Fair & Explainable AI
Like humans, AI-based systems can also be biased. An AI-based system is considered fair if the outputs produced by it are independent of sensitive attributes such as gender, race, religious faith, disability, etc. Otherwise, the system is considered biased. Bias may creep in at a stage as early as data capture or as late as post-deployment.
Explainable AI refers to methods and techniques in the application of Artificial Intelligence and Machine Learning technology (AI & ML) such that the results of the solution can be understood by not only the AI practitioners but also by the business and even the consumers
Our Services:
Provide consultation to frame & implement comprehensive fair & explainable AI policy and guidelines based on context and regional & global aspects
Assess your AI-based systems for fairness and perform root cause analysis for biasness in your AI systems.
Mitigate bias in your AI systems at the pre-processing stage, in-processing stage, and post-processing stage as appropriate.
Generate detailed analysis reports depicting fairness statistics and validating that the AI system is fair and free from bias.
Generate detailed Explainability visual reports by leveraging various methods such as Shapley values, PDP, Feature Importance etc.
