When Using AI in Enterprises, Balancing Innovation and Privacy Is Critical

While the U.S. is making strides in the advancement of AI use cases across industries, we have a long way to go before AI technologies are commonplace and truly ingrained in our daily life.

What are the missing pieces? Better data access and improved data sharing.

As our ability to address point applications and solutions with AI technology matures, we will need a greater ability to share data and insights while being able to draw conclusions across problem domains. Cooperation between individuals from government, research, higher education and the private sector to make greater data sharing feasible will drive acceleration of new use cases while balancing the need for data privacy.

This sounds simple enough in theory. Data privacy and cybersecurity are top of mind for everybody and prioritizing them go hand in hand with any technology innovation nowadays, including AI. The reality is that data privacy and data sharing are rightfully sensitive subjects. This, coupled with widespread government mistrust, is a legitimate hurdle that decision makers must evaluate to effectively provide access to and take our AI capabilities to the next level.

In the last five to 10 years, China has made leaps and bounds forward in the AI marketplace through the establishment of its Next Generation Artificial Intelligence Development Plan. While our ecosystems differ, the progress China has made in a short time shows that access to tremendous volumes of datasets is an advantage in AI advancement. It is also triggering a domino effect.

Government action in the U.S. is rampant. Recently, in June, President Biden established the National AI Research Task Force, which follows former President Trump’s 2019 executive order to fast-track the development and regulation of AI – signs that American leaders are eager to dominate the race.

While the benefits of AI are clear, we must acknowledge consumer expectations as the technology progresses. Data around new and emerging use cases shows that the more consumers are exposed to the benefits of AI in their daily lives, the more likely they are to value its advancements.

According to new data from the Deloitte AI Institute and the U.S. Chamber of Commerce’s Technology Engagement Center, 65 percent of survey respondents indicated that consumers would gain confidence in AI as the pace of discovery of new medicines, materials and other technologies accelerated through the use of AI. Respondents were also positive about the impact government investment could have in accelerating AI growth. The conundrum is that the technology remains hard to understand and relate to for many consumers.

While technology literacy in general has progressed thanks to the internet and digital connectivity, general awareness around data privacy, digital security and how data is used in AI remains weak. So, as greater demands are put on the collection, integration and sharing of consumer data, better transparency, education and standards around how data is collected, shared and used must be prioritized simultaneously. With this careful balance we could accelerate innovation at a rapid pace.

The data speaks for itself. The more of it we have, the stronger the results. Just like supply chain management of raw materials is critical in manufacturing, data supply chain management is critical in AI. One area that many organizations prioritize when implementing AI technology is applying more rigorous methods around data provenance and organization. Raw collected data is often transformed, pre-processed, summarized or aggregated at multiple stages in the data pipeline, complicating efforts to track and understand the history and origin of inputs to AI training. The quality and fit of resultant models – the ability for the model to make accurate decisions – is primarily a function of the corpus of data they were trained on, so it is imperative to identify what datasets were used and where they originated.

Datasets must be broad and show enough examples and variations for models to be correctly trained on. When they are not, the consequences can be severe. For instance, in the absence of sufficient datasets, AI-based face recognition models have reinforced racial profiling in some cases and AI algorithms for healthcare risk predictions have left minorities with less access to critical care.

With so much on the line, diverse data with strong data supply chain management is important, but there are limits to how much data a single company can collect. Enter the challenges of data sharing, data privacy and the issue of which information individuals are willing to hand over. We are seeing this play out through medical applications of AI, i.e., radiology images and medical records, and in other aspects of day-to-day life, from self-driving cars to robotics.

For many, granting access to personal data is more appealing if the purpose is to advance potentially life-saving technology, versus use cases that may appear more leisurely. This makes it critical that leading AI advancements prioritize the use cases that consumers deem most valuable, while remaining transparent about how data is being processed and implemented.

Two recent developments – the National AI Research Task Force and the NYC Cyber Attack Defense Center – are positive steps forward. While AI organizations and leaders will continue to drive innovation, forming these groups could be the driver in bringing AI to the forefront of technology advancement in the U.S. The challenge will be whether the action that they propose is impressive enough to consumers and outweighs privacy concerns and government mistrust.

Advancements in AI are driving insights and innovation across industries. As AI leaders it is up to us to continue the momentum and collaborate to accelerate AI innovation safely. For us to succeed, industry leaders must prioritize privacy and security around data collection and custodianship, create transparency around data management practices and invest in education and training to gain public trust.

The inner workings of AI technology are not as discernable as most popular applications and will remain that way for some time – but how data is collected and used must not be so hard for consumers to see and understand.

About the Author

Rob Lee is the Chief Technology Officer at Pure Storage, where he is focused on global technology strategy, and identifying new innovation and market expansion opportunities for the company. He joined Pure in 2013 after 12 years at Oracle Corp. He serves on the board of directors for Bay Area Underwater Explorers and Cordell Marine Sanctuary Foundation. Lee earned a bachelor’s degree and a master’s degree in electrical engineering and computer science from the Massachusetts Institute of Technology.