Unlocking the Power of Explainable Artificial Intelligence (XAI)

In today’s fast-paced world, businesses increasingly rely on artificial intelligence (AI) systems to make decisions that can significantly impact individual rights, human safety, and critical business operations. But how do these AI models derive their conclusions? What data do they use? And can we trust the results they produce? Addressing these critical questions is the essence of “explainability,” and getting it right is becoming essential for organizations aiming to harness the full potential of AI.

The Need for Explainable AI (XAI) 

Explainable artificial intelligence, often referred to as XAI, is a set of processes and methods that enable human users to comprehend and trust the results and output created by machine learning algorithms. 

XAI is used to describe AI models, their expected impact, and potential biases. It helps characterize model accuracy, fairness, transparency, and outcomes in AI-powered decision making. This level of explainability is crucial for organizations in building trust and confidence when implementing AI models into production. It also plays a pivotal role in adopting a responsible approach to AI development.

As AI continues to advance, a significant challenge arises: understanding and retracing how an AI algorithm arrived at a specific result. In many cases, the entire calculation process becomes a “black box” that is impossible to interpret. These black box models are created directly from data, and not even the engineers or data scientists who create the algorithm can fully understand or explain the internal workings, making it challenging to verify accuracy and leading to a loss of control, accountability, and auditability.

Why Explainable AI Matters

The importance of explainable AI cannot be overstated. It is crucial for organizations to have a full understanding of AI decision-making processes. Blindly trusting AI models can lead to unwanted consequences. Explainable AI bridges the gap by enabling humans to understand and explain machine learning algorithms, deep learning, and neural networks. These AI models are often perceived as black boxes, especially neural networks used in deep learning, which are particularly challenging for humans to comprehend.

Furthermore, bias in AI, often rooted in race, gender, age, or location, has been a longstanding concern when training AI models. AI model performance can also drift or degrade over time due to differences between production data and training data. Continuous monitoring and management of these models to promote AI explainability is essential, not only for the business impact but also for building end-user trust, model auditability, and ensuring the productive use of AI. It also serves to mitigate compliance, legal, security, and reputational risks associated with deploying production AI.

Explainable AI is a key requirement for implementing responsible AI. Responsible AI is a methodology for the large-scale implementation of AI methods in real organizations with a focus on fairness, model explainability, and accountability. To adopt AI responsibly, organizations must embed ethical principles into AI applications and processes, ultimately building AI systems based on trust and transparency.

How Explainable AI Works

Explainable AI and interpretable machine learning empower organizations to access the underlying decision-making of AI technology, enabling adjustments to improve the user experience. By understanding when AI systems provide enough confidence in their decisions and how they can correct errors, organizations can better leverage AI technology for decision-making processes.

Comparing AI and XAI

The distinction between regular AI and explainable AI lies in the level of transparency and interpretability. XAI employs specific techniques and methods to ensure that each decision made during the machine learning process can be traced and explained. In contrast, conventional AI often arrives at a result using machine learning algorithms, with the architects of the AI systems lacking a full understanding of how the algorithm reached that result. This lack of transparency can hinder accuracy checking, leading to issues with control, accountability, and auditability.

Explainable AI Techniques

The setup of XAI techniques encompasses three main methods:

1. Prediction Accuracy: Accuracy is a critical component of AI’s success in daily operations. One popular technique for determining prediction accuracy is Local Interpretable Model-Agnostic Explanations (LIME), which explains the predictions of classifiers made by machine learning algorithms.

2. Traceability: Traceability is another essential technique for achieving XAI. It involves limiting the ways decisions can be made and setting up a narrower scope for machine learning rules and features. DeepLIFT (Deep Learning Important Features) is an example of a traceability XAI technique. It compares the activation of each neuron to its reference neuron and establishes a traceable link between each activated neuron, revealing dependencies between them.

3. Decision Understanding: This aspect addresses the human factor. While many people initially distrust AI, educating the team working with AI systems is crucial for building trust. When individuals understand how and why AI makes decisions, it becomes easier to work with AI efficiently.

Explainability vs. Interpretability in AI

Interpretability measures the degree to which an observer can understand the cause of a decision. It focuses on how accurately humans can predict the result of an AI output. Explainability takes a step further by examining how the AI algorithm arrived at the result, providing a deeper understanding of the decision-making process.

Explainable AI and Responsible AI

Explainable AI and responsible AI share similar objectives but adopt different approaches. Explainable AI focuses on examining AI results after they are computed, while responsible AI considers AI during the planning stages to ensure the AI algorithm is responsible before results are computed. Both can work in conjunction to create better AI systems.

Continuous Model Evaluation

Explainable AI allows businesses to troubleshoot and improve model performance while ensuring stakeholders understand AI model behaviors. Investigating model behaviors through tracking insights on deployment status, fairness, quality, and drift is essential for scaling AI. Continuous model evaluation empowers businesses to compare model predictions, quantify model risk, and optimize model performance. This process involves displaying both positive and negative values in model behaviors and using data to generate explanations, which speeds up model evaluations. Data and AI platforms can generate feature attributions for model predictions, enabling teams to visually investigate model behavior through interactive charts and exportable documents.

Benefits of Explainable AI

Explainable AI offers numerous advantages for organizations:

1. Operationalize AI with Trust and Confidence: Building trust in production AI and ensuring interpretability and explainability of AI models simplifies the process of model evaluation, while increasing transparency and traceability.

2. Speed Time to AI Results: Continually monitoring and managing models helps optimize business outcomes and fine-tune model development efforts based on continuous evaluation.

3. Mitigate Risk and Cost of Model Governance: Keeping AI models explainable and transparent aids in managing regulatory compliance, risk, and other requirements. It minimizes the overhead of manual inspection and costly errors while mitigating the risk of unintended bias.

Five Considerations for Explainable AI

To achieve desirable outcomes with explainable AI, organizations should consider:

1. Fairness and Debiasing: Managing and monitoring fairness and scanning deployments for potential biases.

2. Model Drift Mitigation: Analyzing models and making recommendations based on logical outcomes, with alerts for deviations from intended results.

3. Model Risk Management: Quantifying and mitigating model risk, with alerts for inadequate model performance.