I would say interpretability is the ability of humans to understand the output of artificial intelligence (AI). Explainability of AI is the ability to understand how the AI arrived at the results or the output. This is the main reason several scholars are calling for designing AI systems in a white box setting that is transparent and will be human-understandable, as opposed to the largely current black box design models. I wish you all the best in your research.

Dr. Daniel Deng Malok.

I would argue that interpretability is our ability to understand the results generated by artificial intelligence (AI), while explainability allows us to understand how those results were achieved—the processes, decisions, and inferences that shape each output. This distinction is essential: it transforms AI from a “black box” into a tool with which we can dialogue, learn, and build trust. Human presence in the decision-making cycle remains indispensable.

We are still exploring the most efficient, safe, and reliable ways to interact with intelligent systems, and it is in this space of uncertainty that our perception, contextual judgment, and creativity come into play. It's not about competing with AI, but about complementing its capabilities, guiding its analyses with ethical discernment, intuition, and sensitivity to nuances that machines cannot yet grasp.

By designing transparent and understandable systems, we create opportunities for deeper collaboration: humans and machines working together, enhancing results, expanding horizons, and ensuring that complex decisions remain aligned with human values. AI can accelerate calculations and analyze large volumes of data, but human reflection, interpretation, and creativity remain irreplaceable. I wish you all much success in your research, and may these insights inspire projects that integrate humans and AI in a balanced, ethical, and productive manner, transforming collaboration into a true source of learning and innovation.

Iraê C. Brandão