The Psychology of Understanding: How to Tailor Your Explanations to Different Audiences

As a developer or data scientist working with machine learning models or complex distributed systems, you know that explaining these systems to others can be a daunting task. While you may be comfortable with technical terminology and concepts, your audience may not be.

But don't worry - it's not impossible to bridge that gap! With a little knowledge of how different audiences process information, you can tailor your explanations to ensure that they are understood.

The Fundamentals of Understanding

It is often assumed that understanding is a straightforward process - either someone gets it or they don't. However, in reality, it's much more complex than that.

Understanding is affected by various factors, such as prior knowledge, cognitive biases, learning style, and attention. Additionally, different people have different levels of exposure to technology, which can influence their comfort level with technical concepts.

When it comes to explaining to a non-technical audience, we need to pay extra attention to how we frame our explanations. It's not enough to use technical terms and concepts; we need to put the information into a relatable and understandable context.

The Audience Matters

One of the most important factors in tailoring your explanations is understanding your audience. Just like there are different learning styles, there are also different audience styles.

Some people prefer visual aids, others prefer storytelling, and even others prefer a hands-on approach. Thus, understanding what kind of explanation style would work best for each audience is crucial.

For example, if you are explaining a machine learning model to a business stakeholder, it may be helpful to provide them with visualizations that demonstrate the model's capabilities. This will help increase their confidence in the model's accuracy while giving them a more comprehensive understanding of its use case.

However, if you are explaining the same machine learning model to a developer team, they may need more in-depth technical explanations focusing on the underlying algorithms and data structures used by the model.

Understanding your audience's background and motivations can also help tailor your explanations. For instance, if you're talking to a non-technical person who is driven by financial motivation, highlighting the cost savings of implementing an ML model can help connect the dots and make it more relevant to them.

Technical Jargon: The Good, The Bad, and The Ugly

Technical terms are an unavoidable part of working with technology. But when it comes to explaining technical concepts to a non-technical audience, jargon can be more of a hindrance than a help.

While technical jargon can convey meaning more succinctly when speaking to other professionals in the field, it can cause unnecessary confusion and misunderstanding when speaking to a layman.

Always consider your audience and try to avoid technical jargon that may be unfamiliar to them. If you must use a technical term, try to provide an explanation or context around it so that the audience understands its meaning.

Storytelling: A Narrative Approach to Explanation

As humans, we're wired for stories. Stories capture our attention and can make complex ideas easier to relate to. Because of this, storytelling can be an effective tool in explaining technical concepts to non-technical people.

By creating a clear narrative sequence and using analogies, you can frame your explanation in a way that resonates with the audience. The story can provide a framework for the explanation, with each component of the system contributing to the overall narrative.

For example, explaining how an ML model works can be done through storytelling by making an analogy of a doctor conducting a diagnosis. You can explain how the model analyses data to make predictions in the same way a doctor analyses symptoms to make a diagnosis.

Using analogies like these can go a long way in connecting new, abstract concepts with familiar ones, making the experience of learning vastly more approachable and enjoyable.

Use Visual Aids

Visual aids such as diagrams, flowcharts, and other representations can be extremely helpful in conveying complex information to non-technical people.

Visual aids provide a way for people to understand how different elements of the system relate to each other, thus making complex concepts more tangible. In addition, by making the information more accessible, visual aids can make people more engaged and increase the chances that they will remember the details.

A flowchart, for instance, can walk people through each step of how the ML model works, breaking it down into smaller, more digestible pieces.

Be Watchful of Cognitive Biases

Cognitive biases are unconscious errors in reasoning that can lead to inaccurate conclusions or unsupported beliefs. When communicating a technical concept, it's crucial to be aware of these biases and how they could influence the audience's understanding of the topic.

A common mistake can be assuming that just because you understand a concept that others do also. This bias can lead to under-explaining the concept, oversimplifying it, or making assumptions about the audience's prior knowledge.

Confirmation bias is another common bias people face when processing information. People tend to seek out information that confirms their beliefs or opinions while ignoring information that contradicts it.

While it might be difficult to match the information you're communicating perfectly to a person's preconceived notions and biases, you must frame your explanation in a way that's accurate, informative, and non-threatening.

Practice Empathy

It's essential to remember that different people absorb information at different paces and may need extra support while learning something new. Practising empathy and patience while working through an explanation can help people better retain information and feel more at ease in asking questions.

By recognizing and addressing the unique learning styles, technical abilities, and motivational factors of your audience, you'll be able to increase the efficiency of your explanation and improve the likelihood that people will retain what they've learned.

Wrapping Up

Explaining technical concepts to a non-technical audience takes time and effort, but it's an essential skill, especially when it comes to machine learning models and complex distributed systems.

By taking the time to learn more about your audience, using storytelling, analogies, visual aids, and framing your explanation in a meaningful context will allow for better comprehension and retention.

So next time, as you prepare to explain an ML model to a business stakeholder or a colleague unfamiliar with the concept, take a step back to consider your audience, frame your explanation thoughtfully, and practice empathy. It can make a huge difference in the outcome.

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