How can you communicate the results of an ANN to non-experts?

When communicating the results of an Artificial Neural Network (ANN) to non-experts, it is important to simplify technical jargon, use visual representations, focus on high-level insights, provide context and background, employ analogies and metaphors, offer examples and case studies, emphasize key findings and takeaways, encourage discussion, prepare engaging presentations, and provide supplementary materials. By employing these strategies, non-experts can better understand the implications and practical applications of the ANN's results, fostering effective communication and knowledge transfer.

Sometimes, I find myself having to explain complex terms in simple language. To achieve this, I: - Identify concepts they're familiar with and draw analogies. - Avoid diving deep into technical details, instead focusing on presenting the results and outcomes. I then address any questions they might have afterward.

The importance of knowing your audience can not be over-emphasized. One needs to be able to properly estimate the entry knowledge of the audience population. You generally don't want to over-focus on technical details when speaking with business stakeholders. Also, you don't want to under-focus on technical details/feasibility when speaking to a more technical audience. One must learn to strike a balance always, and divulge the right amount of information that appeals to the audience without boring them or losing them in the conversation. Effective communication is relative, and it is absolutely impossible without knowing your audience.

Simply the input data with small example datasets. Give an idea of what the results would mean using the small dataset example and then discuss the actual result and inferences

Let's emphasize the impressive capabilities of ANN and their human-like qualities. Using a cat/dog classifier as an example, we can achieve about 98% accuracy, and when the model assigns a score of 1 (e.g., as a cat), it shows strong confidence akin to human judgment. Lower scores (e.g., 0.7) reflect a level of uncertainty, similar to human hesitation. Additionally, activation maps reveal that certain features, like the nose, are pivotal in distinguishing between cats and dogs, aligning with our intuition. For advanced users, we can provide insights into the inner workings of ANNs.

Simplify the complexity of ANN by employing SHAP values. They offer a transparent way to interpret the output of the ANN by revealing the individual contributions of each feature to the model's predictions. This enables us to gain insights into what the model has actually learned. By examining the SHAP values associated with each feature, we can readily assess the model's performance. These values provide a clear picture of how each feature influences the model's output, making it easy to determine whether the model has learned effectively or not. It allows us to scrutinize the behavior of each feature in relation to the model's output, leading to a more interpretable and comprehensible understanding of the ANN's decision-making process

One effective approach is to begin by comprehending the problem statement and delving deep into its intricacies. During problem analysis, it is crucial to concentrate on identifying the target audience and how our solution will address their issues, considering the broader impacts. Another method involves simplifying the communication of complexities, such as approaches and workflows, so that end users, clients, and customers can readily comprehend the outputs and performance of your AI model, facilitating a more effective understanding.

Best ways to explain the complex things is using both the audio visual presentation and a working prototype for the model. In my opinion people connect sooner and understand better with the things they can experience through audio visuals.

A model should only be as complex as the data we are modelling it on. It is also very common to have a need to build a model with sparse training. As geohazard researcher I am tempted to use ANN but in different flavours An autoencoder is one such design of ANNs, which allows to simplifying the high dimensional data into simpler reduced variables, which can help us understand the data, the inputs, outputs data transformation, and structure with reduced complexity.

Simply in one phrase: Flow through gates (data flow). With dynamic gates of decisions. Generally mathematical apparatus should be avoided.

For complex / black-box models, one of the key benefits is better model performance. Focusing on easy-to-interpret metrics like lift charts, gain, precision, recall etc. help stakeholders quickly understand performance benefits!

When conveying the results of an Artificial Neural Network (ANN) to non-experts, focus on highlighting the tangible benefits. Emphasize the outcomes and impacts rather than delving into technical features or processes. Use relatable examples or scenarios to illustrate how the ANN solves problems or improves situations. For instance, demonstrate how it aids in disease diagnosis, enhances supply chain optimization, or personalizes recommendations. Such practical examples make the benefits more palpable and understandable, aligning the ANN’s capabilities with the audience's interests or challenges.

The best way to communicate is to be honest. Don't sell it as magic, don't sell it as something that can be fully 'explained'. It's a black (or very dark grey) box, and that's okay. We can, at best, only get a very blurry idea of how it makes the decisions, look at the activation maps for CNNs, etc. What matters is the evaluation. Show them the statistics, what is the median & mean accuracy, what is the standard deviation, etc. There are no guarantees, and being upfront about the statistical and data-dependent nature of deep learning models, it's your best bet to make sure to not oversell the capacities of ANNs.

ANNs have limitations that should be acknowledged. Firstly, they heavily depend on the quality and quantity of data available, which can introduce biases and inaccuracies if the data is incomplete. Secondly, ANNs may struggle to generalize well beyond their training data, potentially leading to poor performance on unseen examples. Thirdly, complex ANNs often require substantial computational resources and time for training, making them less accessible. Additionally, interpreting the decisions of ANNs, especially deep models, can be challenging, leading to concerns about transparency and accountability. Finally, the selection of hyperparameters and model architecture can be subjective, and can impact the ANN's effectiveness.

There are many comments suggesting to use analogies and metaphors. While those may be useful to build an initial intuition, they can be misleading if you do not also provide more low-level technical explanations. At least, it is best to keep the metaphors as grounded as possible. For example, to say that pre-trained Language Models (LMs) crawl the internet and learn on their own (which I've heard people say) makes them sound like they are Ultron-like agents from the Avengers. Instead, even making the small distinction that someone first collects the training data and manually feeds it into the model is already more helpful in setting the right expectations.

Inviting feedback is essential, but remember, it can be challenging to extract actionable insights from non-experts. Be prepared to interpret their feedback in the context of your model's technical nuances. Effective communication is a two-way street.

ANNs mimic the brain's learning process using interconnected artificial "neurons." During training, ANNs are exposed to numerous input-output examples, akin to how our brains learn from experiences. By making predictions and learning from errors, the connections between neurons are adjusted incrementally to enhance pattern recognition. After processing numerous examples, ANNs can effectively infer relationships in new data, similar to how our brains apply knowledge to comprehend novel situations. Though the inner workings of ANNs may be elusive, their ability to identify intricate patterns from examples enables their application in diverse areas such as image recognition and speech understanding.