Announcing Fiddler’s Latest Suite of ML Monitoring Capabilities Powered by AI Explainability

Today, at the VentureBeat Transform event, we launched our ML Monitoring feature set, inclusive of data drift detection, outlier detection, and data integrity. These capabilities are coupled with Fiddler’s industry-leading Explainable AI Platform to efficiently and effectively explain, analyze, and resolve MLOps production monitoring issues. Challenges in MLOps Monitoring  AI adoption is accelerating, with one … Continue reading “Announcing Fiddler’s Latest Suite of ML Monitoring Capabilities Powered by AI Explainability”

Fiddler & Captum join hands to enhance explainable AI offerings

We are excited to announce that Fiddler and Captum, from Facebook AI, are collaborating to push the boundaries of Explainable AI. The goals of this partnership are to help the data science community to improve model understanding and its applications, as well as to promote the usage of Explainable AI in the ML workflow. Fiddler is … Continue reading “Fiddler & Captum join hands to enhance explainable AI offerings”

Accelerating AI in the time of COVID-19; Fiddler named to Forbes’ AI 50 list

We’re excited to announce that Fiddler has been named one of America’s most promising artificial intelligence companies on this year’s Forbes AI 50 list. Forbes partnered with venture firms Sequoia Capital and Meritech Capital to look at over 400 privately-held, U.S.-based companies that are applying AI in meaningful, business-oriented ways. Judges evaluated companies on their … Continue reading “Accelerating AI in the time of COVID-19; Fiddler named to Forbes’ AI 50 list”

Identifying bias when sensitive attribute data is unavailable: Geolocation in Mortgage Data

In our last post, we explored data on mortgage applicants from 2017 released in accordance with the Home Mortgage Disclosure Act (HMDA). We will use that data, which includes self-reported race of applicants, to test how well we can infer race using applicants’ geolocations in our effort to better understand methods to infer missing sensitive … Continue reading “Identifying bias when sensitive attribute data is unavailable: Geolocation in Mortgage Data”

Identifying bias when sensitive attribute data is unavailable: Exploring Data from the HMDA

To test their automated systems for possible bias across racial or gender lines, organizations may seek to know which individuals belong to each race and gender group. However, such information may not be easily accessible, and organizations may use techniques to infer such information in the absence of available data [1]. Here, we explore a … Continue reading “Identifying bias when sensitive attribute data is unavailable: Exploring Data from the HMDA”