The Paradox of Neural Networks: Simplicity, Complexity, Utility, and Explainability

Introduction

In the active discipline of artificial intelligence, neural networks have emerged as a formidable tool, capable of learning from extensive data and making precise predictions. However, these networks' nature, simplicity, utility, and complexity often spark intriguing questions and debates. One such discussion revolves around the necessity of understanding the intricate details of how a model works, known as explainability, versus simply using the model because it produces good results, referred to as utility.

The Simplicity and Complexity of Neural Networks

Neural networks are computational models based on the biological neural networks of animal brains. A neural network consists of interlinked nodes (neurons) that perform primary mathematical operations on input data. This simplicity forms the basis of neural networks, allowing them to process and systematically learn from data. Regardless, while the individual operations within a neural network are simple, the whole behavior of a neural network is complex. The complexity emerges from the interactions between the multiple neurons and the learning process, which involves modifying the weights based on the error of the network's output.

The Utility of Neural Networks and the Cost of Explainability

The utility of neural networks is definite. We have used neural networks to advance significantly in various areas, from image and speech recognition to natural language processing and autonomous driving. Nevertheless, their utility often comes at the expense of explainability. Due to their complexity, neural networks can perform as "black boxes," making outputs without evidently comprehending how they arrived at those outputs. This lack of interpretability can be formidable, mainly in domains where accountability and understanding the decision-making process are crucial, such as healthcare and finance.

For example, assume a case where a bank uses a system driven by a neural network to determine eligibility for a loan. If the model rejects a loan application, the client has the legal right to know why the bank declined their application. However, due to the complexity of the neural network, it may take time to provide an evident reason. This lack of explainability can conduct in legal claims and reputational risk for the bank.

Similarly, if a healthcare institution uses a system based on a neural network model that inaccurately diagnoses a patient, the outcomes could be life-threatening. With explainability, it would be more straightforward to comprehend what conducted to the misdiagnosis and how to prevent it in the future.

The Trade-off Between Accuracy and Explainability

The back-and-forth between explainability and utility is rooted in the theoretical underpinnings of machine learning and is not just a philosophical debate. Then, an intrinsic trade-off exists between a model's accuracy and interpretability. Simple models like decision trees or linear regression are straightforwardly interpretable but may not offer the most accurate predictions for complex problems. By contrast, complex models, for example, neural networks, can deliver highly accurate predictions, but they often need to be interpretable.?

However, explainability may only be as critical in some domains. The balance may tip more towards the utility in lower-risk applications, such as recommendation systems for music or movies. Users are typically more interested in the quality of the recommendations rather than understanding why the system recommended a particular song or movie.

Moreover, it's important to note that the debate between explainability and utility is not binary. There are intermediate solutions, such as developing localized explanations for parts of a model or using techniques like LIME (Local Interpretable Model-Agnostic Explanations) or SHAP (Shapley Additive exPlanations), which can provide insights into the model's decision-making process without requiring full explainability.

Conclusion

To summarize, with their simple mathematical operations, complex structures, and high utility, neural networks present a fascinating paradox. The debate between explainability and utility has yet to be readily resolved and likely depends on the specific application and its requirements. As we continue to advance in artificial intelligence, discovering a balance between these two aspects will be crucial to harnessing the full potential of neural networks while ensuring responsible and informed decision-making.

NB: Paulo Manrique 's post inspired this essay. His post discusses the nature of neural networks. It highlights how these networks perform basic mathematical operations with relative simplicity but achieve complexity and utility by combining these operations with data-based learning. It also raises the philosophical dilemma of whether understanding these aspects is necessary to use neural networks effectively.