Explainable AI Frameworks help businesses to implement explainability. It also ensures businesses run bias evaluations within AI systems. Moreover, Explainable AI Frameworks explain and interpret AI models.
Further, Explainable AI and its frameworks align methods and techniques to enable AI technology. These methods and techniques make it easier for humans to comprehend the results.
Hence, in this article, we will understand the various Explainable AI Frameworks.
Understanding Explainable AI Frameworks
Firstly, the Explainable AI Frameworks enable users to impose the process of explainability. Moreover, it assesses results and data with respect to biases and fairness.
Explainable AI frameworks also generate explanations and interpretations to offer concrete results. Further, it re-contextualizes existing processes to measure and quantify their scope.
Explainable AI Frameworks execute explainability using the following steps:
- Data Analysis
- Model Evaluation
- Production Monitoring
To understand the approaches of explainability businesses must delve deep into the various dimensions in human comprehensibility. Firstly, let us understand the concept of transparency, which provides humans with a comprehensive view of the internal processes within a model. Further, businesses must look for the evaluation criteria for models. Finally, businesses must consider the types of explanations that fulfill the requirements of models.
Transparency refers to the level of understanding for humans of the internal processes in a model. Hence, here are the three dimensions that are considered:
- Simulatability: It refers to the first level in transparency. Moreover, it is the capability of a model to simulate information for human understanding. Further, it includes models that are easy and concise that belong to this group. Although, simplicity is not enough as it constricts humans to calculate the model’s decision.
- Decomposability: Secondly, this level of transparency indicates the function to segregate a model into various components. It also explains the components that include input, parameters, and computations. Although, not all the models adhere to this level.
- Algorithmic Transparency: This third level of transparency demonstrates the capability to comprehend the process a model undergoes to provide results. Moreover, complex models like neural networks, tend to build incomprehensible loss functions. Further, the solution to the training objectives must provide approximate calculations as well. Hence, any model that belongs to this level helps monitor the mathematical analysis.
Evaluation Criteria was introduced for rule extraction methods by Craven and Shavlik, 1999. Although now the following components help in the evaluation of models in explainability.
- Comprehensibility: It refers to the degree that extracts representations. These representations are easily comprehensible and include components of transparency.
- Fidelity: It refers to the degree of accuracy while extracting representations. It also captures the opaque models within the extractions.
- Accuracy: It refers to the function of extracting representations that accurately speculate hidden examples.
- Scalability: It refers to the techniques that scale opaque models. It also includes large data sets from various sources.
- Generality: It refers to the technique that needs specific training and authorization on opaque models.
The types of explanations are considered for opaque models specifically:
- Text Explanations
- Visual Explanations
- Local Explanations
- Explanations by examples and use cases
- Explanations by simplification and ease in usage
- Feature Relevance and Specification Explanations
What-if Tool by TensorFlow is an intuitive and user-friendly visual interface. Moreover, it is one of the best Explainable AI frameworks as it visually represents datasets and provides comprehensive results. It also deploys TensorFlow models for analysis and evaluations.
The tool is also abbreviated as WIT and it monitors performances in various scenarios and hypothetical situations. It also calculates the role of various data features and visualizes models and their behavior. It also compares different models and their subsets.
Moreover, Explainable AI Framework works as an expansion of Jupyter, Colaboratory, and Cloud AI Platform notebooks. It also executes tasks like binary classification, multi-class classification, and regression. Further, it uses data types like Tabular, Image, and Text data.
LIME or Local Interpretable Model-Agnostic Explanations is a technique developed by researchers from the University of Washington. It helps attain a higher level of transparency within an algorithm.
As the number of dimensions increase, it is difficult to manage and monitor the local fidelity of models. However, LIME provides solutions for feasible tasks and identifies models approximately.
Moreover, LIME offers understandability to both optimize and conceptualize representations. It also accommodates domain and task-specific understandability criteria.
LIME is one of the best Explainable AI Frameworks as it easily interprets any black box classifier with multiple classes. Further, the modular and extensive approach offers reliable and comprehensive predictions of models.
DeepLIFT is a comparative technique for activation of each neuron to its “reference activation”. It also allocates resources to contribute scores according to their comparisons.
Moreover, it provides different considerations for positive and negative contributions. It also uncovers dependencies hidden due to various approaches. Hence it calculates scores efficiently in one backward pass.
Skater is one of the best Explainable AI Frameworks as it provides Model Interpretation for all types of models. It also helps develop an understandable machine learning system. Moreover, it offers solutions for real-time use cases with its unification features.
It is also an open-source python library that designs and demystifies specific structures. These structures are comprehensive within a black box model that works both globally and locally.
Shapley Value SHAP or SHapley Additive exPlanations refers to the aggregate marginal contributions within a feature value for various coalitions.
Coalitions combine features that estimate and predict specific values of a specific feature. It uses a unified method to interpret the results of various machine learning models.
Further, SHAP combines game theory that offers desirable components. It is a cooperative technique as it combines various approaches.
AIX360 or AI Explainability 360 offers an open-source library. IBM develops this framework to enable the interpretability and explainability of various datasets in a machine learning model. It functions as a Python package that includes comprehensive algorithms. Moreover, these algorithms monitor various dimensions of explanations and their proxy explainability metrics.
Activation Atlases is one of the most robust Explainable AI Frameworks. Google collaborates with OpenAI to develop this novel technique to visualize the interaction between neural networks. It also monitors the way neural networks expand their horizon with information and various layers.
Moreover, the method provides a visual representation of the internal processes in convolutional vision networks. It also procures an overview of concepts in hidden layers to help humans comprehend information from the network.
Rulex is a company that develops predictive models for first-order conditional logic rules. Moreover, it helps to provide immediate comprehensive results for everyone to use.
Further, its core machine learning algorithm, Logic Learning Machine (LLM) uses a different approach than the conventional AI. It also provides conditional logic rules to predict decisions in a comprehensive language. Moreover, its rules generate self-explanatory predictions.
In conclusion, Explainable AI Frameworks are techniques and solutions that help resolve complex models. Moreover, the frameworks build trust among humans and AI systems by interpreting and understanding predictions and outcomes. Hence, paving the way for more transparency by using XAI frameworks that provide reasoning for decisions and predictions.