The U.S. government’s effort to take advantage of AI has not lived up to its promise, according to a new report.
What’s new: Implementations of machine learning systems by federal agencies are “uneven at best, and problematic and perhaps dangerous at worst,” said authors of a survey by the Administrative Conference of the United States, Stanford Law School, and New York University School of Law.
What they found: Less than half of civilian federal agencies surveyed used some form of AI, and about 7 percent of them accounted for the lion’s share of AI implementations evaluated. The most common implementations were in law enforcement, health care, and financial regulations. Examples include the Border Patrol’s use of face recognition for its Biometric Entry/Exit program and the Securities and Exchange Commission’s Corporate Issuer Risk Assessment, which helps regulators detect faults in companies’ financial reports.
- Only 12 percent of implementations used deep learning. The rest used approaches such as logistic regression with structured data (which the authors deemed lower sophistication) or random forests with hyperparameter tuning (which they judged medium sophistication).
- Government agencies are legally required to explain their decisions, such as why a person was denied benefits. But algorithms often reach conclusions for reasons that are not explainable, making it difficult to appeal.
- Around half of the systems evaluated were developed by outside contractors. The authors recommend greater investment in in-house talent because it’s more likely to tailor systems appropriately to government uses.
Yes, but: The authors relied primarily on publicly available information, which may not contain sufficient technical perspective for such analysis. In addition, the survey period ended in August 2019, so the report excludes systems deployed since then.
Why it matters: AI could help government agencies operate more effectively and efficiently, but this report shows that they have a long way to go to fulfill that vision.
We’re thinking: Governments have an obligation to audit AI systems for performance, fairness, and compliance before rolling them out. Yet most agencies (and, for that matter, most corporations) don’t have the capability to assess these factors. We need tools that that enable a variety of stakeholders to define clear standards and assess whether they’ve been met, so we can spot problems, mitigate risks, and build trust in automated systems. We hope that companies such as Credo AI (which is backed by Andrew Ng’s AI Fund) can help.