AI and ML technologies have found their way into core processes of industries like financial services, healthcare, education, etc. Even with multiple use cases already in play, the opportunities with AI are unparalleled and its potential is far from exhausted. However, with increasing use of AI and ML among AI-driven organizations, ML engineers and decision makers who rely on AI outcomes, are now faced with explaining and justifying the decisions by AI models. Decisions have already been made, with the formation of various regulatory compliance and accountability systems, legal frameworks, requirements of Ethics and Trustworthiness. Ultimately, an AI model will be deemed trustworthy only if its decisions are explainable, comprehensible and reliable.
Today, multiple methods make it possible to understand these complex systems, but they come with several challenges to be considered. While ‘intelligence’ is the primary deliverable of AI, ‘Explainability’ has become the fundamental need of a product.
Arya.ai has innovated a state-of-the-art framework, ‘Arya-xAI’ to offer transparency, control and Interpretability on Deep learning models. This whitepaper explores:
- The explainability imperative
- Tangible business benefits of XAI
- Overview of current XAI methods and their challenges
- Details on the functioning of Arya-XAI framework