Explainable AI: Building Trust and Transparency in AI Systems
Explainable AI
Explainable artificial intelligence (XAI) is a set of processes and methods that allows human users to trust the results attained by machine learning algorithms. Explainable AI describes an AI model and helps characterize model accuracy, fairness, transparency, and outcomes in AI-powered decision-making. Explainable AI is crucial for organizations to establish trust and confidence when putting AI models into production. AI explainability helps organizations to adopt a responsible approach to AI development. Explainability allows AI developers to work on the system as they expect with regulatory standards.
Why Explainable AI Matters?
Organizations should have a full understanding of the AI decision-making processes with model monitoring and accountability of AI, due to trustability. Explainable AI helps humans understand and explain machine learning algorithms, deep learning and neural networks.
ML models are black boxes that are impossible to interpret. Neural networks used in deep learning are the hardest factors for a human to understand. AI model performance can drift or degrade since production data differs from training data. This helps businesses to continuously monitor and manage models to promote AI explainability while measuring the business impact of using AI algorithms. XAI also promotes end-user trust, model audibility, and productive use of AI, mitigating compliance, legal, security, and reputational risks of production AI.
Explainable AI implements responsible AI, a methodology for large-scale implementation of AI methods in real organizations with fairness, model explainability, and accountability. Organizations need to embed ethical principles into AI applications and processes by building AI systems based on trust.
How Explainable AI Works?
Explainable AI combined with machine learning ensures organizations' access to AI technology’s empowerment. XAI improves the user experience of a product or service by helping end users trust that AI is making good decisions. AI has become more advanced, and ML processes still need to be understood and controlled to ensure AI model results accurately.
Let’s have a look at the difference between AI and XAI, techniques, and explaining AI processes.
Comparing AI and XAI
XAI implements specific techniques and methods to ensure each decision is made during the ML process. AI often arrives at a result using an ML algorithm, but the architects of AI systems do not fully understand how the algorithm reached the result, making it hard to check for accuracy and leading to loss of control, accountability, and auditability.
Explainable AI Techniques
XAI technique setup is of three methods, prediction accuracy and traceability addresses technology requirements, and decision understanding addresses human needs.
Prediction accuracy - Accuracy is a key component of the use of AI in everyday operations. Prediction accuracy can be determined by running simulations and comparing XAI output to the results in the training data set. Some popular techniques used here are Local Interpretable Model-Agnostic Explanations (LIME), which explain the prediction of classifiers by ML algorithm.
Traceability - This is another key technique for accomplishing XAI. One example of traceability XAI technique is DeepLIFT (Deep Learning Important Features), which compares the activation of each neuron to the reference neuron shows a traceable link between activated neurons, and even shows dependencies of them.
Decision understanding - A human factor, where many people have a distrust in AI. This is done by educating the team working with the AI so they can understand how and why the AI makes decisions.
Explainability versus Interpretability in AI.
Interpretability is the degree of success rate that humans can predict for the result of an AI output, while explainability goes a step forward and looks at how AI arrived at the result.
How does explainable AI relate to responsible AI?
Explainable AI and responsible AI have similar objectives, but different approaches. Let’s see the differences between them:
- Explainable AI looks for the AI results once it’s computed.
- Responsible AI looks for the AI during the planning stages making AI algorithms responsible before the results get computed.
- Explainable and responsible AI works together to make better AI.
Benefits of Explainable AI
As organizations increasingly recognize the necessity to understand the decision-making processes of "black box" AI models, there has been a surge of interest in Explainable AI (XAI). The significant advantages of XAI can be summarized into five primary benefits:
Enhanced decision-making: XAI offers transparent and interpretable explanations for decisions made by AI models by helping organizations comprehend how to influence predicted outcomes. For example, with the SHAP explainability tool, it's possible to pinpoint the key features contributing to customer churn. This knowledge enables organizations to implement strategic changes to products or services, thereby effectively reducing churn.
Accelerated AI optimization: XAI provides a valuable tool for organizations looking to optimize their models more efficiently. By offering visibility into performance metrics, key drivers, and accuracy levels, XAI helps organizations pinpoint issues and enhance model performance quickly and effectively. This is in stark contrast to traditional black box models, where failures can be difficult to identify and address.
Trust building and bias reduction: By facilitating scrutiny of AI systems for fairness and accuracy, XAI bolsters trust and minimizes bias. The explanations offered by XAI reveal the patterns recognized by the model, allowing MLOps teams to pinpoint errors and evaluate data integrity. This contributes to a more robust and trustworthy AI ecosystem.
Increased adoption of AI systems: As organizations, customers, and partners gain a deeper understanding and trust in Machine Learning (ML) and Automated Machine Learning (AutoML) systems, the adoption of AI systems steadily increases. XAI-powered transparent AI models empower predictive, prescriptive, and augmented analytics, fostering widespread acceptance and extensive utilization of these advanced technologies.
Regulatory compliance assurance: XAI plays a critical role in ensuring regulatory compliance by facilitating the auditing of justifications behind AI-driven decisions. It does so by providing users with an understanding of the conclusions drawn about them and the data utilized in reaching those conclusions, thereby making compliance with laws more manageable.
Explainability Techniques
Shapley Additive Explanations (SHAP)
SHAP is a visualization tool to enhance the explainability of machine learning models, which utilizes game theory and Shipley values for model prediction of each feature value. SHAP is applied to any machine learning model for model neutrality producing consistent explanations and handling feature interactions. It is used in data science to explain predictions in a human-understandable manner for decision-making both globally and locally.
Local Interpretable Model-agnostic Explanations (LIME)
LIME is said to be the method for locally interpreting AI-black box machine learning model predictions. It generates synthetic data by perturbing individual data points and trains a glass-box model on the data to approximate the behavior of the black-box model. By analyzing the glass-box model, LIME provides insights into how specific features influence predictions for individual instances, providing a global interpretation of the entire model.
Partial Dependence Plot (PDP or PD plot)
PDF is a visual tool for understanding the impact of one or two features on the predicted outcome of a machine-learning model, illustrating if the relationship between the target variable and a particular feature is linear, monotonic, or complex. PDF applied globally provides a quick method for interpretability compared to other perturbation-based approaches.
Morris Sensitivity Analysis
Morris method is global sensitivity analysis which examines the importance of individual inputs in a model. It follows a one-step approach, where one input is varied while keeping others fixed at a specific level. This adjustment of input values fosters analysis as fewer model executions are required. Morris method is used for screening purposes, helping in identifying inputs significantly. The main criterion is it provides a global perspective on input importance.
Accumulated Local Effects (ALE)
ALE is said as a method to calculate feature effects in machine learning models. This offers global explanations for both classification and regression models. ALE provides a thorough picture of how each attribute and the model’s predictions connect throughout the entire dataset.
Anchors
Anchors serve as locally sufficient conditions that guarantee a specific prediction with high confidence. Particular prediction identifies the key features and conditions providing precise and interpretable explanations at a local level. Anchor’s nature allows for a more granular understanding of how the model arrives at its predictions, enabling analysts to gain insights.
Contrastive Explanation Method (CEM)
CEM is a local interpretability technique for classification models, generating instance-based explanations regarding Pertinent Positives (PP) and Pertinent Negatives (PN). PP identifies minimal and sufficient features, while PN highlights necessary features absent for a complete explanation. EM understands why a model is made with specific predictions, offering insights into positive and negative factors.
Global Interpretation via Recursive Partitioning (GIRP)
GIRP is a method which interprets machine learning models globally, using a contribution matrix of input variables to identify key variables and their impacts. Like other local methods, GIRP provides an understanding of the model's behavior across the dataset.
Scalable Bayesian Rule Lists (SBRL)
Scalable Bayesian Rule Lists (SBRL) is a machine learning technique, which lists logical structure, similar to decision lists. SBRL is used for both global and local interpretability at a more granular level, offering flexibility.
Tree Surrogates
This is an interpretable model trained to approximate the predictions of black-box models, providing insights into the behavior of AI black-box models by interpreting the surrogate model. Tree surrogates are used globally to analyze overall model behavior and locally to examine specific instances.
Explainable Boosting Machine (EBM)
EBM revitalizes traditional GAMs by machine-learning techniques like bagging, and automatic interaction detection. They offer interpretability comparable to the AI black box model and are efficient and compact during prediction.
Supersparse Linear Integer Model (SLIM)
SLIM, an optimization approach addresses the trade-off between accuracy and sparsity in predictive modeling. SLIM achieves sparsity by restricting the model’s coefficients to a small set of integers. This is particularly valuable in medical screening, which can help in identifying relevant factors.
Reverse Time Attention Model (RETAIN)
RETAIN is a predictive model designed to analyze Electronic Health Records (EHR) data, utilizing a two-level neural attention mechanism to identify important past visits and significant variables within key diagnoses. It mimics the chronological thinking of physicians by processing the EHR data in reverse time order. This model is mainly applied to predict heart failures.
Use Cases of Explainable AI
Healthcare
Banking
Financial Services
Insurance
Automobiles
Legal
Travel
Launch Your Explainable AI Project With Osiz
Osiz, the leading AI development company offers an explainable AI solution to accelerate responsible, transparent workflow across the lifecycle for both generative and machine learning models. Our XAI directs, manages, and monitors your organization’s AI activities to better manage growing AI regulations and detect and mitigate risk. We build robust AI systems powered by explainable AI, to address crucial aspects of transparency, compliance, and risk mitigation, benefitting your business.
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Source - https://www.osiztechnologies.com/blog/explainable-ai
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