The U.S. Census Is Wrong on Purpose

Blog by David Friedman: “This is a story about data manipulation. But it begins in a small Nebraska town called Monowi that has only one resident, 90 year old Elsie Eiler.

The sign says “Monowi 1,” from Google Street View.

There used to be more people in Monowi. But little by little, the other residents of Monowi left or died. That’s what happened to Elsie’s own family — her children grew up and moved out and her husband passed away in 2004, leaving her as the sole resident. Now she votes for herself for Mayor, and pays herself taxes. Her husband Rudy’s old book collection became the town library, with Elsie as librarian.

But despite what you might imagine, Elsie is far from lonely. She runs a tavern that’s been in her family for 50 years, and has plenty of regulars from the town next door who come by every day to dine and chat.

I first read about Elsie more than 10 years ago. At the time, it wasn’t as well known a story but Elsie has since gotten a lot of coverage and become a bit of a minor celebrity. Now and then I still come across a new article, including a lovely photo essay in the New York Times and a short video on the BBC Travel site.

A Google search reveals many, many similar articles that all tell more or less the same story.

But then suddenly in 2021, there was a new wrinkle: According to the just-published 2020 U.S. Census data, Monowi now had 2 residents, doubling its population.

This came as a surprise to Elsie, who told a local newspaper, “Then someone’s been hiding from me, and there’s nowhere to live but my house.”

It turns out that nobody new had actually moved to Monowi without Elsie realizing. And the census bureau didn’t make a mistake. They intentionally changed the census data, adding one resident.

Why would they do that? Well, it turns out the census bureau sometimes moves residents around on paper in order to protect people’s privacy.

Full census data is only made available 72 years after the census takes place, in accordance with the creatively-named “72 year rule.” Until then, it is only available as aggregated data with individual identifiers removed. Still, if the population of a town is small enough, and census data for that town indicates, for example, that there is just one 90 year old woman and she lives alone, someone could conceivably figure out who that individual is.

So the census bureau sometimes moves people around to create noise in the data that makes that sort of identification a little bit harder…(More)”.

Air Canada chatbot promised a discount. Now the airline has to pay it

Article by Kyle Melnick: “After his grandmother died in Ontario a few years ago, British Columbia resident Jake Moffatt visited Air Canada’s website to book a flight for the funeral. He received assistance from a chatbot, which told him the airline offered reduced rates for passengers booking last-minute travel due to tragedies.

Moffatt bought a nearly $600 ticket for a next-day flight after the chatbot said he would get some of his money back under the airline’s bereavement policy as long as he applied within 90 days, according to a recent civil-resolutions tribunal decision.

But when Moffatt later attempted to receive the discount, he learned that the chatbot had been wrong. Air Canada only awarded bereavement fees if the request had been submitted before a flight. The airline later argued the chatbot wasa separate legal entity “responsible for its own actions,” the decision said.

Moffatt filed a claim with the Canadian tribunal, which ruled Wednesday that Air Canada owed Moffatt more than $600 in damages and tribunal fees after failing to provide “reasonable care.”

As companies have added artificial intelligence-powered chatbots to their websites in hopes of providing faster service, the Air Canada dispute sheds light on issues associated with the growing technology and how courts could approach questions of accountability. The Canadian tribunal in this case came down on the side of the customer, ruling that Air Canada did not ensure its chatbot was accurate…(More)”

To Design Cities Right, We Need to Focus on People

Article by Tim Keane: “Our work in the U.S. to make better neighborhoods, towns and cities is a hapless and obdurate mess. If you’ve attended a planning meeting anywhere, you have probably witnessed the miserable process in action—unrestrainedly selfish fighting about false choices and seemingly inane procedures. Rather than designing places for people, we see cities as a collection of mechanical problems with technical and legal solutions. We distract ourselves with the latest rebranded ideas about places—smart growth, resilient cities, complete streets, just cities, 15-minute cities, happy cities—rather than getting down to the actual work of designing the physical place. This lacks a fundamental vision. And it’s not succeeding.

Our flawed approach to city planning started a century ago. The first modern city plan was produced for Cincinnati in 1925 by the Technical Advisory Corporation, founded in 1913 by George Burdett Ford and E.P. Goodrich in New York City. New York adopted the country’s first comprehensive zoning ordinance in 1916, an effort Ford led. Not coincidentally, the advent of zoning, and then comprehensive planning, corresponded directly with the great migration of six million Black people from the South to Northern, Midwestern and Western cities. New city planning practices were a technical means to discriminate and exclude.

This first comprehensive plan also ushered in another type of dehumanization: city planning by formula. To justify widening downtown streets by cutting into sidewalks, engineers used a calculation that reflected the cost to operate an automobile in a congested area—including the cost of a human life, because crashes killed people. Engineers also calculated the value of a sidewalk through a formula based on how many people the elevators in adjoining buildings could deliver at peak times. In the end, Cincinnati’s planners recommended widening the streets for cars, which were becoming more common, by shrinking sidewalks. City planning became an engineering equation, and one focused on separating people and spreading the city out to the maximum extent possible…(More)”.

University of Michigan Sells Recordings of Study Groups and Office Hours to Train AI

Article by Joseph Cox: “The University of Michigan is selling hours of audio recordings of study groups, office hours, lectures, and more to outside third-parties for tens of thousands of dollars for the purpose of training large language models (LLMs). 404 Media has downloaded a sample of the data, which includes a one hour and 20 minute long audio recording of what appears to be a lecture.

The news highlights how some LLMs may ultimately be trained on data with an unclear level of consent from the source subjects. ..(More)”.

Toward a 21st Century National Data Infrastructure: Managing Privacy and Confidentiality Risks with Blended Data

Report by the National Academies of Sciences, Engineering, and Medicine: “Protecting privacy and ensuring confidentiality in data is a critical component of modernizing our national data infrastructure. The use of blended data – combining previously collected data sources – presents new considerations for responsible data stewardship. Toward a 21st Century National Data Infrastructure: Managing Privacy and Confidentiality Risks with Blended Data provides a framework for managing disclosure risks that accounts for the unique attributes of blended data and poses a series of questions to guide considered decision-making.

Technical approaches to manage disclosure risk have advanced. Recent federal legislation, regulation and guidance has described broadly the roles and responsibilities for stewardship of blended data. The report, drawing from the panel review of both technical and policy approaches, addresses these emerging opportunities and the new challenges and responsibilities they present. The report underscores that trade-offs in disclosure risks, disclosure harms, and data usefulness are unavoidable and are central considerations when planning data-release strategies, particularly for blended data…(More)”.

AI cannot be used to deny health care coverage, feds clarify to insurers

Article by Beth Mole: “Health insurance companies cannot use algorithms or artificial intelligence to determine care or deny coverage to members on Medicare Advantage plans, the Centers for Medicare & Medicaid Services (CMS) clarified in a memo sent to all Medicare Advantage insurers.

The memo—formatted like an FAQ on Medicare Advantage (MA) plan rules—comes just months after patients filed lawsuits claiming that UnitedHealth and Humana have been using a deeply flawed AI-powered tool to deny care to elderly patients on MA plans. The lawsuits, which seek class-action status, center on the same AI tool, called nH Predict, used by both insurers and developed by NaviHealth, a UnitedHealth subsidiary.

According to the lawsuits, nH Predict produces draconian estimates for how long a patient will need post-acute care in facilities like skilled nursing homes and rehabilitation centers after an acute injury, illness, or event, like a fall or a stroke. And NaviHealth employees face discipline for deviating from the estimates, even though they often don’t match prescribing physicians’ recommendations or Medicare coverage rules. For instance, while MA plans typically provide up to 100 days of covered care in a nursing home after a three-day hospital stay, using nH Predict, patients on UnitedHealth’s MA plan rarely stay in nursing homes for more than 14 days before receiving payment denials, the lawsuits allege…(More)”

Nobody knows how to audit AI

Axios: “Some legislators and experts are pushing independent auditing of AI systems to minimize risks and build trust, Ryan reports.

Why it matters: Consumers don’t trust big tech to self-regulate and government standards may come slowly or never.

The big picture: Failure to manage risk and articulate values early in the development of an AI system can lead to problems ranging from biased outcomes from unrepresentative data to lawsuits alleging stolen intellectual property.

Driving the news: Sen. John Hickenlooper (D-Colo.) announced in a speech on Monday that he will push for the auditing of AI systems, because AI models are using our data “in ways we never imagined and certainly never consented to.”

  • “We need qualified third parties to effectively audit generative AI systems,” Hickenlooper said, “We cannot rely on self-reporting alone. We should trust but verify” claims of compliance with federal laws and regulations, he said.

Catch up quick: The National Institute of Standards and Technology (NIST) developed an AI Risk Management Framework to help organizations think about and measure AI risks, but it does not certify or validate AI products.

  • President Biden’s executive order on AI mandated that NIST expand its support for generative AI developers and “create guidance and benchmarks for evaluating and auditing AI capabilities,” especially in risky areas such as cybersecurity and bioweapons.

What’s happening: A growing range of companies provide services that evaluate whether AI models are complying with local regulations or promises made by their developers — but some AI companies remain committed to their own internal risk research and processes.

  • NIST is only the “tip of the spear” in AI safety, Hickenlooper said. He now wants to establish criteria and a path to certification for third-party auditors.

The “Big Four” accounting firms — Deloitte, EY, KPMG and PwC — sense business opportunities in applying audit methodologies to AI systems, Nicola Morini Bianzino, EY’s global chief technology officer, tells Axios.

  • Morini Bianzino cautions that AI audits might “look more like risk management for a financial institution, as opposed to audit as a certifying mark. Because, honestly, I don’t know technically how we would do that.”
  • Laura Newinski, KPMG’s COO, tells Axios the firm is developing AI auditing services and “attestation about whether data sets are accurate and follow certain standards.”

Established players such as IBM and startups such as Credo provide AI governance dashboards that tell clients in real time where AI models could be causing problems — around data privacy, for example.

  • Anthropic believes NIST should focus on “building a robust and standardized benchmark for generative AI systems” that all private AI companies can adhere to.

Market leader OpenAI announced in October that it’s creating a “risk-informed development policy” and has invited experts to apply to join its OpenAI Red Teaming Network.

Yes, but: An AI audit industry without clear standards could be a recipe for confusion, both for corporate customers and consumers using AI…(More)”.

Future-Proofing Transparency: Re-Thinking Public Record Governance For the Age of Big Data

Paper by Beatriz Botero Arcila: “Public records, public deeds, and even open data portals often include personal information that can now be easily accessed online. Yet, for all the recent attention given to informational privacy and data protection, scant literature exists on the governance of personal information that is available in public documents. This Article examines the critical issue of balancing privacy and transparency within public record governance in the age of Big Data.

With Big Data and powerful machine learning algorithms, personal information in public records can easily be used to infer sensitive data about people or aggregated to create a comprehensive personal profile of almost anyone. This information is public and open, however, for many good reasons: ensuring political accountability, facilitating democratic participation, enabling economic transactions, combating illegal activities such as money laundering and terrorism financing, and facilitating. Can the interest in record publicity coexist with the growing ease of deanonymizing and revealing sensitive information about individuals?

This Article addresses this question from a comparative perspective, focusing on US and EU access to information law. The Article shows that the publicity of records was, in the past and not withstanding its presumptive public nature, protected because most people would not trouble themselves to go to public offices to review them, and it was practical impossible to aggregate them to draw extensive profiles about people. Drawing from this insight and contemporary debates on data governance, this Article challenges the binary classification of data as either published or not and proposes a risk-based framework that re-insert that natural friction to public record governance by leveraging techno-legal methods in how information is published and accessed…(More)”.

Creating Real Value: Skills Data in Learning and Employment Records

Article by Nora Heffernan: “Over the last few months, I’ve asked the same question to corporate leaders from human resources, talent acquisition, learning and development, and management backgrounds. The question is this:

What kind of data needs to be included in learning and employment records to be of greatest value to you in your role and to your organization?

By data, I’m talking about credential attainment, employment history, and, emphatically, verified skills data: showing at an individual level what a candidate or employee knows and is able to do.

The answer varies slightly by industry and position, but unanimously, the employers I’ve talked to would find the greatest value in utilizing learning and employment records that include verified skills data. There is no equivocation.

And as the national conversation about skills-first talent management continues to ramp up, with half of companies indicating they plan to eliminate degree requirements for some jobs in the next year, the call for verified skill data will only get louder. Employers value skills data for multiple reasons…(More)”.

Name Your Industry—or Else!

Essay by Sarah M. Brownsberger on “The dehumanizing way economics data describes us”: “…My alma mater wants to know what industry I belong to. In a wash of good feeling after seeing old friends, I have gone to the school website to update my contact information. Name and address, easy, marital status, well and good—but next comes a drop-down menu asking for my “industry.”

In my surprise, I have an impulse to type “Where the bee sucks, there suck I!” But you can’t quote Shakespeare in a drop-down menu. You can only opt only for its options.

The school is certainly cutting-edge. Like a fashion item that you see once and assume is aberrant and then see ten times in a week, the word “industry” is all over town. Cryptocurrency is an industry. So are Elvis-themed marriages. Outdoor recreation is an industry. A brewery in my city hosts “Industry Night,” a happy hour “for those who work in the industry”—tapsters and servers.

Are we all in an industry? What happened to “occupation”?…(More)”.