The debate over the explainability of artificial intelligence (AI) systems continues to grow as the technology becomes more advanced and is applied to various domains such as healthcare, hiring, and criminal justice. Some argue that the “black box” nature of modern machine learning models makes them unaccountable and potentially dangerous, leading to calls for more transparency and interpretability. However, the importance of AI explainability is often overstated, as a lack of explainability does not necessarily equate to unreliability or unsafety.
Even the creators of cutting-edge deep learning models struggle to fully articulate how these models transform inputs into outputs due to the complexity of neural networks trained on millions of examples. Yet, this level of understanding is not required for many other technologies we use daily, such as pharmaceuticals and microchips, where partial knowledge suffices as long as the outputs meet their objectives and are reliable. When it comes to high-stakes AI systems, the focus should be on testing them to validate performance and ensure they behave as intended, rather than solely on understanding the inner workings of the algorithm.
AI interpretability, an emerging field, aims to shed some light on the black box of deep learning by identifying salient input features and characterizing how information flows through neural networks. While these techniques may provide some insights into how AI models arrive at their predictions, it is unrealistic to expect AI systems to be fully explainable like simple equations or decision trees. Irreducible complexity is likely to be a feature of the most powerful AI models, and that should not deter us from benefiting from their outputs.
Explainability should not be fetishized to the detriment of other priorities, as an AI system that is easily interpretable by humans is not necessarily more robust or reliable than a black box model. Trade-offs between performance and explainability may exist, and ultimately, what matters is the real-world impact of AI systems. While striving to make AI systems interpretable where possible, the focus should be on evaluating their effectiveness in delivering results that align with human values.
Despite the importance of accountability in AI development, developers should not let abstract notions of explainability become a distraction or obstacle to realizing the vast potential of artificial intelligence to enhance our lives. With appropriate precautions taken, even a black box model can be a powerful tool for good if its outputs are beneficial and aligned with human values. In the end, it is the outcome that matters, not the level of explainability of the process that led to it.
