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ML Bias & Fairness

Ensure Ethical AI through ML Observability

Experience peace of mind with an ML observability platform that diligently ensures fairness and actively mitigates bias in your AI systems.

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Guard Against Bias

By mitigating bias, you not only safeguard fairness in your AI but also enhance decision-making quality and build trust with users, paving the way for the ethical and responsible evolution of your technology.

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Track Fairness Metrics

Don’t wait to hear about discriminatory or unfair outcomes from your customers or the media. Tailor dashboards to showcase and track fairness metrics, ensuring your AI systems more transparent, accountable, and aligned with societal values.

This fosters trust among stakeholders and ensures that their AI contributes equitably to the betterment of all communities.

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Evaluate Fairness Across Protected Groups

See behind the complex decision-making process of AI. Uncover features driving biased predictions and gain insights into how attributes influence outcomes.

Detailed explanations enable pinpointing sources of bias, enabling effective retraining for fairer, more transparent, and ethically sound AI systems.

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Look what our customers have to say about us

“In a space that is developing fast and offerings multiple competing solutions, Aporia’s platform is full of great features and they consistently adopt sensible, intuitive approaches to managing the variety of models, datasets and deployment workflows that characterize most ML projects. They actively seek feedback and are quick to implement solutions to address pain points and meet needs as they arise.”

Felix D.

Principal, MLOps & Data Engineering

“As a company with AI at its core, we take our models in production seriously. Aporia allows us to gain full visibility into our models' performance and take full control of it."

Orr Shilon

ML Engineering Team Lead

“ML models are sensitive when it comes to application production data. This unique quality of AI necessitates a dedicated monitoring system to ensure their reliability. I anticipate that similar to application production workloads, monitoring ML models will – and should – become an industry standard.”

Aviram Cohen


“With Aporia's customizable ML monitoring, data science teams can easily build ML monitoring that fits their unique models and use cases. This is key to ensuring models are benefiting their organizations as intended. This truly is the next generation of MLOps observability.”

Guy Fighel

General Manager AIOps

“ML predictions are becoming more and more critical in the business flow. While training and benchmarking are fairly standardized, real-time production monitoring is still a visibility black hole. Monitoring ML models is as essential as monitoring your server’s response time. Aporia tackles this challenge head on.”

Daniel Sirota

Co-Founder | VP R&D

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