranted. Are they representative of the entire distribution of model output? Then, it is more appropriate to give a sense of the range of the examples. “Out of 5 examples, all showed behaviour x” is a very different statement for a cherry-picked sample of output and a more representative one. Be open about the sampling.
• When comparing to human cognitive capacities, provide evidence to support your comparison. Unchecked by evidence, we are dubious judges of our abilities. Asserting that people never make a certain sort of error — “a real person would never fail to see that…” — requires empirical data to support the claim.
• Be aware of human tendencies in processing input, in this case the output of AI models, and adjust your criticism to it. We tend to be very generous in our interpretation of text, doing our best to make sense of it. We might be less forgiving with other forms of input (e.g. video). The targeting of your AI criticism should reflect more than our human processing biases.
• Moving goal posts can be acceptable, but provide a justification for why the goal posts have to be moved. Often we put the goal posts where we believed that they would capture something deeper: Reasoning and understanding. While we might have been mistaken in that judgement, it needs to be argued why we were mistaken and why the new place for the goal post will do any better. That a model beat a goal post is, on its own, not a reason to move the post.
• Stay curious.

Footnotes