AryaXAI: Accelerating the path to ML transparency

AryaXAI by Arya.ai offers 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 and details on the functioning of Arya-XAI framework

Ketaki Joshi

August 18, 2022

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, ‘AryaXAI’ 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

Complete this form to download

Name *
Work Email *
Contact Number
Country *
Designation *
Company *
Thank you for your interest!

Your Documents is ready to download. please click below to initiate the download.

If you are unable to download the document or have same query, please contact us on

Oops! Something went wrong while submitting the form.

See how AryaXAI improves
ML Observability

Learn how to bring transparency & suitability to your AI Solutions, Explore relevant use cases for your team, and Get pricing information for XAI products.

AryaXAI: Accelerating the path to ML transparency

Whitepaper

By

Ketaki Joshi

August 18, 2022

AI Explainability

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, ‘AryaXAI’ 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

Complete this form to download

Thank you! Your submission has been received!
Thank you for your interest!

Your Documents is ready to download. please click below to initiate the download.

If you are unable to download the document or have same query, please contact us on

Oops! Something went wrong while submitting the form.

See how AryaXAI improves
ML Observability

Get Started with AryaXAI