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  • Businesses are increasingly adopting and deploying AI

  • to automate businesses and IT processes,

  • gain new insights through data analysis

  • and better engage with and serve customers.

  • Yet as AI adoption takes off across industries,

  • the biggest obstacle to widespread deployment is trust.

  • Trust, along with language and automation,

  • are the critical ingredients needed to scale AI for business.

  • First, it's important to consider how AI is built.

  • AI is managed across a lifecycle

  • a sequence of steps from preparing and building models

  • to deploying and managing them out in the world.

  • These steps must all be carefully monitored,

  • and guardrails must be put in place.

  • At IBM, we call this process AI Governance,

  • which is designed to help businesses create policies,

  • assign decision rights

  • and ensure accountability.

  • With proper governance of AI,

  • we can prevent undesirable outcomes

  • including models unintentionally harboring bias,

  • being trained with improper or unapproved information,

  • or even having unexpected shifts in performance.

  • One major challenge to date

  • is that the monitoring and documentation

  • of the AI model lifecycle is performed manually.

  • This requires substantial expertise

  • and can increase the risk of incorrect information.

  • Enter IBM Watson's AI FactSheets.

  • Born out of IBM Research

  • and built for a hybrid cloud world,

  • AI FactSheets will automatically capture

  • key information on a model's performance

  • and automatically create reports

  • to support transparency and compliance.

  • Drilling down deeper,

  • AI FactSheets are customizable for varying audiences

  • external and internal.

  • and reporting views can be customized

  • providing people like the business owner,

  • data scientist,

  • model validator

  • and model operator

  • unique reports with insights tailored to their specific needs.

  • Like nutrition labels for foods,

  • FactSheets would provide

  • important information about AI models,

  • such as its purpose,

  • performance,

  • data sets and more

  • model facts that are key

  • to building consumer and enterprise trust

  • in AI services across the industry.

  • Creating a template allows organizations

  • to define the information collected on an AI model,

  • such as how the model was created,

  • tested, trained, deployed and evaluated.

  • It can also standardize what data can and can't be used.

  • What regulationssuch as GDPR

  • or company policies need to be accounted for,

  • and many other factors.

  • Automated Data Capture: Model Facts.

  • To date, documenting an AI model's performance

  • has required extensive amounts of time and resources,

  • often leading to quickly outdated and irrelevant reports.

  • Soon, organizations will be able to continuously

  • and automatically capture metadatamodel facts

  • across the entirety of the AI lifecycle.

  • Automated Reporting: FactSheet.

  • With automated data capture,

  • the FactSheet will provide real-time reporting

  • on the performance of the model,

  • along with custom metrics.

  • The FactSheet can be tailored to the needs

  • and preferences of different users and audiences,

  • enabling collaboration across varying levels

  • of technical expertise that otherwise wouldn't be possible.

  • As with all IBM Watson technology,

  • AI FactSheets will be open,

  • built to run anywhere,

  • and can help organizations manage risks

  • across any hybrid cloud environment.

Businesses are increasingly adopting and deploying AI

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如何建立对您的AI模型的信任IBM Watson的最新创新(How to build trust in your AI model: the latest innovation from IBM Watson)

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    joey joey 發佈於 2021 年 05 月 09 日
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