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Abhijeet Goel

Model Agnostic vs Model Specific Explainability

In recent past there is a trend of supplementing complex machine learning and deep learning algorithms with augmented explanations for the decisions taken by the complex ML models. This notion termed as XAI Explainable AI is a recent trend which has emerged to counter the criticism of complex AI models

Explain vs Interpret: What to choose?

In recent past one of the hot areas in AI has been the domain of Explainable AI also known as XAI. This initiative started by Darpa in US is aimed at making AI and decisions taken by AI systems more acceptable to humans. A key thought process underlying this is

Poka — Yoking your ML Model

Fail proofing your AI model -Dr Srinivas Padmanabhuni, CTO , testAIng.com A very popular notion in quality management is the notion of Poka-Yoke. It is invented in Japan for ensuring quality. Poka-Yoke means ‘mistake-proofing’ or more literally — avoiding (yokeru) inadvertent errors (poka). In context of our daily lives there are

10 Tests for your AI/ML/DL model

CTO, testAIng.com In recent past there has been a spate of accidents involving AI and Machine learning models in practice and deployment. Much so that there is an active database of all such accidents being chronicled (https://incidentdatabase.ai/ ). At a time when AI is making strides in radical

Why current testing processes in AI/ML are not enough?

The current notions of quality assurance and testing in AI/ML pipelines is based on the idea of validation using a random set-aside set of data on which the model is tested and metrics computed thereof. Metrics like accuracy on the random set-aside data set termed ambiguously as test data,

Why we need to let go of our programming instinct in ML based AI?

Why we need to let go of our programming instinct in ML based AI?

In the modern world of software we are used to the paradigm of software engineering. The discipline of software engineering is predicated upon following a rigid regimen of quality programming, with deployment of expert coders and programmers to enable building of systems. Hence, a basic necessity often stressed is that

Why AI is required to have explainability?

In today s world of machine learning dominated Artificial Intelligence applications, there is a renewed push for the agenda of explainability. The triggers for explainability could be multifold: * A comfort feeling of knowing what you are handing over control to * A knowledge of the influence of the precise input component