In Depth Daniel Castro testifying

Published on December 12th, 2017 | by

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Congressional Testimony for “Digital Decision-Making: The Building Blocks of Machine Learning and Artificial Intelligence”

The following is a transcript of Center for Data Innovation director Daniel Castro’s testimony at the Senate Subcommittee on Commerce, Science, and Transportation hearing titled, “Digital Decision-Making: The Building Blocks of Machine Learning and Artificial Intelligence.

Chairman Wicker, Ranking Member Schatz and members of the subcommittee, I appreciate the opportunity to appear before you today to discuss the importance of artificial intelligence to the U.S. economy and how best to govern this important technology.

AI has the potential to create a substantial and lasting impact on the economy by increasing the level of automation in virtually every sector, leading to more efficient processes and higher-quality outputs, and boosting productivity and per-capita incomes.

In the coming years, AI is expected to generate trillions of dollars of economic value and help businesses make smarter decisions, develop innovative products and services, and boost productivity.

For example, manufacturers are using AI to invent new metal alloys for 3D printing; pharmaceutical companies are using AI to discover new lifesaving drugs; and agricultural businesses are using AI to increase automation on farms.

Companies that use AI will have an enormous advantage compared to their peers that do not.

Therefore, the United States should prioritize policy initiatives that promote AI adoption in its traded sectors where U.S. firms will face international competition.

Many other countries already see the strategic importance of becoming lead adopters of AI, and they have begun implementing policies to pursue this goal.

  • For example, this past March, Canada launched the “Pan-Canadian Artificial Intelligence Strategy,” intended to help establish Canada as an international leader in AI research.
  • The UK’s new budget, published last month, includes several provisions that have the goal of making the UK a world leader in AI, such as by establishing a new research center and funding new PhDs.
  • Japan has created an “Artificial Intelligence Technology Strategy” to develop and commercialize AI.
  • And China has declared its intent to be the world’s “premier artificial intelligence innovation center” by 2030.

However, to date, the U.S. government has not declared its intent to be a global leader in this field, nor has it begun the even harder task of developing a strategy to achieve that vision.

Moreover, China, which has launched an ambitious program to dominate this field, has already surpassed the United States in terms of the total number of papers published and cited in some AI disciplines, such as deep learning.

The United States should not cede its existing advantages in AI. Instead, it should pursue a multi-prong national strategy to remain competitive in this field.

  • First, the federal government should continue to expand its funding to support strategic areas of AI in which industry is unlikely to invest, as well as better plan and coordinate federal funding for AI R&D across different agencies.
  • Second, the federal government should support educational efforts to ensure a strong pipeline of talent to create the next generation of AI researchers and developers, including through retraining and diversity programs, as well as pursue immigration policies that allow U.S. businesses to recruit and retain highly skilled computer scientists.
  • Third, federal and state regulators should conduct regulatory reviews to identify regulatory barriers to commercial use of AI in various industries, such as transportation, health care, education, and finance.
  • Fourth, the federal government should continue to supply high-value datasets that enable advances in AI, such as providing open access to standardize reference datasets for text analysis and facial recognition. Federal agencies should also facilitate data sharing between industry stakeholders, such as the Department of Transportation’s efforts to promote voluntary data exchange about the safety of autonomous vehicles.
  • And, fifth, the federal government should assess what type of economic data it needs to gather from businesses to monitor and evaluate AI adoption, much like it has tracked rural electrification or broadband connectivity as key economic indicators.

Now, as with any technology, there will be some risks and challenges associated with AI that require government oversight.

But the United States should not replicate the European approach to AI where rules creating a right to explanation and a right to human review for automated decisions risk severely curtailing the use of AI.

Instead, the United States should create its own innovation-friendly approach to providing oversight of the emerging algorithmic economy just as it has for the Internet economy.

Such an approach should prioritize sector-specific policies over comprehensive regulations, outcomes over transparency, and enforcement actions against firms that cause tangible harm over those that merely make missteps without injury.

In many cases, regulators will not need to intervene because the private sector will address problems about AI, such as bias or discrimination, on its own.

Moreover, given that U.S. companies are at the forefront of efforts to build AI that is safe and ethical, maintaining U.S. leadership in this field will be important to ensure these values remain embedded in the technology.

AI is a transformational technology that has the potential to significantly increase efficiency and innovation across the U.S. economy, creating higher living standards and improved quality of life. But while the United States has an early advantage in AI, many other countries are trying to be number one.

We need more leadership on this issue, and so I commend you for holding this hearing.

Thank you for the opportunity to be here today. I look forward to answering your questions.

Download the testimony.

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