The most advanced ML Observability product in the market
Building an ML platform is nothing like putting together Ikea furniture; obviously, Ikea is way more difficult. However, they both, similarly, include many different parts that help create value when put together. As every organization sets out on a unique path to building its own machine learning platform, taking on the project of building a […]
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We’re excited 😁 to share that Forbes has named Aporia a Next Billion-Dollar Company. This recognition comes on the heels of our recent $25 million Series A funding and is a huge testament that Aporia’s mission and the need for trust in AI are more relevant than ever. We are very proud to be listed […]
Simply put, Explainable Artificial Intelligence (XAI) allows machine learning algorithms to create outputs and results that can be understood and trusted by humans. A model that is explainable AI describes its impact, biases, and the expected impact of the model. With its help, AI-powered decision-making can be assessed for fairness, transparency, and accuracy.
When putting AI models into production, building trust and confidence is crucial for an organization. An organization can likewise use AI explainability to adopt a responsible AI development approach.
Understanding the process that led an AI-enabled system to produce a specific outcome is very beneficial. In addition to helping developers monitor a system’s performance, explaining how a decision was made is important for meeting regulatory standards and allowing those affected to challenge the decision.