Trusted and ethical AI with IBM Cloud Private for Data

By | 2 minute read | March 27, 2019

The next big wave to impact humanity is artificial intelligence. It will be powered by the new oil: data. AI is poised to fundamentally change every industry, every business, and possibly every person on earth.

A moment like this is rare—and we are just at the beginning. IDC predicts that “By 2019, 40 percent of digital transformation initiatives will use AI services; by 2021, 75 percent of commercial enterprise apps will use AI.”

AI is an exponential step forward for businesses, promising new, ground-breaking customer solutions once considered impossible. At IBM, we have made it our mission to make AI more comprehensive, more responsible, and in partnership with our customers and partners, to do amazing things with AI.

As AI becomes central to business, trust and transparency in AI becomes critical. If the data used to train the models reflect unfair bias, and the resulting insights and recommendations aren’t transparent and trusted, then AI probably won’t be embraced and used at scale. In addition, businesses will be susceptible to heavy regulatory fines and reputation losses. Businesses could have hundreds of AI pilot projects and initiatives, each one potentially built using different tools and running in different environments, due to team preference or to avoid vendor lock-in. Whatever the reason for this current reality, as leaders look to deploy and manage all those models across their workflows, the time and talent required to ramp up AI can become prohibitive. ​

IBM anticipated these barriers to scaling enterprise AI. We developed a platform to help clients operationalize AI faster while infusing trust and transparency with IBM Cloud Private for Data and the add-on Watson OpenScale.

When used together, ICP for Data and Watson OpenScale offer capabilities that help businesses:

  • Detect and proactively mitigate bias, to ensure the performance of models is fair​
  • Allow users to see how and why models make the recommendations they do
  • Trace the lineage of the data and training used to build models ​
  • Work with the most commonly-used build and runtime environments – across public and private clouds – to ensure an open, integrated workspace ​
  • Automate design, monitoring, and continuous improvement of models to reduce the burden on teams​
  • Integrate models into workflows and process automation platforms​

At IBM, we’ve been hard at work to establish our clients as leaders in digital transformation through IBM Cloud Private for Data. We’ve developed IBM Cloud Private for Data to deliver breakthrough Data solutions and AI services, while giving the industry the mechanism to infuse trust and detect bias in data which is the foundation of AI.

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