EXPLAINABLE AI (XAI)
WHAT IS EXPLAINABLE AI (XAI) ?
- Explainable artificial intelligence (XAI) is a set of processes and methods that allows human users to comprehend and trust the results and output created by machine learning algorithms.
- Explainable AI is used to describe an AI model, its expected impact and potential biases.
- It helps characterize model accuracy, fairness, transparency and outcomes in AI-powered decision making.
- Explainable AI is crucial for an organization in building trust and confidence when putting AI models into production.
- AI explainability also helps an organization adopt a responsible approach to AI development.
- Explainability can help developers ensure that the system is working as expected, it might be necessary to meet regulatory standards, or it might be important in allowing those affected by a decision to challenge or change that outcome.
IMPORTANCE OF XAI :
- It is crucial for an organization to have a full understanding of the AI decision-making processes with model monitoring and accountability of AI and not to trust them blindly.
- Explainable AI can help humans understand and explain machine learning (ML) algorithms, deep learning and neural networks.
- Neural networks used in deep learning are some of the hardest for a human to understand.
- Explainable AI also helps promote end user trust, model auditability and productive use of AI.
- It also mitigates compliance, legal, security and reputational risks of production AI.
- Explainable AI is one of the key requirements for implementing responsible AI, a methodology for the large-scale implementation of AI methods in real organizations with fairness, model explainability and accountability.
DIFFERENCE BETWEEN AI AND XAI :
- XAI implements specific techniques and methods to ensure that each decision made during the ML process can be traced and explained.
- AI, on the other hand, often arrives at a result using an ML algorithm, but the architects of the AI systems do not fully understand how the algorithm reached that result. This makes it hard to check for accuracy and leads to loss of control, accountability and auditability.
TECHNIQUES USED IN XAI :
Prediction accuracy :
- Accuracy is a key component of how successful the use of AI is in everyday operation.
- By running simulations and comparing XAI output to the results in the training data set, the prediction accuracy can be determined.
Traceability :
- Traceability is another key technique for accomplishing XAI.
- This is achieved, for example, by limiting the way decisions can be made and setting up a narrower scope for ML rules and features.
Decision understanding :
- This is the human factor.
- Many people have a distrust in AI, yet to work with it efficiently, they need to learn to trust it.
- This is accomplished by educating the team working with the AI so they can understand how and why the AI makes decisions.
How does explainable AI relate to responsible AI?
Explainable AI and responsible AI have similar objectives, yet different approaches. Here are the main differences between explainable and responsible AI:
- Explainable AI looks at AI results after the results are computed.
- Responsible AI looks at AI during the planning stages to make the AI algorithm responsible before the results are computed.
- Explainable and responsible AI can work together to make better AI.
USE OF XAI IN REAL LIFE:
HEALTHCARE:
- Accelerate diagnostics, image analysis, resource optimization and medical diagnosis.
- Improve transparency and traceability in decision-making for patient care.
- Streamline the pharmaceutical approval process with explainable AI.
FINANCIAL SERVICES:
- Improve customer experiences with a transparent loan and credit approval process.
- Speed credit risk, wealth management and financial crime risk assessments.
- Accelerate resolution of potential complaints and issues.
- Increase confidence in pricing, product recommendations and investment services.
CRIMINAL JUSTICE:
- Optimize processes for prediction and risk assessment.
- Accelerate resolutions using explainable AI on DNA analysis, prison population analysis and crime forecasting.
- Detect potential biases in training data and algorithms.
WAY FORWARD:
- To help adopt AI responsibly, organizations need to embed ethical principles into AI applications and processes by building AI systems based on trust and transparency.
- Explainable AI especially explainable machine learning will be essential if future warfighters are to understand, appropriately trust, and effectively manage an emerging generation of artificially intelligent machine partners.
SYLLABUS: MAINS, GS-3, SCIENCE AND TECHNOLOGIES
SOURCE: IBM.COM
