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Explanations on the AI models
with ANAI
Explainable AI or XAI helps you get insights behind a model’s decisions and dive deeper into the “black box” of ML models whereas Responsible AI (RAI) helps an organization build AI related products with user safety and trust in mind.
Explainable ai | Explainable machine learning
XAI or Explainable AI helps you get insights behind a model’s decision and dive deeper into the “black box” of ML models.
Responsible AI a.k.a RAI helps an organization build AI related products with user safety and trust in mind.
1 +
Unique XAI & RAI Models
1 x
Improvement in Model Performance
1 %
Reduction in Model Bias
1 %
Reduction in Compliance Cost
What is XAI?
XAI or Explainable AI helps you get insights behind a model’s decision and dive deeper into the “black box” of ML models. Hence at ANAI, we are using the state-of-the-art methods to generate diverse explanations for your use case and we provide an easy-to-use interface to deal with your tedious Data Science related works.
What is RAI?
Responsible AI a.k.a RAI helps an organization build AI related products with user safety and trust in mind. It keeps ethics are the forefront and provides a framework which ultimately dissolves bias and encourages fairness and trust.
Together XAI and RAI will help you
Easily understand every single prediction made by your ML model and spot biases and data related problems with ease.
XAI will help with generating user trust on your AI solutions as they will be able to see the inner workings of your model for a particular decision.
Responsibility has always been an important aspect while deploying ML models that affect users on a higher level and XAI and RAI helps in making your models more Fair, Robust and Responsible.
Be a part of our open source to contribute towards the mission of democratizing AI
ANAI's XAI models helps dive deeper
ANAI has been built with explainability and responsibility in mind:
- ANAI’s XAI-based solutions enable businesses to dive deeper into the decisions made by an AI model to help them understand and open the black box of such models.
- ANAI has 25+ XAI techniques such as SHAP, K-LIME, and many others to give out explanations for a model’s working and helps the stakeholders understand the features that affect the predictions the most.
Generate Results On Trained Models Before Publishing
With ANAI, organizations can detect the biases within their data and the model’s performance early on so that the system that they deliver is responsible and fair.
ANAI allows businesses and other entities to increase their transparency and help their customers generate trust in their AI-based systems so that they remain within the good eye of the customers and also of many regulatory bodies.
How Explanability is Necessary in Different Industries?
Finance
Ability to explain the reasoning behind an AI’s decisions is highly relevant as it helps customers build trust in the system and increases transparency. XAI and RAI helps mitigating the doubts regarding an AI-based system’s bias towards a particular demographic during mortgage lending or refinancing, helping entities govern, audit and maintain such systems with ease.
Hiring
Explainable and Responsible AI can help eliminate all sorts of biases found in AI models trained on a biased dataset during hiring. XAI can help with giving out explanations for why a certain candidate is selected instead of some other, which can lead to an increased trust and transparency within the system, and employers can hire the best candidates available.
Healthcare
Explaining the results of an AI model’s outcome helps with getting the model approved by the health regulators and increases the overall trust and reliability on the AI-based systems during health diagnosis. XAI’s ability to find prominent features affecting a prediction can allow doctors to understand the real reasons behind a patient’s illness and can also help with finding the right treatment.
