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TL : DR
AI explainability (XAI) enables understanding of how AI models make decisions, crucial to fostering trust and improving performance. By integrating XAI into the development process along with strong AI governance, developers can improve accuracy, reduce risk, and eliminate bias.
Introduction
As AI systems are increasingly being deployed across industries, they are integrating into critical applications. However, the rate of adoption is inconsistent.
Here’s an example. A company might develop an AI solution using machine learning to power their manufacturing production, creating a safer and more efficient process. It’s an expensive proposition, so the company is expecting big things when it’s deployed. Yet, workers are hesitant to adopt it.
Why? Because working around the machines is inherently dangerous and the employees don’t trust the AI models to keep them safe and make the right decisions.
AI models must be trustworthy and transparent. That is at the core of the concept of explainable AI.
What Is Explainability in AI?
AI explainability (XAI) is the ability to understand how AI models and algorithms work so that end users can comprehend and trust the results. You can build powerful AI/ML tools, but if those using them don’t understand or trust them, you likely won’t get optimal value.
When building applications, developers must also create AI explainability tools to solve this challenge.
Key Takeaways
- AI explainability is essential for building trust, ensuring regulatory compliance, and improving model performance.
- Techniques such as interpretable models, post-hoc explanation methods, and visualization tools can enhance AI explainability.
- Integrating explainability from the start, tailoring explanations for different stakeholders, and continuous monitoring are key best practices.
Understanding AI Explainability
AI models are complex. The inner workings are known to the developers, but hidden from users. XAI helps users understand how models work and how they arrive at results.
The National Institute of Standards and Technology (NIST) proposed four key concepts for XAI:
- Explanation: The system provides reasons for its outputs.
- Meaningful: The explanations are understandable to the target audience.
- Accuracy: The explanations correctly reflect the system’s reasoning.
- Knowledge limits: The system operates within its designed scope and confidence levels.
Focusing on these four principles can bring clarity to users, showcasing model explainability and inspiring trust in applications.
Types of AI Models
AI models are typically one of two types: black box or white box AI models.
Black box AI models are highly complex, often using deep neural networks, which makes it challenging to understand decision-making. While they tend to deliver high accuracy, it is difficult to trust the model’s reasoning since you can’t see inside. It can also be difficult to diagnose and correct errors because root causes may not be obvious within the complexity.
By contrast, white-box AI models are clearer. You can see the logic and reasoning behind decisions. These AI solutions typically rely on simpler algorithms like decision trees or rules. These models may not achieve the same level of accuracy on complex tasks, but the inner workings are easier to understand.
Importance of AI Explainability
AI explainability aids in three key areas:
Building Trust
AI explainability creates a foundation of trust for users. This is especially important in mission-critical applications in high-stakes industries such as healthcare, finance, or areas that Google describes as YMYL (Your Money or Your Life).
Regulatory Compliance
Regulators are trying to catch up with the emergence of AI and there are important decisions ahead about how and when laws and rules need to be applied. Regardless, explainable AI will be central to compliance in many cases to demonstrate transparency.
There are already some laws in place. For example, the EU’s General Data Protection Regulation (GDPR) grants individuals the “right to explanation” so that individuals can understand how automated decisions about them are made. This would apply in cases such as AI processes for loan approvals, resume filtering for job applicants, or fraud detection.
Improving AI Performance
Besides explaining things to end users, XAI helps developers create and manage models. With a firm understanding of how decisions and outputs are made, developers are more likely to identify biases or flaws. This leads to better model tuning and improved performance.
Techniques for Enhancing AI Explainability
Developers can apply certain techniques to improve AI explainability.
Interpretable Models
AI interpretability is built into some AI models to make it easier to understand. They follow a hierarchal structure of rules and conditions, such as:
- Decision trees: Represents the process as a tree structure, making it easy to follow the logic behind each decision
- Rule-based systems: Relies on a set of predefined rules to make decisions, providing inherent interpretability
- Linear regression models: Expresses the relationship between features and the output as a linear equation allowing users to see how each feature influences the outcome
Post-Hoc Explanation Methods
For black-box models, explainability is more complex. Post-hoc explanation methods by analyzing the model’s input and outputs. Common AI explainability tools include:
- Local Interpretable Model-Agnostic Explanations (LIME): The LIME framework approximates the behavior of a black-box model locally, providing explanations for individual predictions.
- SHapley Additive exPlanations (SHAP): The SHAP framework is based on game theory, assigning importance values to input features and quantifying their contributions to the model’s output.
- Attention Mechanisms: Used in natural language processing (NLP) and computer vision, attention mechanisms highlight the parts of the input that the model focuses on when making predictions.
Visualization Tools
Visual representations can be helpful in explainability, especially for users who are not developers or data scientists.
For example, visualizing a decision tree or rules-based system using a diagram makes it easier to understand. This gives users a clear definition of the logic and pathways the algorithms choose to make decisions.
For image analysis or computer vision, a saliency map would highlight the regions in an image that contribute to decisions made by an AI model. This could help machine operators to better understand why algorithms chose to position items in a specific way in production or why parts were rejected for quality issues.
Developers can also create partial dependence plots (PDPs) which can visualize the impact of certain features on outputs. PDPs can show the non-linear relationships between input variables and model predictions.
Best Practices for AI Explainability
Following a few best practices can help, starting with the integration of AI explainability during initial development.
Integrating Explainability from the Start
By incorporating interpretability requirements during the design phase, you can build your development roadmap, documenting key system information at each step. This helps inform your explainability process and keeps models focused on accurate and unbiased data.
User-Centric Explanations
You will need to have explanations for both technical and non-technical users, so you will need to tailor your explanations for comprehension. Data scientists will want to take a deeper dive into the inner workings than executives or line-level workers, who will be more focused on the practical implications of outputs.
Continuous Monitoring and Feedback
Explainability should be an ongoing process, especially for complex models that evolve over time as more data is gathered. As AI systems evolve new scenarios, explanations should be assessed and updated as necessary.
User feedback will be crucial in the monitoring process to account for different scenarios or use cases to help improve the clarity of explanations and the accuracy of the AI model.
Challenges in Achieving AI Explainability
XAI has significant challenges that must be addressed and the more complex a model is, the more challenging it is.
Another challenge is eliminating bias. If the training data is biased, the model will be tuned to make biased decisions. AI governance to ensure data accuracy, security, and fairness is a crucial aspect of developing trustworthy AI.
Developers can overcome these challenges by building in explainable AI principles from the start, highlighting the factors that influence decisions and showing how changing inputs change outputs.
There’s often a trade-off between model accuracy and explainability, especially for models that rely on:
- High-dimensional data with large numbers of variables
- Reinforcement learning, where models learn through trial and error
- Generative Adversarial Networks (GANs) which use two neural networks to help improve outcomes
In some cases, the best approach is combining AI with human oversight. Such human-in-the-loop systems empower humans to leverage AI while still maintaining control over the final decision-making process.
Final Thoughts
AI explainability is an essential part of responsible AI development. By making the decision-making process transparent and understandable, you can establish a level of trust and comfort among users. This also aids in ensuring regulatory compliance and improving system performance.
As AI integration continues across industries and various aspects of our lives, organizations must prioritize AI explainability in their development strategies. This helps inspire confidence in outcomes and promotes a culture of transparency and accountability within development teams.
The pursuit of XAI is a key component in AI governance and ethical use. As AI systems evolve and become more powerful — and more complex — ensuring this transparency is increasingly crucial to mitigate potential risks and adhere to ethical principles.
ZenData integrates privacy by design across the entire data lifecycle, emphasizing contest and risks in data usage. We help companies with insights into data use, third-party risks, and alignment with data regulations and policies. If you would like to learn more about how ZenData can help you with AI governance and compliance to reduce risk, contact ZenData today.