What is Explainable AI (XAI)?

Branch of AI that deals with making AI systems more transparent and understandable to humans, by providing explanations or justifications for their decisions or actions.

Artificial intelligence (AI) is the science and engineering of creating intelligent machines that can perform tasks that normally require human intelligence, such as vision, speech, reasoning, decision making, and learning. AI has been advancing rapidly in recent years, thanks to the availability of large amounts of data, powerful computing resources, and sophisticated algorithms. However, as AI becomes more complex and capable, it also becomes more difficult for humans to understand how it works and why it behaves in certain ways. This poses challenges for trust, accountability, ethics, and governance of AI systems.

Explainable AI (XAI) is a branch of AI that deals with making AI systems more transparent and understandable to humans, by providing explanations or justifications for their decisions or actions. XAI aims to address the problem of the “black box” nature of many AI models, especially those based on deep learning and neural networks, which are often hard to interpret even by the experts who create them. XAI also seeks to empower users, developers, regulators, and other stakeholders to interact with AI systems more effectively and responsibly.

Why is XAI important?

XAI is important for several reasons:

Trust: Humans are more likely to trust and use AI systems if they can understand how they work and what they do. Trust is essential for building confidence and acceptance of AI among users and society at large. Trust also enables collaboration and cooperation between humans and AI agents.
Accountability: Humans are responsible for the outcomes and impacts of AI systems, whether they are developers, operators, or users. Accountability requires that humans can monitor, audit, evaluate, and control AI systems, as well as explain their behavior and justify their actions to others. Accountability also implies that humans can correct, improve, or challenge AI systems when needed.
Ethics: Humans have ethical values and principles that guide their actions and decisions. Ethics requires that AI systems respect and align with these values and principles, as well as comply with relevant laws and regulations. Ethics also demands that AI systems are fair, unbiased, transparent, and respectful of human dignity and rights.
Governance: Humans have rules and norms that govern their interactions and relationships with each other and with the environment. Governance requires that AI systems follow these rules and norms, as well as contribute to the common good and public interest. Governance also involves establishing standards, policies, and mechanisms for the development, deployment, and oversight of AI systems.

How does XAI work?

XAI works by providing different types of explanations or justifications for the decisions or actions of AI systems. These explanations or justifications can be:

Global: They apply to the whole AI system or model, describing its general characteristics, properties, assumptions, limitations, strengths, and weaknesses.
Local: They apply to a specific decision or action of the AI system or model, explaining its input-output relationship, causal factors, influencing features, counterfactual scenarios, uncertainty levels, etc.
Contrastive: They compare a decision or action of the AI system or model with another alternative or expectation, highlighting the differences and similarities between them.
Counterfactual: They show what would have happened if the input or some feature of the input had been different from what actually occurred.
Causal: They show how a change in the input or some feature of the input causes a change in the output or some feature of the output.
Probabilistic: They show how likely or confident the output or some feature of the output is given the input or some feature of the input.

XAI can use different methods or techniques to generate these explanations or justifications. Some of these methods or techniques are:

Interpretable models: These are models that are inherently easy to understand by humans, such as linear models, decision trees, rule-based systems, etc. They have simple structures, clear logic, low complexity, high transparency, etc.
Post-hoc explanations: These are explanations that are generated after the model has been trained or deployed. They use various tools or frameworks to analyze the model’s behavior and extract insights from it. Some examples are feature importance measures (such as Shapley values), saliency maps (such as Grad-CAM), influence functions (such as LIME), etc.
Explanation by design: These are explanations that are incorporated into the model’s design or training process. They use various techniques to enhance the model’s interpretability or explainability while preserving its performance. Some examples are regularization methods (such as sparsity), attention mechanisms (such as self-attention), generative models (such as variational autoencoders), etc.

XAI can also use different formats or modalities to present these explanations or justifications to humans. These formats or modalities can be:

Textual: These are explanations that are expressed in natural language, such as sentences, paragraphs, bullet points, etc. They can use simple or complex vocabulary, syntax, and semantics, depending on the target audience and the level of detail required.
Visual: These are explanations that are displayed in graphical or pictorial forms, such as charts, graphs, tables, images, animations, etc. They can use colors, shapes, sizes, positions, movements, etc., to convey information and meaning.
Interactive: These are explanations that allow humans to explore, manipulate, query, or modify the AI system or model and its explanations. They can use interfaces, widgets, buttons, sliders, menus, etc., to facilitate human-AI interaction and feedback.

What are some examples of XAI applications?

XAI has many potential applications in various domains and scenarios where AI systems are used or expected to be used. Some examples are:

Healthcare: XAI can help doctors, nurses, patients, and other stakeholders understand the diagnosis, prognosis, treatment, and prevention of diseases and conditions based on AI models. For instance, XAI can explain why a patient has a certain risk of developing a disease based on their medical history and test results ¹ , or why a certain drug or therapy is recommended for a patient based on their symptoms and preferences ².
Finance: XAI can help bankers, investors, customers, and other stakeholders understand the credit scoring, fraud detection, portfolio management, and financial planning based on AI models. For example, XAI can explain why a customer is approved or rejected for a loan based on their credit history and income ³, or why a certain investment strategy is suggested for a customer based on their risk profile and goals.
Education: XAI can help teachers, students, parents, and other stakeholders understand the assessment, feedback, recommendation, and personalization based on AI models. For instance, XAI can explain why a student received a certain score or grade on a test or assignment based on their performance and knowledge, or why a certain course or material is recommended for a student based on their learning style and interests.
Law: XAI can help lawyers, judges, jurors, clients, and other stakeholders understand the legal reasoning, analysis, prediction, and advice based on AI models. For example, XAI can explain why a case has a certain likelihood of winning or losing based on its facts and precedents, or why a certain legal action or strategy is advised for a client based on their situation and objectives.

What are some challenges and limitations of XAI?

XAI is not without challenges and limitations. Some of them are:

Trade-off: There is often a trade-off between the performance and the interpretability or explainability of AI models. In general, the more complex and powerful the model is, the harder it is to understand and explain. Finding the optimal balance between these two aspects is not easy and may depend on the context and the goal of the application.
Evaluation: There is no clear or universal way to measure or evaluate the quality or effectiveness of XAI methods or techniques. Different stakeholders may have different expectations, preferences, and criteria for what constitutes a good explanation or justification. Moreover, different types of explanations or justifications may have different strengths and weaknesses, and may be suitable for different situations and purposes.
Ethics: There are ethical issues and risks associated with XAI, such as privacy, security, bias, manipulation, responsibility, etc. For example, XAI may reveal sensitive or personal information about the data or the users that may be exploited or misused by malicious actors. Or, XAI may generate misleading or inaccurate explanations or justifications that may influence or deceive the users in undesirable ways. Or, XAI may shift or obscure the accountability or liability of the developers, operators, or users of AI systems.

Conclusion

XAI is a branch of AI that deals with making AI systems more transparent and understandable to humans, by providing explanations or justifications for their decisions or actions. XAI is important for trust, accountability, ethics, and governance of AI systems. XAI works by providing different types of explanations or justifications using different methods or techniques and different formats or modalities. XAI has many potential applications in various domains and scenarios where AI systems are used or expected to be used. However, XAI also faces challenges and limitations such as trade-off, evaluation, and ethics.

References

¹: Explainable Artificial Intelligence (XAI) for Medical Diagnosis ²: Explainable Artificial Intelligence (XAI) for Clinical Decision Support ³: Explainable Machine Learning in Credit Risk Management : [Explainable Artificial Intelligence (XAI) for Financial Services] : [Explainable Artificial Intelligence (XAI

What is Explainable AI (XAI)?