Explainable Artificial intelligence
Heading 6
Key Points of XAI reports
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Identifying modelling bugs
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Sometimes ground truth information leaks into the test set and this leads to false performance reports.
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This can happen when features are engineered in such a way that the label is (unintentionally) used when computing the feature values
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Depending on how the code is written, this can be hard to detect.
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It can also happen when missing values are filled in by taking the class distribution into account (and doing it on the test set, where theoretically you don’t know the class).
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Better oversampling
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Models trained on unbalanced datasets often need oversampling of the smaller class in order to achieve good performance.
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By using DeepGenerator, our in-house technique for exemplar generation, we achieved better results then standard over-sampling techniques, such as SMOTE
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E.g: German Credit Data
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Feature selection
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Identifying a small subset of features which account for most of the predictive power of the model.
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Using XAI techniques to select features often leads to different and better choices than simply using the features which the model itself deems as important (e.g. features with large weights)
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Having a model which relies on fewer features is not only easier to interpret and faster, but often even works better in practice since it generalizes better to new unseen data.
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Avoid overfitting (and/or bias)
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Sometimes, a model relying to much on a small set of features is a sign of overfitting of the training data when, in fact, the performance of the model on real world data is much lower
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Also, models are prone to picking up biases from the training set and this can lead to controversial decisions (e.g. using “race” as a feature to predict loan default).
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XAI techniques can identify such issues and alleviate them.
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Generate adversarial/counterfactual examples
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Adversarial examples are cases where the correct prediction is obvious for a human, but the model makes a mistake (usually with high confidence). This can be exploited by potential attackers.
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Counterfactual examples are similar. They refer to small insignificant changes made to a data point which completely changes the model’s prediction on it.
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Using XAI to find adversarial and counterfactual examples can reveal underlying issues with the model.
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E.g: German Credit Data
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Reveal (unexpected) correlations between features and label
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Revealing correlations from the model’s perspective, which might or might not correspond to domain knowledge and this can validate or invalidate the model.
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It can also reveal insights about how the training set was put together. (some trends might be present in the dataset, but they don’t represent the real world behaviour)
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E.g: German Credit Data
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Model Lifecycle Management
Our Model lifecycle management platform provides all the tools you need to create a
model governance, model comparisons, extensive model versioning and history of user, algorythm, model experimenting, model algorithm explainability, reducing bias, identify model bias to provide a XAI deployment you can trust.
model risk mitigation
model risk exposure
Correlation uncertainty
Time inconsistency
Uncertainty on volatility
Volatility smile
Implied volatility
Model risk is the risk of loss resulting from using imprecise or poorly developed models to make decision. Models are used throughout business services firms for:
Assessing / predicting exposures
Detecting / predicting fraud
Assigning consumer credit scores
Detecting suspicious or unlawful activity
There are costs and risks involved in using models, however, ranging from the direct costs of developing and implementing them to the adverse impacts of making decisions based on flawed or misused models. So it’s essential to adopt model risk management (MRM) to control the risks created by unmanaged models
Models - Model lifecycle management: Build, analyze, manage, improve
Enhanced Experiments
Model versioning
Historical look back archive
Added notes
Publish model functionality
Experiments
Anomaly Detection: Zscore based Isolation Forest, OneClassSVM
Regression
Forecasting
Unsupervised
Hyper parameters grid search
Ability to run / experiment existing model on multiple datasets
Model Inspector
parameters
metrics
data visualization
Hyper parameters grid search
Model comparison analysis - compare two models on the same data set to evaluate performance.
Model Compare
Show example screen and explain
Model Prediction Predictions
Visual Insights / Digital Dashboard
Customizable
Sharable
Hyper parameters grid search
Model Feedback loop
Improve model based on case management feedback
Improving model outcomes
Measure feature importance
Deep analysis of all features and the proper relevance and weighting
Investigate and identify bias in model
Reducing erroneous outcomes
Investigate and identify bias in training data
Reducing erroneous outcomes
Identify model weekness
Review model to identify existing and potential new features that should be
included to refine model outcomes
Investigate and identify out of distribution data for
Train surrogate model
Synthesize data
Peace of mind
Continuous model monitoring
Model accuracy score / f1
Cloud based ML services (GCP,AWS)
Model risk is the danger of misfortune coming about because of utilizing imprecise or ineffectively formulated models for decision making. Models are being utilized by different organizations, too. Esteeming introductions, doling out shopper financial assessments, anticipating exchange extortion, and identifying dubious, criminal or terroristic movement are only a portion of their shifted employments.
There are expenses and dangers associated with utilizing models, notwithstanding, going from the immediate expenses of creating and executing them to the antagonistic effects of settling on choices dependent on defective or abused models. So it's fundamental to receive model danger the executives (MRM) to control the dangers made by unmanaged models
AI systems sometimes learn undesirable tricks that do an optimal job of satisfying explicit pre-programmed goals on the training data, but that do not reflect the complicated implicit desires of the human system designers. For example, a 2017 system tasked with image recognition learned to "cheat" by looking for a copyright tag that happened to be associated with horse pictures, rather than learning how to tell if a horse was actually pictured.] In another 2017 system, a supervised learning AI tasked with grasping items in a virtual world learned to cheat by placing its manipulator between the object and the viewer in a way such that it falsely appeared to be grasping the object
Explainable machine learning
Explainable artificial intelligence (XAI), or Machine Learning (ML) commonly describes post hoc analysis and strategies made to help humans understand a previously trained model and/or its predictions. Examples of usual strategies consist of:
Reason code generating techniques
In particular, local interpretable model-agnostic explanations (LIME) and Shapley values.
Local and global visualizations of model predictions
Accumulated local effect (ALE) plots, one- and two- dimensional partial dependence plots, individual conditional expectation (ICE) plots, and decision tree surrogate models.
Interpretable or white-box models
Over the past few years, more researchers have been designing new machine learning algorithms that are nonlinear and highly accurate, but also directly interpretable, and interpretable as a term has become more associated with these new models.
Examples of these newer Bayesian or constrained variants of traditional black-box machine learning models include explain‐ able neural networks (XNNs),10 explainable boosting machines (EBMs), monotonically constrained gradient boosting machines, scalable Bayesian rule lists,11 and super-sparse linear integer models (SLIMs).12,13 In this report, interpretable or white-box models will also include traditional linear models, decision trees, and business rule systems. Because interpretable is now often associated with a model itself, traditional black-box machine learning models, such as multilayer perceptron (MLP) neural networks and gradient boosting machines (GBMs), are said to be uninterpretable in this report. As explanation is cur‐ rently most associated with post hoc processes, unconstrained, black-box machine learning models are usually also said to be at least partially explainable by applying explanation techniques after model training. Although difficult to quantify, credible research efforts into scientific measures of model interpretability are also underway.14 The ability to measure degrees implies interpretability is not a binary, on-off quantity. So, there are shades of interpretability between the most transparent white- box model and the most opaque black-box model. Use more interpretable models for high-stakes applications or applications that affect humans.
Model debugging
Refers to testing machine learning models to increase trust in model mechanisms and predictions.15 Examples of model debugging techniques include variants of sensitivity (i.e., “What if?”) analysis, residual analysis, prediction assertions, and unit tests to verify the accuracy or security of machine learning models. Model debugging should also include remediating any discovered errors or vulnerabilities.
Fairness
Fairness is an extremely complex subject and this report will focus mostly on the more straightforward concept of disparate impact (i.e., when a model’s predictions are observed to be different across demographic groups, beyond some reasonable threshold, often 20%). Here, fairness techniques refer to dispa‐ rate impact analysis, model selection by minimization of dispa‐ rate impact, remediation techniques such as disparate impact removal preprocessing, equalized odds postprocessing, or sev‐ eral additional techniques discussed in this report.16,17 The group Fairness, Accountability, and Transparency in Machine Learning (FATML) is often associated with fairness techniques and research for machine learning, computer science, law, vari ous social sciences, and government. Their site hosts useful resources for practitioners such as full lists of relevant scholar‐ ship and best practices.