{"id":164670,"date":"2021-06-02T16:00:34","date_gmt":"2021-06-02T11:00:34","guid":{"rendered":"https:\/\/venturebeat.com\/?p=2694011"},"modified":"2021-06-02T16:00:34","modified_gmt":"2021-06-02T11:00:34","slug":"truera-launches-tool-for-spotting-and-contextualizing-ai-bias-other-issues","status":"publish","type":"post","link":"https:\/\/www.technologyforyou.org\/truera-launches-tool-for-spotting-and-contextualizing-ai-bias-other-issues\/","title":{"rendered":"TruEra launches tool for spotting and contextualizing AI bias, other issues"},"content":{"rendered":"
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Explainable AI is vital for trust, compliance, and building better \u2014 and hopefully less biased \u2014 AI models. Both customers and regulators<\/a> want to know more about what\u2019s inside the black box<\/a>. But most monitoring tools leave blind spots, alerting data scientists to issues but not their causes.<\/p>\n

Today, AI startup TruEra<\/a> launched a solution that allows enterprises to not only have a deeper understanding of their machine learning models, but also to identify the root cause of issues and contextualize them almost instantaneously. The tool, called TruEra Monitoring and based on the company\u2019s AI Quality Platform, looks for AI quality metrics including bias<\/a>, model stability over time, and which features are driving the score.<\/p>\n

\u201cIf a model is experiencing bias, or is experiencing drift, we can pinpoint that to the exact features that are driving that disparity and then put that in proportion to how much they\u2019re driving that disparity,\u201d Will Uppington, cofounder and CEO of TruEra, told VentureBeat. \u201cThat\u2019s pretty much instantaneous, whereas most solutions just don\u2019t do that root cause analysis.\u201d<\/p>\n

Filling the monitoring gap<\/h2>\n

Current solutions can monitor infrastructure and KPIs, but by offering a way to monitor the inputs and outputs of machine learning models themselves, TruEra<\/a> aims to fill what it calls the \u201cmonitoring gap.\u201d In some benchmarks, the company said, the tool is more than 10 times faster than other solutions at a comparable level of accuracy.<\/p>\n

The other differentiator is that TruEra Monitoring is best integrated during development, offering diagnostic insights that can shape a better model from the early stages.<\/p>\n

\u201cThe approach a lot of people have taken is they\u2019ve modeled it after SAS infrastructure monitoring, which is kind of a standalone, very performance-oriented activity, whereas that\u2019s completely the wrong approach for machine learning,\u201d Uppington said. \u201cWe believe the right approach is that monitoring is just a continuation of the development cycle. Today\u2019s operational data is tomorrow\u2019s training data.\u201d<\/p>\n

Serving the customers<\/h2>\n

When using the tool, customers can interact with a variety of dashboards, including an overwatch page that summarizes each model being monitored. There are also links to debugging tools, so teams can act quickly when they find an anomaly. And for users who prefer to integrate the data into their existing platforms, there\u2019s also an API to export the data.<\/p>\n

HarperCollins, one of the first companies to deploy the tool, says it has been \u201cvery happy with the results\u201d so far.<\/p>\n

The book publisher has been using TruEra Monitoring since February, when it was still in beta. Matthew Bennett, North America CIO of HarperCollins Publishers, says monitoring of machine learning is one of the major missing pieces of IT management, and he\u2019s excited the solution goes beyond current ad hoc tools.<\/p>\n

\u201cTruEra helped us improve the monitoring of some of our system\u2019s outputs, shifting away from a manual, non-scalable approach,\u201d Bennett told VentureBeat.<\/p>\n

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