Decidex
Decidex

DecideX

DecideX DecideX DecideX

AI for high risk decision               

CEO:  ------

 FOUNDER: Sai Pranav   

 co-FOUNDER: Sajiv.S 

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Why we Choose DecideX

DecideX 's abstract

real-time problems faced by us and the solution.

real-time problems faced by us and the solution.

This project aims to design visualization-based explainable AI tools that help humans interpret and understand decision made by complex machine-learning models. The team will explore techniques such as features attribution, heat map ,decision path ways and interactive dashboards to reveal the reasoning behind model outcomes.

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real-time problems faced by us and the solution.

real-time problems faced by us and the solution.

real-time problems faced by us and the solution.

AI systems are being deployed in high - risk domains such as finance , healthcare , and criminal justice . However , most deep learning models operate as "black boxes," providing predictions without showing how or why those decisions were made.

this lack of transparency leads to mistrust   ,unfair decisions , and difficulty in detecting er

AI systems are being deployed in high - risk domains such as finance , healthcare , and criminal justice . However , most deep learning models operate as "black boxes," providing predictions without showing how or why those decisions were made.

this lack of transparency leads to mistrust   ,unfair decisions , and difficulty in detecting error or biases .

There is a critical need for explainable AI(XAI) tools that allow humans to understand , verify , and trust AI - generated decisions.

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real-time problems faced by us and the solution.

real-time problems faced by us and the solution.

real-time problems faced by us and the solution.

This project aims to design visualization-based explainable AI tools that help humans interpret and understand decision made by complex machine-learning models. The team will explore techniques such as features attribution, heat map ,decision path ways and interactive dashboards to reveal the reasoning behind model outcomes.

.

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