On July 8, 2020, the House Financial Services Committee’s Taskforce on Artificial Intelligence held a hearing entitled “Exposure Notification and Contact Tracing: How AI Helps Localities Reopen Safely and Researchers Find a Cure.”

In his opening remarks, Congressman Bill Foster (D-IL), chairman of the task force, stated that the hearing would discuss the essential tradeoffs that the coronavirus disease 2019 (COVID-19) pandemic was forcing on the public between life, liberty, privacy and the pursuit of happiness. Chairman Foster noted that what he called “invasive” artificial intelligence (AI) surveillance may save lives, but would come at a tremendous cost to personal liberty. He said that contact tracing apps that use back-end AI, which combines raw data collected from voluntarily participating COVID-19-positive patients, may adequately address privacy concerns while still capturing similar health and economic benefits as more intrusive monitoring.

Congressman Barry Loudermilk (R-GA) discussed how digital contact tracing could be more effective than manual contact tracing, but noted that it must have strong participation ̶̶̶̶from people – 40-60 percent adoption rate overall – to be effective. He said that citizens would need to trust that their privacy would not be violated. To help establish this trust, he suggested, people would need to be able to easily determine what data would be collected, who would have access to the data and how the data would be used.

Four panelists testified at this hearing. Below is a summary of each panelist’s testimony, followed by an overview of some of the post-testimony questions that committee members raised:

Brian McClendon, the CEO and co-founder of the CVKey Project, discussed how privacy, disclosure and opt-in data collection impact the ability to identify and isolate those infected with COVID-19. AI and machine learning require large amounts of data. He stated that while the most valuable data to combat COVID-19 can be found in the contact-tracing interviews of infected and exposed people, difficulties exist in capturing this information. For example, attempted phone calls to reach exposed individuals may go unanswered because people often do not pick up calls from unknown numbers. Mobile apps, he said, offer a way to conduct contact tracing with greater accuracy and coverage. Mr. McClendon discussed two ways that such apps could work: (1) using GPS location or (2) via low-energy Bluetooth. For the latter, Mr. McClendon explained a method developed by two large technology companies: when a user of a digital contact tracing app tests positive for COVID-19, he or she then chooses to opt in to upload non-personally identifiable information to a state-run cloud server, which would then determine whether potential exposures have occurred and provide in-app notifications to such users.

Krutika Kuppalli, M.D., an infectious diseases physician, discussed how using contact tracing can help impede the spread of infectious diseases. She noted that it is important to remember ethical considerations involving public health information, data protection and data privacy when using these technologies.

Andre M. Perry, a fellow at the Brookings Institution, began his presentation by discussing how COVID-19 has disproportionately affected Black and Latino populations, reflecting historical inequalities and structural racism. Mr. Perry identified particular concerns regarding AI and contact tracing as they pertain to structural racism and bias. These tools, he stated, are not neutral and can either exacerbate or mitigate structural racism. To address such bias, he suggested, contact tracing should include people who have generally been excluded from systems that have provided better health and economic outcomes. Further, the use of AI tools in the healthcare arena presents the same risk as in other fields: the AI is only as good as the programmers who design it. Bias in programming can lead to flaws in technology and amplify biases in the real world. Mr. Perry stated that greater recruitment and investment with Black-owned tech firms, rigorous reviews and testing for bias and more engagement with local communities is required.

Ramesh Raskar, a professor at MIT and the founder of the PathCheck Foundation, emphasized three elements during his presentation: (1) how to augment manual contact tracing with apps; (2) how to make sure apps are privacy-preserving, inclusive, trustworthy, and built using open-source methods and nonprofits; and (3) the creation of a National Pandemic Response Service. Regarding inclusivity, Mr. Raskar noted that Congress should actively require that solutions be accessible broadly and generally; contact tracing cannot be effective only for segments of the population that have access to the latest technology.

Post-testimony questions

Chairman Foster asked about limits of privacy-preserving techniques by providing an example of a person who had been isolated for a week, then interacted with only one other person, and then later received a notification of exposure: such a person likely will know the identity of the infected person.  Mr. Raskar replied that data protection has different layers: confidentiality, anonymity, and then privacy. In public health scenarios, Mr. Raskar stated that today, we only care about confidentiality and not anonymity or privacy (eventually, he commented, you will have to meet a doctor).

If we were to implement a federal contact tracing program, Representative Loudermilk asked, how would we ensure citizens that they can know what data will be used and collected, and who has access? Mr. McClendon responded that under the approach developed by the two large technology companies, data is random and stored on a personal phone until the user opts in to upload random numbers to the server. The notification determination is made on the phone and the state provides the messages. The state will not know who the exposed person is until that person opts in by calling the manual contact tracing team.

Representative Maxine Waters (D-CA) asked what developers of a mobile contact tracing technology should consider to ensure that minority communities are not further disadvantaged. Mr. Perry reiterated that AI technologies have not been tested, created, or vetted by persons of color, which has led to various biases.

Congressman Sean Casten (D-IL) asked whether AI used in contact tracing is solely backward-looking or could predict future hotspots. Mr. McClendon replied that to predict the future, you need to know the past. Manual contact tracing interviews, where an infected or exposed person describes where he or she has been, would provide significant data to include in a machine-learning algorithm, enabling tracers to predict where a hotspot might occur in the future. However, privacy issues and technological incompatibility (e.g., county and state tools that are not compatible with each other) mean that a lot of data is currently siloed and even inaccessible, impeding the ability for AI to look forward.

Please visit our Coronavirus Resource Center and subscribe to our mailing list to receive alerts, webinar invitations and other publications to help you navigate this challenging time.