Arya.AI Aims To Solve AI's 'Black Box' Problem With Advanced Explainability Tools

In this time when there's a new AI tool every day, understanding how these tools function and how these AI models arrive at decisions in the first place is a concern. Indian startup Arya.AI is addressing this issue with its innovative platform AryaXAI, turning opaque black-box models into interpretable white-box systems.

So, how does Arya.AI plan to solve the explainability dilemma and what does it mean for industries worldwide? Let's explore!
 

Why Explainability Matters In AI?

As AI models grow in size and complexity, their internal workings become more challenging to understand. This lack of transparency can lead to trust issues, especially in high-stakes applications.

Emerging regulations like the EU's AI Act mandate explainability for high-risk AI systems, highlighting the need for solutions that ensure compliance and trust.

Vinay Kumar Sankarapu, CEO of Arya.AI, encapsulates the challenge: "Capabilities are fine—you can say your model is 99.99% accurate but I want to know why it is 0.01% inaccurate. Without transparency, it becomes a black box and no one will trust it enough to put billions of dollars into it."
 

What Makes AryaXAI A Game-Changer?

Arya.AI's proprietary 'Backtrace' technique provides accurate, real-time explanations for deep learning models across diverse data types, from structured to unstructured.

The platform offers feature-level explanations, enabling users to see precisely which inputs influenced a model's decision and to what extent.


AryaXAI's natural language explanations bridge the gap between technical complexity and business requirements, making it accessible to both technical and non-technical stakeholders.

Its ability to generate real-time explanations makes it ideal for production environments requiring quick and transparent decision-making.

AryaXAI is particularly valuable in regulated industries such as finance and healthcare. While it provides detailed audit trails and compliance documentation, it also helps ensure ethical and accurate diagnostics and treatment planning decision-making.
 

The Problem Beyond Hallucinations

Recent studies have revealed biases in large language models (LLMs), such as racial and gender disparities in ranking job applicants. These biases, let alone explain the need for explainable AI to address fairness and trust issues.

Mukundha Madhavan, tech lead at DataStax, comments: "Foundation models, their training and architecture—it feels like they are external entities, almost victims of their own complexity. We are still scratching the surface when it comes to understanding how these models work."
 

Arya.AI's Vision For Autonomous Systems

The company is exploring advanced techniques like contextual deep Q-learning to enable AI agents to handle tasks requiring memory and planning.

Sankarapu emphasizes aligning models with user expectations through feedback loops and explainability to build truly autonomous systems that handle complex tasks like banking transactions.

Arya.AI also focuses on improving model efficiency and unlocking new research areas, reinforcing its role as a catalyst for innovation.
 

What Do The Experts Say

Sankarapu identifies a paradox in AI development: "We are creating more complicated models that are harder to understand while saying alignment is required for them to become mainstream."

He highlights the need for efficiency and explainability to scale alongside model complexity, positioning AryaXAI as a solution to this challenge.

Madhavan proposes a three-pronged approach to AI transparency: explainability research, model alignment and practical safeguards for real-world applications.
 

Looking Ahead

AryaXAI aligns with global regulatory frameworks like the EU's AI Act to ensure organizations can deploy AI systems confidently and responsibly.

By transforming black-box models into transparent systems, Arya.AI helps industries meet compliance requirements while fostering trust and ethical AI practices.

Do you think AryaXAI will set a new standard for explainable AI?

Let us know in the comments below!