In this post, I aim to summarize one common view on AI transparency and AI reliability. It’s difficult to identify the field’s “consensus” on AI transparency and reliability, so instead I will present a common view so that I can use it to introduce a number of complications and open questions that (I think) warrant further investigation.

Here’s a short version of the common view I summarize below:

Black box testing can provide some confidence that a system will behave as intended, but if a system is built such that it is transparent to human inspection, then additional methods of reliability verification are available. Unfortunately, many of AI’s most useful methods are among its least transparent. Logic-based systems are typically more transparent than statistical methods, but statistical methods are more widely used. There are exceptions to this general rule, and some people are working to make statistical methods more transparent.

The value of transparency in system design

Nusser (2009) writes:

…in the field of safety-related applications it is essential to provide transparent solutions that can be validated by domain experts. “Black box” approaches, like artificial neural networks, are regarded with suspicion – even if they show a very high accuracy on the available data – because it is not feasible to prove that they will show a good performance on all possible input combinations.

Unfortunately, there is often a tension between AI capability and AI transparency. Many of AI’s most powerful methods are also among its least transparent:

Methods that are known to achieve a high predictive performance — e.g. support vector machines (SVMs) or artificial neural networks (ANNs) — are usually hard to interpret. On the other hand, methods that are known to be well-interpretable — for example (fuzzy) rule systems, decision trees, or linear models — are usually limited with respect to their predictive performance.¹

But for safety-critical systems — and especially for AGI — it is important to prioritize system reliability over capability. Again, here is Nusser (2009):

strict requirements [for system transparency] are necessary because a safety-related system is a system whose malfunction or failure can lead to serious consequences — for example environmental harm, loss or severe damage of equipment, harm or serious injury of people, or even death. Often, it is impossible to rectify a wrong decision within this domain.

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1. Quote from Nusser (2009). Emphasis added. The original text contains many citations which have been removed in this post for readability. Also see Schultz & Cronin (2003), which makes this point by graphing four AI methods along two axes: robustness and transparency. Their graph is available here. In their terminology, a method is “robust” to the degree that it is flexible and useful on a wide variety of problems and data sets. On the graph, GA means “genetic algorithms,” NN means “neural networks,” PCA means “principal components analysis,” PLS means “partial least squares,” and MLR means “multiple linear regression.” In this sample of AI methods, the trend is clear: the most robust methods tend to be the least transparent. Schultz & Cronin graphed only a tiny sample of AI methods, but the trend holds more broadly. ↩