Explainable AI (XAI): Unlatching black box

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Growing Need for Clarity in AI Systems

In today’s rapidly evolving landscape, AI algorithms and machine learning systems have permeated numerous sectors. Data-driven problem-solving strategies are now commonplace, with significant advancements in applied machine learning systems becoming increasingly evident, particularly in the field of deep learning. However, the complexity of these systems has given rise to new challenges in terms of explainability, rendering them difficult to comprehend even for AI experts. As a result, such intricate systems are often dubbed “black boxes,” a concept we will explore further in this article.

Given the prevalent use of machine learning in critical domains such as safety, healthcare, and defense, understanding the inner workings of these algorithms is crucial for building trust in the underlying framework. This is where explainable AI (XAI) comes into play. XAI is a research field dedicated to developing techniques that enhance our understanding and validation of machine learning models.

The demand for explainability and transparency extends beyond data scientists who create these models; end users and model stakeholders also expect clear explanations for how specific decisions were made. In this sense, explainability and transparency can be viewed as integral components of the overall user experience.

Interpretable Models vs. Post-hoc Explanations

When developing a machine learning model from data, we can either prioritize the model’s interpretability (i.e., building an interpretable ML model) or rely on post-hoc methods to extract human-understandable explanations from complex trained models, such as neural networks. Post-hoc methods can be further categorized into black-box and white-box approaches:

  1. Black-box models: In this approach, we have limited knowledge about the model’s inner workings and rely solely on its inputs and outputs to derive explanations. Black-box models are difficult to interpret as their internal mechanisms remain undisclosed. Deep learning models often employ these opaque systems.
  2. White-box models: Contrarily, white-box models grant us access to the model’s internal mechanisms, such as gradients and weights in a neural network. This allows us to identify important features and understand the model’s decision-making process, thus facilitating the explanation of its behavior.

Building Trust and Enhancing Model Performance

Consider an AI system in the healthcare domain tasked with recommending surgical procedures. Without a thorough understanding of the system’s decision-making process, users may struggle to trust its recommendations, as any incorrect decision could lead to catastrophic consequences and severely impact a patient’s life. XAI addresses this concern by clarifying how decisions are made, thereby fostering trust and acting as a catalyst for the adoption of machine learning systems.

Apart from building trust in AI models, XAI can be employed for several other purposes, including:

  1. Troubleshooting and improving model performance: XAI helps identify areas in which the model may underperform or exhibit errors, allowing developers to refine the model and enhance its accuracy.
  2. Tracking deployment insights: XAI enables stakeholders to monitor the model’s real-world performance, providing valuable information on how it adapts to changing conditions and different data sets.
  3. Ensuring fairness and quality: By uncovering potential biases, XAI promotes the development of equitable models that deliver high-quality, unbiased results.
  4. Monitoring model performance to mitigate issues: XAI aids in the detection and resolution of problems such as model drift, ensuring consistent and reliable performance over time.

Real-world Applications of Explainable AI Across Various Industries

  1. Justice: Criminal risk assessment algorithms used to predict criminal behavior identify patterns associated with crime to generate recidivism scores. Adopting such algorithms without understanding the underlying correlations can be problematic. For example, high recidivism rates correlated with low income from historical criminal data may produce detrimental outcomes. XAI can help mitigate these issues by providing clarity on the correlations formed.
  1. Finance: Executives can use XAI to test the robustness of models by creating stress test scenarios. Complex models can be employed with confidence for deeper insights and performance improvement, thanks to the transparency provided by XAI.
  1. Healthcare: XAI can enhance various aspects of healthcare, such as preventive medication, drug discovery, treatment options, and identifying genetic abnormalities by obtaining patient insights for predictive analysis. XAI can also validate the decision-making process of AI-driven healthcare wearables and software that analyze data and generate potential health-related risks.
  1. Defense: XAI is expected to play a significant role in the development of “third wave AI systems” in DARPA programs. Advanced and innovative technologies in the defense sector, such as software used by the Ukrainian military to identify undercover Russian soldiers or automated UAVs, can be adopted seamlessly with the assistance of XAI.

Categorizing Explainable AI Methods

Explainable AI methods can be categorized based on two key properties: scope and model type, as described below:

  1. Scope: XAI methods can be either global or local in scope.
    • Local Scope: Focuses on explaining a specific local prediction. Examples include LIME, SHAP, and counterfactual explanations. These methods may not be suitable for all model instances when considered as a whole.
    • Global Scope: Aims to explain the entire model by providing an average explanation. Examples include Partial Dependence Plots (PDP), Function Decomposition, and Accumulated Local Effects. These methods are not well-suited for explaining specific instances.
  2. Model Type: XAI methods can be either model-specific or model-agnostic.
    • Model-Specific: Tailored to a particular algorithm type, these methods delve into the inner mechanisms of the model.
    • Model-Agnostic: Appropriate for all model types, these methods analyze features and their associations with outcomes without focusing on a specific model’s inner workings.

Popular Explainability Techniques in AI Systems

An effective explainable AI system can identify a model’s strengths and weaknesses based on its parameters, enabling better predictions of its future performance and increasing the likelihood of its acceptance. Some popular explainability techniques include:

  1. LIME (Local Interpretable Model-agnostic Explanations): LIME offers local explanations for individual predictions by approximating complex models with simpler, interpretable models, such as linear regression or decision trees.
  2. SHAP (SHapley Additive exPlanations): SHAP values quantify the contribution of each feature to a specific prediction by drawing on cooperative game theory concepts. This method offers consistent and locally accurate feature attributions for various model types.
  3. Counterfactual Explanations: These explanations describe alternative scenarios in which the model’s prediction would have been different. By modifying specific input features, counterfactual explanations help users understand how the model would have behaved under different conditions.
  4. Partial Dependence Plots (PDP): PDPs visualize the marginal effect of a feature on the model’s predicted outcome while averaging out the influence of all other features. This global explanation technique helps identify the relationship between specific features and the target variable.
  5. Feature Importance: This method ranks features based on their contribution to the model’s performance, offering insights into which variables are most influential in the decision-making process.
  6. Anchors: Anchors provide rule-based explanations for individual predictions by identifying the minimum set of input features that guarantee a specific prediction with high confidence. This method helps users understand the conditions under which the model would consistently make the same decision.

By employing these explainability techniques, AI practitioners can enhance the transparency and trustworthiness of their models, thereby improving model acceptance and facilitating better decision-making.

Popular Tools for Explainable AI

A wide variety of tools are available to make explainability more accessible for AI practitioners. Some popular tools for explainable AI include:

  1. ELI5: ELI5 (Explain Like I’m 5) is a Python library that offers model-agnostic explanations for various ML models, including support for scikit-learn, XGBoost, LightGBM, and CatBoost. It provides methods such as feature importance, permutation importance, and model debugging. GitHub – ELI5
  2. LIME: LIME is a Python library that allows users to explain the predictions of any classifier by approximating the model locally with an interpretable model. GitHub – LIME
  3. SHAP: SHAP is another Python library that implements SHAP values for various ML models, providing unified and consistent explanations for different algorithms. GitHub – SHAP
  4. Skater: Skater is an open-source Python library for model interpretation and visualization. It offers global and local interpretability, as well as support for multiple model types, including deep learning models. GitHub – Skater
  5. AI Explainability 360: Developed by IBM Research, AI Explainability 360 is an open-source toolkit that provides various algorithms for explainable AI, including LIME, SHAP, and contrastive explanations. AI Explainability 360
  6. What-If Tool: The What-If Tool is an interactive visual interface for model exploration and understanding, integrated with TensorFlow. It allows users to investigate model performance across different data points, visualize model explanations, and compare multiple models. What-If Tool
  7. InterpretML: Developed by Microsoft Research, InterpretML is an open-source Python library that offers a unified framework for training interpretable models and explaining black-box models using techniques like LIME, SHAP, and PDP. GitHub – InterpretML

By leveraging these powerful tools, AI practitioners can incorporate explainability into their work, fostering trust, transparency, and better decision-making in AI systems.

Conclusion

Explainable AI (XAI) plays a crucial role in building transparent, understandable, and trustworthy AI systems. As machine learning models become more complex and widespread, it is imperative to ensure that their decision-making processes are accessible and comprehensible to both AI practitioners and end-users. XAI techniques, such as LIME, SHAP, and counterfactual explanations, help elucidate the inner workings of AI models, fostering trust and facilitating better decision-making.

Moreover, the growing adoption of AI in sensitive domains like healthcare, finance, and defense highlights the importance of explainability. By employing XAI methods, stakeholders can gain insights into model performance, mitigate biases, and ensure fairness, ultimately promoting the responsible use of AI technologies.

The variety of tools available for implementing explainable AI, such as ELI5, LIME, SHAP, and InterpretML, make it easier for AI practitioners to integrate explainability into their work. By leveraging these tools and techniques, the AI community can work towards building transparent, reliable, and ethical AI systems that truly benefit society.

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Author: Vrinda Tandon

Editor: Amita Kapoor

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