Machine learning has applications within just about every vertical, from demand forecasting in retail to diagnosing cancer for medical patients. Typically, these machine learning models output their results as a probability of a certain outcome. It might say, for instance, that a particular patient has a 72% chance of lung cancer from looking at their CT scan. 

Imagine yourself as a patient in this situation, and you can probably see where the problem is. If you found out you had a 72% chance of having cancer, you would undoubtedly want to know why the machine learning system thought that. 

In theory, that’s where Explainable AI (XAI) comes in. XAI would allow someone like a patient or doctor to read a report that explained exactly how the machine learning model came to its conclusion. In practice, however, most machine learning algorithms operate as a ‘black box’, meaning that there is no way for human beings to understand how they came to their conclusions. 

There are two key reasons humans have a hard time understanding machine learning models. First, the ‘factors’ that are used by these models usually have no analogue in human thinking. (WARNING: massive simplification ahead) These models typically use a very large number of ‘neurons’ that each separately try to learn an element of the prediction problem. These neurons are randomly assigned to do some calculation, and over time the model iterates until these neurons start to do better at creating an output. Unfortunately, because these neurons are often just a simple calculation along with some numerical weights, they can’t really be described in human terms. 

When predicting retail sales, for instance, the machine learning model might end up creating a neuron that tends to heavily weight recent sales values when predicting upcoming values. It wouldn’t actually have a name in the model corresponding to its role, like “recent sales factor neuron.” Rather, it would be some random calculation that just happens, in general, to end up weighing recent sales heavily. Now, a data scientist might be able to look at an individual neuron and roughly figure out what it is doing, but remember there are often 1,000 or more neurons, and models often include several ‘layers’ of these neurons on top of each other, which start to make the role of any individual neuron hopelessly opaque.

One way data scientists have tried to get around this is by using heat maps. When looking at an automated cancer diagnosis, for example, an XAI system can highlight on the CT image where the model is placing the most weight. This is somewhat helpful, but ultimately insufficient because of how all the factors in the model come together. For instance, the model may be concerned with a particular group of pixels in the CT scan, but only because of the relationship between those pixels and another group in a totally separate part of the scan. In that case, it is really the combination of pixels that is important, but the heat map has no way of showing that. 

This brings us to the second big problem, which is that even if you could correctly describe the actions of each individual neuron, how could you hope to actually synthesize that information into something that would be easy for a human being to understand? A model with 100+ factors is complicated enough without taking into account that each of those factors is interacting with each other.

This is where infoSentience’s Fractal Synthesis technology can make a huge difference. In order to understand how Fractal Synthesis works, we first need to take a step back and look at how infoSentience’s technology works in general (how meta, right?). infoSentience has created technology that can analyze any data set, figure out what is most important, and explain what it found using natural language. Critically, this is system is flexible across four key dimensions:

Having this level of flexibility gives the system the ability to report not just on a given data set overall, but on any individual component or sub-set within the data. That’s why we call it Fractal Synthesis- it’s able to apply its algorithms and generate in-depth reports at any level of specificity. 

For example, for a given retail data set it could create a three-paragraph report on the top-level results, which might include mentioning that a particular department had done well. If the user was interested in learning more about that department they could create a brand new three-paragraph report just on that department. If an interesting metric was mentioned in that department report, let’s say sales returns for example, the user could create a brand-new report just on sales returns within that department, or zoom out and look at sales returns for the entire company. 

You can probably see how this could help solve the critical problems within XAI. Machine learning models are based on hundreds of factors AND their interactions with each other. At the end of the day, these interactions sum up to a number, which might correspond to the topic sentence in a report. In order to contextualize this topic sentence, the Fractal Synthesis technology could dive into all of the factors and use its subject and length flexibility to summarize the most important factors. Since each of the factors summarized is itself made up of multiple components, a user could simply ask the system to ‘dive in’ to that factor to get a new report on its most important subcomponents. 

In order to make any of this high-level synthesis possible, the Fractal Synthesis system does need to be able to categorize the ‘work’ that each neuron is doing on its own and in combination. This is a tricky process (currently the biggest limitation of the system), and tends to vary quite a bit depending on the model being used and its targeted output. Fundamentally, however, the system plays the role of the data scientist that is able to examine the output of a single neuron and determine what, approximately, that neuron is doing. The key difference being that once it has that ‘map’ of what every neuron is doing it is capable of quickly synthesizing what the model is collectively doing and explaining that using natural language. 

Solving XAI is critical to allow  the power of machine learning models to be applied in real-world situations. It is needed because it allows us to: (1) trust AI systems by enabling us to understand and validate the decisions that they make, (2) debug AI systems more effectively by identifying the sources of errors or biases in the system, and (3) identify opportunities for improvement in AI systems by highlighting areas where the system is underperforming or inefficient. Fractal Synthesis technology could be the key  to unlocking XAI in complex machine learning models, and I’m looking forward to keeping you informed on our progress in this space.

[Note: All of the following concerns AI’s ability to write about specific data sets, something very different from ChatGPT-style natural language generators]

We all know the basics for why good writing is important in business. Decision makers want to read reports that are accurate, impactful, and easy to understand. With those qualities in mind, let’s take a look at this short paragraph:

Widgets were down this month, falling 2.5%. They were up the last week of the month, rising 4.5%.

Assuming the numbers are correct, the sentences would certainly qualify as being ‘accurate’. The metrics mentioned also seem like they would be ‘impactful’ to a widget maker. Is it ‘easy to understand’? Here’s where it gets a bit trickier. Both sentences by themselves read fine, but when you put them together, there’s something missing. The writing comes across as robotic. Ideally, we’d want the sentences to read more like this:

Widgets were down this month, falling 2.5%. The last week of the month was a bright spot though, as sales rose 4.5%.

This is conveying the exact same basic information as before: (1) monthly sales down, and (2) last week up. But the sentence contains a critical new word: though. Transition words like ‘though’, ‘however’, ‘but’, and so on play a critical role in helping our brains not just download data, but rather tell the story of what is happening in the data. In this case, the story is about how there’s a positive sign in the time series data, which is also emphasized by using the ‘bright spot’ language. 

If a manager was just reading the first example paragraph, it’s likely that they would be able to fill in the missing info. After reading the second sentence their brain would take a second and say “oh, that’s a good sign going forward in the midst of overall negative news.” But having to make the reader write the story in their head is not cost free. 

After all, it's not enough to just understand a report. Whether it’s an employee, C-suite executive, client, or anybody else, the point of a report is to give someone the information they need to make decisions. Those decisions are going to require plenty of thinking on their own, and ideally that is what you are spending your brainpower on when reading a report.  

This brings me to my alternate definition of what ‘good writing’ really means: it’s when you are able to devote your brainpower to the implications of the report rather than spending it on understanding the report.  

So what are the things that we need to allow our brain to relax when it comes to understanding data analysis? There are three main factors, which I call “The Three T’s”, and they are:

All of these things are hard enough for a human data analyst or writer to pull off (am I extra nervous about my writing quality for this post? Yes, yes I am). For most Natural Language Generation (NLG) AI systems, nailing all Three T’s is downright impossible. Each factor presents its own unique challenges, so let’s see why they are so tough.

Trusting AI

The bare minimum for establishing trust is to be accurate, and that’s one thing that computers can do very easily. Better, in fact, than human analysts. The other key to establishing trust is making sure every bit of critical information will make it into the report, and this is where AI’s have traditionally struggled. This is because most NLGs in the past used some sort of template to create their narratives. This template might have a bit of flexibility to it, so a paragraph might look like [1 – sales up/down for month] [2 – compare to last year (better or worse?)] [3 – estimated sales for next month]. Still, templates like that aren’t nearly flexible enough to tell the full story. Sometimes the key piece of information is going to be that there was a certain sub-component (like a department or region) that was driving the decrease. Other times the key context is going to be about how the movement in the subject of the report was mirrored by larger outsize groups (like the market or economic factors). Or, the key context could be about the significance of the movement of a key metric, such as whether it has now trended down for several months, or that it moved up more this month than it has in three years. 

There’s no way to create a magic template that will somehow always include the most pertinent information. The way to overcome this problem is to allow the AI system to work the same way a good human analyst does- by first analyzing every possible event within the data and then writing up a report that includes all of those events. But having this big list of disjointed events makes it that much more difficult to execute on the second ‘T’: Themes. 

Organizing the Story

How does the AI go from a list of interesting events to a report that has a true narrative through line, with main points followed by interesting context? The only way to do this is by embedding conceptual understanding into the AI NLG system. The system has to be able to understand how multiple stories can come together to create a theme. It has to be able to understand which stories make sense as a main point and which stories are only interesting as context to those main points. It also needs to be able to have the ability to fit those stories into a narrative of a given length, thereby requiring some stories to be told at a high level rather than going into all of the details. 

Writing the Story

Just like how the importance of transitions are a small-scale version of themes, the challenges for dealing with transitions are a small-scale version of themes. That doesn’t make them any easier, unfortunately. The issue with themes is structural- how do I organize all the key pieces of information? The problem with transitions comes about after everything has been organized and the AI has to figure out how to write everything up.

Again, the solution is to have an AI system that understands conceptually what it is writing about. If it understands that one sentence is ‘good’ and the next sentence is ‘bad’, then it is on the path to being able to include the transitions needed to make for good writing. Of course, it’s not quite that simple, as there are many conceptual interactions taking place within every sentence. For example, starting out a sentence with “However, …” would read as robotic if happening twice within the span of a few sentences. Therefore, the AI system needs to find a way to take into account all of the conceptual interactions affecting a given sentence and still write it up properly.

Have no Fear!

These are all complex challenges, but thankfully infoSentience has built techniques that can handle The Three T’s with no problems. Whether it’s a sales report, stock report, sports report, or more, infoSentience can write it up at the same quality level as the best human writers. Of course, we can also do it within seconds and at near infinite scale (Bill Murray voice- “so we got that going for us”).