In general, the black box AI approach is typically used in deep neural networks, where the model is trained on large amounts of data and the internal weights and parameters of the algorithms are adjusted accordingly. Such models are effective in certain applications, including image and speech recognition, where the goal is to accurately and quickly classify or identify data.

Black box AI and white box AI are different approaches to developing AI systems. The selection of a certain approach depends on the end system's specific applications and goals. White box AI is also known as explainable AI or XAI.

XAI is created in a way that a typical person can understand its logic and decision-making process. Apart from understanding how the AI model works and arrives at particular answers, human users can also trust the results of the AI system. For all these reasons, XAI is the antithesis of black box AI.

While the input and outputs of a black box AI system are known, its internal workings are not transparent or difficult to comprehend. White box AI is transparent about how it comes to its conclusions. Its results are also interpretable and explainable, so data scientists can examine a white box algorithm and then determine how it behaves and what variables affect its judgment.

Since the internal workings of a white box system are available and easily understood by users, this approach is often used in decision-making applications, such as medical diagnosis or financial analysis, where it's important to know how the AI arrived at its conclusions.

Explainable or white box AI is the more desirable AI type for many reasons.

One, it enables the model's developers, engineers and data scientists to audit the model and confirm that the AI system is working as expected. If not, they can determine what changes are needed to improve the system's output.

Two, an AI system that's explainable allows those who are affected by its output or decisions to challenge the outcome, especially if there is the possibility that the outcome is the result of inbuilt bias in the AI model.

Third, explainability makes it easier to ensure that the system conforms to regulatory standards, many of which have emerged in recent years -- the EU's AI Act is one well-known example -- to minimize the negative repercussions of AI. These include risks to data privacy, AI hallucinations resulting in incorrect output, data breaches affecting governments or businesses, and the spread of audio or video deepfakes leading to the spread of misinformation.

Finally, explainability is vital to the implementation of responsible AI. Responsible AI refers to AI systems that are safe; transparent; accountable; and used in an ethical way to produce trustworthy, reliable results. The goal of responsible AI is to generate beneficial outcomes and minimize harm.

Here is a summary of the differences between black box AI and white box AI: