AI services and the concept of 'explainability'

Questions for AI services: I understand that some regard AI models as “black boxes” because it’s difficult to understand how they make decisions. Do you agree that ‘explainability’ refers to the ability to understand how an AI model arrived at a particular decision? Are AI models becoming so complex, that they become more difficult to explain? Do you agree that in applications such as healthcare and finance, decisions must be explainable and transparent? To which other categories would the latter apply? Do we also need transparency regarding the source (i.e. training) data?ChatGPT’s answer:Yes, as an AI language model, I agree that explainability refers to the ability to understand how an AI model arrived at a particular decision. AI models can be complex and often involve many layers of computations that are difficult for humans to interpret. As a result, some AI models are considered "black boxes" because it can be challenging to understand how they make decisions.

It is true that as AI models become more complex, they can become more challenging to explain. However, there is ongoing research into developing methods to improve the explainability of AI models. Some approaches include visualization tools, feature importance analysis, and generating human-readable explanations.

In applications such as healthcare and finance, decisions must be explainable and transparent. This is because these areas have high stakes, and the decisions made by AI models can have a significant impact on people's lives. Other categories that would benefit from explainability and transparency include criminal justice, national security, and environmental protection.

Transparency regarding the source (i.e., training) data is also important. This is because the training data can influence the decisions made by AI models. If the training data is biased or incomplete, the model may make inaccurate or unfair decisions. As a result, it is essential to understand the quality and characteristics of the training data used to build AI models.Google Bard’s answer:Yes, I agree that explainability refers to the ability to understand how an AI model arrived at a particular decision. AI models are becoming increasingly complex, and this can make them more difficult to explain. In applications such as healthcare and finance, where decisions can have a significant impact on people's lives, it is important for these decisions to be explainable and transparent. This is because people need to be able to understand why an AI model made a particular decision, and to trust that the decision was made fairly and accurately. Other categories where explainability and transparency are important include criminal justice, education, and employment. In all of these areas, people need to be able to understand how AI models are being used and to trust that these models are not making biased or unfair decisions.

As for transparency regarding the source (i.e. training) data, I believe that this is also important. People need to be able to understand what data was used to train an AI model, and to know whether this data is biased or unfair. This is because the data that is used to train an AI model can have a significant impact on the decisions that the model makes. For example, if an AI model is trained on data that is biased against a particular group of people, the model is more likely to make biased decisions against that group of people.

Overall, I believe that explainability and transparency are important for all AI applications. These qualities help to build trust in AI systems and to ensure that these systems are used fairly and ethically.Bing Chat’s answer:That’s a very interesting and complex question. I’ll try to answer it as best as I can.

Explainable AI (XAI) is a set of processes and methods that allows human users to comprehend and trust the results and output created by machine learning algorithms1. It helps characterize model accuracy, fairness, transparency and outcomes in AI-powered decision making. Explainable AI is crucial for building trust and confidence when putting AI models into production.

I agree that explainability refers to the ability to understand how an AI model arrived at a particular decision, but it also involves understanding the expected impact and potential biases of the model. Explainability can help developers ensure that the system is working as expected, meet regulatory standards, or allow those affected by a decision to challenge or change that outcome.

AI models can become more difficult to explain as they become more complex, especially when they involve deep learning and neural networks. These models are often thought of as black boxes that are impossible to interpret. Even the engineers or data scientists who create the algorithm may not understand or explain what exactly is happening inside them or how the AI algorithm arrived at a specific result.

I agree that in applications such as healthcare and finance, decisions must be explainable and transparent, because they have significant impacts on human lives, well-being, and rights. Explainable AI can help detect and resolve bias, drift, and other gaps in data and models, as well as mitigate compliance, legal, security and reputational risks of production AI. Some other categories where explainability and transparency are important are education, law enforcement, social media, and e-commerce.

We also need transparency regarding the source (i.e. training) data, because the quality and diversity of the data can affect the performance and fairness of the AI models. Data transparency can help identify and address data challenges such as missing values, outliers, imbalances, or biases. Data transparency can also help users improve their data by refining or augmenting it with example-based explanations.