Why Neural Networks' Conclusions Are A Black Box

This is a question that comes up a lot. It makes sense to ask; we are conditioned to think of computers and programs as being entities that follow very specific logic flows, capable of generating detailed records about the paths they take while performing operations. Yet, this is not so in the realm of neural networks. Why? Because once trained and operational, they function in many ways similar to the human brain.

Recalling the Essential: How Memory Shapes Wisdom

Picture yourself confidently advising others never to touch a hot stove. Your conviction is rooted in an experience—a painful burn from years ago. Yet, when it comes to the details of that event, certain specifics have faded:

  • Do you remember the exact date when you touched the stove?
  • Or the precise time of day?
  • What about the weather outside, the clothes you wore, or what you had eaten that day?

These particulars are no longer relevant to the wisdom you gained; they have been distilled down to one inferential lesson: a hot stove means potential harm.


Our brains excel at inferring rules and applying them without recalling every detail of the learning experience. This selective memory is efficient—it allows us to remember what matters most for future decisions while discarding extraneous details. This is how neural networks function. They absorb information from data and distill it into patterns, much like how you’ve distilled the experience of burning your hand. Neural networks create a complex map of weighted connections from the data, but like our memory, they don’t retain the specifics once they’ve learned the lesson. It's important to note, however, that despite a growing public perception of these systems as 'plagiarism machines', they don’t remember everything they see. While outputs may sometimes appear heavily sourced from training examples, this is due to statistical patterns, not classic memorization.


So, when you tell someone the stove is hot, you don't need to prove it with the date, time, or weather conditions from your past experience. It's enough to know that touching it is dangerous.

The Transparency Challenge in AI


Because we are placing trust in machines, we crave the transparency that is often lacking in what is called a "black box" system. If a neural network determines a patient has a particular disease, doctors and patients understandably want to know why it reached that conclusion.


The complexity of neural networks makes this transparency difficult. They are not equipped to recall every 'weather condition' or 'time of day' from the data they were trained on. They can tell us the 'stove is hot,' but they can't easily recount the details that led to that knowledge.

Bridging the Gap

This challenge and need for specifics has given rise to the field of explainable AI (XAI), which seeks to bridge the gap between the inferential wisdom of neural networks and the human desire for detailed explanations. The goal is to create models that can not only predict with high accuracy but can also recount the details of their learning process, akin to recalling the full context of the day you burned your hand on the stove.


The quest continues to find a balance between leveraging the powerful inferential capabilities of neural networks and satisfying our need for detailed, transparent explanations. For now, we accept the mysterious nature of these artificial minds, much as we accept the complexities of our own cognition. 


The neural network, like a person who has learned a lesson but can’t recall every detail, holds onto the essence of the experience. And perhaps, in the journey to make AI more explainable, we might also uncover more about how our own memories and inferences work.

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Pete Slade
November 27, 2023