Demystifying Explainable Artificial Intelligence: Benefits, Use Cases, and Models

Apr 26, 2021

Digital Transformation | 5 min READ

New AI Responsibility for AI – Explaining the Why

Artificial Intelligence (AI) is making a significant impact in almost every Industry – Healthcare, Finance, Insurance, and Manufacturing. More sophisticated AI models are being built to cater to the needs of specific use cases. But, the predictions from these AI Models look like "Black-box" output, without providing the reason or explanation of how the Model arrived at that prediction. Researchers, Organizations, Regulators need to understand how AI models are providing recommendations fully, predictions, etc., and this resulted in the emergence of "Explainable AI (XAI)."

Rishi Verma

Practice Director

Artificial Intelligence

Birlasoft

Prajwal Ainapur

Data Science Solutionist

Birlasoft

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What is Explainable AI?

Explainable AI (XAI) can be considered the collection of processes that help the developer and the users understand and interpret the results.

Developers utilize the XAI results to understand the potential bias in the model and improve the model performance. Users can trust and believe the AI model's inference results as it comes along with the interpretation of the inference. 'Code confidence' usually refers to how much the developer/user has confidence in the AI model's inference. Below questions are hard to answer with currently utilized 'Black Box' like models.

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How do users trust the AI model in an autonomous vehicle to make the right decisions in a high traffic scenario?
How to make the doctor trust the inference of the AI model diagnosing a particular disease?

Utilizing an XAI solution to explain the results could help the developer provide the necessary code confidence for the end-user.

Model Explainability is a prerequisite for building trust and adoption of AI systems in high-stakes domains requiring reliability and safety, such as healthcare and automated transportation, and critical applications including predictive maintenance, exploration of natural resources, and climate change modeling.

AI vs. Explainable AI

Visual representation of XAI and how it potentially affects the end-user

Source: DARPA

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The image provides a visual representation of XAI and how it potentially affects the end-user.

XAI solutions have various advantages, which brings them to the spotlight and highlights the need for quicker large-scale adaption worldwide. Regulations (Example: Article 22 of GDPR) give the rights to the individuals to demand an explanation on how an AI system has made a decision that can impact them. The algorithmic Accountability Act of 2019 mandated the companies to perform and provide a risk assessment report for the risks posed by automated decision-making systems based on accuracy, bias, fairness, privacy, security, and discrimination.

Benefits of Explainable AI (XAI)

Reducing Cost of Mistakes: Decision-sensitive fields such as Medicine, Finance, Legal, etc., are highly affected in the event of wrong predictions. Oversight over the results reduces the impact of erroneous results & identifying the root cause leading to improving the underlying model.
Reducing Impact of Model biasing: AI models have shown significant evidence of bias. Examples include gender Bias for Apple Cards, Racial Bias by Autonomous Vehicles, Gender, and Racial bias by Amazon Rekognition. An explainable system can reduce the impact of such biased predictions cause by explaining decision-making criteria.
Responsibility and Accountability: AI models always have some extent of error with their predictions, and enabling a person who can be responsible and accountable for those errors can make the overall system more efficient
Code Confidence: Every inference, along with its explanation, tends to increase the system's confidence. Some user-critical systems, such as Autonomous vehicles, Medical Diagnosis, the Finance sector, etc., demands high code confidence from the user for more optimal utilization.
Code Compliance: Increasing pressure from the regulatory bodies means that companies have to adapt and implement XAI to comply with the authorities quickly.

Benefits of Explainable AI

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Explainable AI Models

Most of the Organizations are working on their proprietary XAI solutions. Various Open source solutions are also available LIME, SHAP, DeepLIFT, CXplain, DeepSHAP, etc., in XAI. LIME, SHAP, and CXplain are some of the commonly utilized XAI models for ML-based solutions. In contrast, Deep learning models utilize more optimized variants for neural networks such as DeepLIFT and DeepSHAP

Demystifying Explainable Artificial Intelligence

More sophisticated classifiers developed in the past decade on top of decision trees, such as XGBoost, LightGBM, CatBoost, etc. One can expect a similar large-scale development to be made in XAI utilizing the current models as a base XAI model. This approach, referred to as Model Agnostic Approach, does not know about what is happening inside the AI model but utilizes only the input and output to understand and provide interpretation. XAI-generated explanations can be categorized as global (that is, summarizing the relevance of input features in the model) or local (based on individual predictions).

Use Cases and Examples of Explainable AI

Insurance

Customer Retention

One of the Cost-effective ways is to retain existing customers than acquiring new customers. XAI can predict the Customer churn with the reasons.

Claims Management

Providing a claim to the Customer without proper reasoning may result in a poor customer experience. Explaining the result can help improve Customer satisfaction.

Insurance Pricing

Insurance pricing depends on multi-factors. With XAI, Customers can better understand the Insurance pricing changes and make informed decisions concerning their requirements.

Banking

AI empowers Banks to provide smooth Customer experiences, driving loyalty and profitability and automating processes. Some of the areas where XAI can help include Fraud detection, Payment exceptions, Customer engagement & cross-selling, collections optimization, tailored pricing, and enhancing Robo-advisors.

Healthcare / Life Sciences

AI-assisted Drug design

Drug Design distinguishes itself from clear-cut engineering by the presence of error, nonlinearity, and seemingly random events. With our incomplete understanding of molecular pathology and inability to formulate infallible mathematical models of drug action and corresponding explanations, XAI bears the potential to augment human intuition and skills for designing novel bioactive compounds with desired properties.

AI Integrated Health-conditions prediction

In today’s world, AI is integrated with Healthcare devices to predict the occurrence of health conditions. XAI shows the rationality of the prediction, thereby increasing accountability & the trust for the predictions made.

Use Cases of Explainable Ai

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Key Takeaways

Explainable AI has found various use cases, especially in event critical sectors such as Finance, Legal, and automation. Market share of XAI Implementations is bound to increase in the coming years. XAI brings in aspects like Transparency (knowing how the system reached a particular answer), Justification (elucidating why the answer provided by the model is acceptable), Informativeness (providing new information to human decision-makers), and Uncertainty estimation (quantifying how reliable a prediction is).

AI researchers and practitioners have focused their attention on explainable AI to better trust and understand models at scale. XAI is the possible way to create and develop an AI solution that can function as dynamically and as somewhat as possible.

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