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The meetings will be held on December 14, 15, and 16, 2021, from 1:00 p.m. to 3:30 p.m., Eastern Standard Time.
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Published Document: 2021-25999 (86 FR 67925)
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AGENCY:
National Telecommunications and Information Administration, U.S. Department of Commerce.
ACTION:
Notice of open meeting.
SUMMARY:
The National Telecommunications and Information Administration (NTIA) will convene three virtual Listening Sessions about issues and potential solutions at the intersection of privacy, equity, and civil rights. The sessions will help to provide the data for a report on the ways in which commercial data flows of personal information can lead to disparate impact and outcomes for marginalized or disadvantaged communities.
DATES:
The meetings will be held on December 14, 15, and 16, 2021, from 1:00 p.m. to 3:30 p.m., Eastern Standard Time.
ADDRESSES:
The meetings will be held virtually, with online slide share and dial-in information to be posted at
https://www.ntia.gov/.
FOR FURTHER INFORMATION CONTACT:
Travis Hall, National Telecommunications and Information Administration, U.S. Department of Commerce, 1401 Constitution Avenue NW, Room 4725, Washington, DC 20230; telephone: (202) 482-3522; email:
thall@ntia.gov.
Please direct media inquiries to NTIA's Office of Public Affairs: (202) 482-7002; email:
press@ntia.gov.
SUPPLEMENTARY INFORMATION:
Background and Authority:
The National Telecommunications and Information Administration (NTIA) is the President's principal advisor on telecommunications and information policy issues.[1]
In this role, NTIA studies and develops policy advice about the impact of technology and the internet on privacy. This includes examining the extent to which technology implementations, business models, and related data processing are adequately addressed by the U.S.'s current privacy protection framework.[2]
Importantly, NTIA has long acknowledged that privacy is a matter of contextual data flow and use rather than simply being a question of publicity.[3]
Increasingly,
( printed page 67926)
scholarship has shown that marginalized or underserved communities are especially in need of robust privacy protections.[4]
These studies have shown that not only are these communities often materially disadvantaged with regards to the marginal effort required to adequately manage privacy controls, they are often at increased risk of suffering harm from losses of privacy or misuse of collected data.
The Administration has highlighted that there is a national imperative to promote equity and increase support for communities and individuals that have been “historically underserved, marginalized, and adversely affected by persistent poverty and inequality.” [5]
As stated in the Executive Order on Advancing Racial Equity and Support for Underserved Communities Through the Federal Government: “[e]ntrenched disparities in our laws and public policies, and in our public and private institutions, have often denied [. . .] equal opportunity to individuals and communities.” [6]
These entrenched disparities persist in the digital economy, and the collection, processing, sharing, and use of data can directly affect—both positively and negatively—structural inequities present in our society.
The following examples underscore how commercial collection and use of personal information, even for legitimate purposes, often results in disparate outcomes for marginalized and underserved communities:
Digital advertising systems have been shown to often reproduce historical patterns of discrimination by enabling discriminatory targeting by advertisers.[7]
Even when targeting criteria does not include protected traits, targeted advertising can be used to perpetuate discrimination using proxy indicators of race, gender, disability, and other characteristics.[8]
Data brokers, health insurance companies, and their subsidiaries are using information such as neighborhood safety, bankruptcies, gun ownership, inferred hobbies, and other information to determine coverage for people they deem more likely to require more expensive care.[9]
These assessments can rely on unreliable and discriminatory heuristics or proxies for characteristics such as race, socioeconomic status, or disability—or as one salesman joked, “God forbid you live on the wrong street these days,” he said. “You're going to get lumped in with a lot of bad things.”[10]
Software implemented by a university to predict whether students will struggle academically used race as a strong predictor for poor performance.[11]
Black students were flagged “high risk” for dropping out of certain subjects, such as science and math, at elevated rates, a designation that researchers warned could improperly lead to advisors encourage students to change to “easier” majors.[12]
In light of these and many more examples, it is critical for policymakers to understand how information policy can reduce data-driven discrimination and disparate treatment. In service of these objectives, NTIA announces through this Notice three virtual Listening Sessions, which aim to advance the policy conversation on how to alleviate the disproportionate privacy harms suffered by marginalized or underserved communities. NTIA's upcoming Listening Sessions are intended as an opportunity to build the factual record for further policy development in this area. The information gathered from these Listening Sessions will inform a subsequent Request for Comment, and together these efforts will provide the basis for NTIA to draft a report. Possible topics include, but are not limited to:
The role and adequacy of current civil rights laws, related protections, and enforcement thereof in mitigating privacy harms against marginalized communities.
The interplay between current civil rights laws and related protections with current privacy laws and proposed reforms.
Data brokers and secondary markets for data.
Exploitation of data or commercially available software for stalking or harassment based on protected class status.
Workplace tracking and surveillance that may be discriminatory.
Hiring, credit, lending, and housing algorithms and advertisements.
Intersectional privacy needs of groups such as trans individuals, the unhoused, or people with disabilities.
The format of the Listening Sessions will include a mix of keynote speeches, moderated panel discussions, and open forums for members of the public to share their perspective. The first Listening session will be held on December 14, 2021, on the intersection of civil rights law and privacy. The second Listening session will be held on December 15, 2021, and will be on the
( printed page 67927)
way in which the collection, use, and processing of personal and personally sensitive data affects structural inequities. The final Listening session will focus on solutions to the gaps and problems identified in the first two sessions, and will be held on December 16, 2021.
NTIA intends to publish a Notice and Request for Comments in the
Federal Register
that will be informed by the input received during the Listening Sessions. Members of the public unable to participate in the Listening Sessions are encouraged to respond to the forthcoming Request for Comments.
Time and Date:
NTIA will convene three virtual Listening Sessions on December 14, 15, and 16, 2021, from 1:00 p.m. to 3:30 p.m., Eastern Standard Time. The exact time of the meeting is subject to change. Please refer to NTIA's website,
https://www.ntia.gov,
for the most current information.
Place:
The meeting will be held virtually, with online slide share and dial-in information to be posted at
https://www.ntia.gov.
Please refer to NTIA's website,
https://www.ntia.gov,
for the most current information.
Other Information:
The meeting is open to the public and the press on a first-come, first-served basis. The virtual meetings are accessible to people with disabilities. Individuals requiring accommodations such as real-time captioning, sign language interpretation or other ancillary aids should notify Travis Hall at (202) 482-3522 or
thall@ntia.gov
at least seven (7) business days prior to the meeting. Access details for the meeting are subject to change. Please refer to NTIA's website,
https://www.ntia.gov/,
for the most current information.
Dated: November 23, 2021.
Kathy D. Smith,
Chief Counsel, National Telecommunications and Information Administration.
3.
See
Internet Policy Task Force,
Commercial Data Privacy and Innovation in the Internet Economy: A Dynamic Policy
18 (Dec. 16, 2010),
https://www.ntia.doc.gov/files/ntia/publications/iptf_privacy_greenpaper_12162010.pdf;
White House,
Consumer Data Privacy in a Networked World: A Framework for Protecting Privacy and Promoting Innovation in the Global Digital Economy,
(Feb. 23, 2012), 16;
see also:
Helen Nissenbaum,
Privacy in Context,
(Nov. 2009). NTIA considers problematic uses and problematic collection to both fall under the umbrella of a “privacy harm,” an idea that is well-established in the literature. (
https://papers.ssrn.com/sol3/
papers.cfm?abstract_id=3782222,
21-22 (“Privacy harms are highly contextual, with the harm depending upon how the data is used, what data is involved, and also how the data might be combined with other data”)).
4.
Danielle Keats-Citron,
Cyber Civil Rights,
89 Boston U. L. Rev. 61 (2008); Khiara Bridges,
The Poverty of Privacy Rights,
Stanford University Press (2017); Mary Madden, Michele Gilman, Karen Levy & Alice Marwick,
Privacy, Poverty, and Big Data: A Matrix Of Vulnerabilities For Poor Americans,
95 Wash. U. L. Rev. 53 (2017); Alvaro Bedoya,
Privacy As Civil Right,
50 New Mexico L. Rev. 3 (2020); Scott Skinner-Thompson,
Privacy At The Margins,
Cambridge University Press (2020); Sara Sternberg Greene,
Stealing (Identity) From The Poor
(2021),
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3781921;
Michele Gilman,
Feminism, Privacy, And Law In Cyberspace,
Oxford Handbook of Feminism and Law in the U.S. (2021 Forthcoming),
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3779323.
7.
Muhammad Ali et al.,
Discrimination Through Optimization: How Facebook's Ad Delivery Can Lead to Skewed Outcomes,
Computers and Society (April 19, 2019),
https://arxiv.org/abs/1904.02095.
9.
Marshall Allen,
Health Insurers Are Vacuuming Up Details About You—And It Could Raise Your Rates,
Pro Publica (July 17, 2018),
https://www.propublica.org/article/health-insurers-are-vacuuming-up-details-about-you-and-it-could-raise-your-rates;
Sarah Jeong,
Insurers Want To Know How Many Steps You Took Today,
The New York Times (April 10, 2019),
https://www.nytimes.com/2019/04/10/opinion/insurance-ai.html
(“But when it comes to insurance in particular, there are unanswered questions about the kind of biases that are acceptable. Discrimination based on genetics has already been deemed repugnant, even if it's perfectly rational. Poverty might be a rational indicator of risk, but should society allow companies to penalize the poor?”).
10.
Marshall Allen,
Health Insurers Are Vacuuming Up Details About You—And It Could Raise Your Rates,
Pro Publica (July 17, 2018),
https://www.propublica.org/article/health-insurers-are-vacuuming-up-details-about-you-and-it-could-raise-your-rates; see also,
Rachel Goodman,
Big Data Could Set Insurance Premiums. Minorities Could Pay the Price,
ACLU (July 19, 2018),
https://www.aclu.org/blog/racial-justice/race-and-economic-justice/big-data-could-set-insurance-premiums-minorities-could
(“Existing health disparities mean that data will consistently show members of certain groups to be more likely to need more health care. What will happen, then, if this data starts being used against those groups? We know, for example, that Black women are much more likely to experience serious complications from pregnancy than white women. So, health insurers might conclude that a woman who is Black and recently married is likely to cost them more money than a white woman in the same position”). Starre Vartan,
Racial Bias Found in a Major Health Care Risk Algorithm,
Scientific American (Oct. 24, 2019),
https://www.scientificamerican.com/article/racial-bias-found-in-a-major-health-care-risk-algorithm/
(“A study published Thursday in
Science
has found that a health care risk-prediction algorithm, a major example of tools used on more than 200 million people in the U.S., demonstrated racial bias—because it relied on a faulty metric [previous patients' health care spending as a proxy for medical needs].”).