Explainable Artificial Intelligence (XAI) for AI & ML Engineers

prologue

Hello to all AI & ML engineers. Artificial intelligence (AI) and machine learning Engineering is the fastest growing sector, with almost every industry adopting engineering to enhance and speed up business decisions. SMEs and AIML experts build solutions.

Undoubtedly, we follow lifecycle-specific recommendations to select the right algorithms and provide solutions for forecasting. Regression (or) clustering (or) classification modeling. Things don’t stop there.

For more clarity, an end-user or stakeholder would like more clarity on the solution and justification. This gray area is the so-called black box.

Currently, in the industry, the expensive add-ons of this series are the so-called Explainable AI (XAI) I’m sure you’ve heard of this term before. This add-on gives confidence to the machine learning (ML) models we are developing and is very transparent. This will drive his adoption of AIML in many industries such as: Banking, Finance, Healthcare, Retail, Manufacturing, Giant Research Use Case.

In this article, we will accurately understand the following topics within a prescribed timeline without getting bored.

• What is Explainable AI?
• Why Choose XAI
• explainability techniques
• The theory behind XAI
• The need for model explainability
• Poor ML Prediction Results

What is Explainable AI?

Explainable Artificial Intelligence (Zai) is a collection of well-defined processes and methods that enable users to understand and trust the output produced by well-chosen machine learning algorithms. Influence, and Potential Bias.

This article provides the intangible view you need. explainability techniques Machine learning (ML), along with key explanations and approaches,

This is necessary for stakeholders and consumers to understand the transparency and interpretability of algorithms beyond their scope.

Generally speaking, there are multiple questions about the benefits of AI and ML adoption and how it can broaden the scope of current business challenges to meet consumer expectations.

So, to answer these questions, Explainable (XAI) is in the field, helping many industries, and explainability is now a prerequisite.

Why Explainable Artificial Intelligence?

As we know AIML is an integral part in terms of digital business decision-making and forecasting. The main concern of business stakeholders here is the lack of clarity and interpretability, as existing ML solutions mostly use black-box algorithms and are highly subject to human bias. we all know this. XAI is introduced into the ML lifecycle, taking responsibility and sealing expectations for explanations and translations. black box algorithm, used for critical business decision-making processes of stakeholders. We will increase their recruitment and coordination.

XAI should understand that it is the most effective best practice for validating that AI and ML solutions are transparent, accountable, ethical and trustworthy. As such, it addresses algorithmic requirements, transparency, risk evolution and mitigation.

XAI is a set of techniques that shed light on the choice of algorithms and help them perform equally well at each stage of a “machine learning” solution, based on ML results, open-handed capabilities for business questions. never forget.model of why and how pattern.

Below this are block diagrams of the classical and XAI approaches.

The big picture of explainable artificial intelligence

Put simply, explainability Easy to apply in two stages, pre-modeling and post-modeling.

Straight data-centric and (Pre) and model-specific (Post). The diagram below shows this very precisely. “

explainability techniques

Explainability can be started by classifying it into the following major categories

• model-specific explainability
• Model-agnostic explainability
• Model-centric explainability
• Data-Centric Explainability

Model-specific explainability: This type of explainability method is strictly related to a specific machine learning model algorithm. example: Decision tree A model is specific only to the decision tree algorithm underlying the model-specific explainability method.

Model-independent explainability: This type of explanation for any type of machine learning model, regardless of the algorithm used. Post-analysis methods are commonly used after machine learning model training. This does not depend on any particular algorithm, such as model-specific explainability, and is not informed of any internal model structure and weights. This is flexible.

Model center: Traditionally, most explanation methods are model-centric. Features and target values It is tuned and applies different algorithms to extract a specific set of results.

Data Centric: We can say that these methods are used to understand the nature of the data. It means that it is consistent and suitable for solving business problems. As we know, data plays a key role in building predictions and classical modeling. Sametime is required to understand the algorithm’s behavior on a particular dataset. If your data is inconsistent, your ML model will most likely fail. Data profiling, data drift monitoring, and data adversarial are specific approaches to data-centric explainability.

Model explainability methods: There are various approaches available for providing model explainability.

Knowledge extraction method: This is exploratory data analysis (Eda) process to extract key insights and statistics from the dataset. This is a post-analysis method, a kind of model-independent explainability.

structure dataset

Statistical results from various data points in a given dataset

Insights from datasets

Example based method: It is intended for non-technical end-users by providing the best way to describe the functionality of the model.

Impact-based method: Features play an important role in influencing the model’s results and its decision-making process. Most of the basic models are supported in this way by focusing on function in decision making.

How to visualize the results: It’s just comparing the results of the models using a particular plotting method.

The theory behind explainable artificial intelligence

There is a major theory behind XAI that needs to be understood in addition to other factors. Here are some key items on that list. Let’s look at them in crystal space.

• benchmark
• dedication
• reliability
• recognition
• experience
• Anomaly control

Benchmarks for Explainable ML systems: As you know, a series of benchmarks is required for the process to be successful. Of course, that XAI has the following parameters to meet expectations while adopting the same at organizational level.

Commitment and Reliability: XAI should provide holistic explanations and credibility explanations, leading to the commitment of ML models. These two factors play an important role, especially in detailed analysis. RAC (root cause analysis).

Awareness and experience: These two factors always make a big difference when dealing with RAC (Root Cause Analysis) For model prediction. There should be a better human-friendly way of explaining, which is always expected in succinct and abstract presentations. Too many details add complexity and affect the end user’s experience.

Anomaly control: Data anomalies are a common challenge in ML solutions. we all know that. Therefore, we need to carefully observe the nature of the DATA we introduce into the algorithm, and before doing so, Eda A process for understanding the data upside down even after the results. The model explanation should include the explainability of the anomaly. As such, end-users are very comfortable understanding the results of the model regardless of continuous or categorical values ​​in the dataset.

Let’s focus on poor predictive results and the need to overcome model explainability in real-time scenarios.

Poor ML Prediction Results

• Most of the time the model has problems because: As you know, this is due to poor prediction.
  • ML Algorithm Bias
  • dataset bias for prediction

Both rely purely on external factors, and this item will disrupt your model on all levels and destabilize it in production.

I hope you are familiar with these two factors. You can easily identify these during the EDA process and modeling with test and training data.

• data commitment
  • Quality and lack of training data
    data

Data quality is a major root cause of poor ML predictions. As such, each data engineer is responsible for this during the onboarding of data to the data platform. And make sure your source system has this expectation from your D&A team and ML engineers.

• Selection of irrelevant features

Feature selection is a key activity for tabular data. As an ML engineer, you should analyze all required fields and exclude them from the testing and training process itself. Dimensionality reduction techniques can also be used if desired. But I need to see what the dependent and independent variables are.

Conclusion

Folks, we have discussed XAI at a high level. Thank you for your understanding. XAI is

• Why Explainable AI?
• Key explainability techniques and theories behind XAI
• The need for model explainability and the consequences of poor ML predictions.

Here are some highlights about XAI in the following points:

Thanks for your time; I’ll get back to you on another topic soon. Goodbye! See you soon – Cheers Shanthabab!

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