New Data Rules Could Empower Patients but Undermine Their Privacy

Natasha Singer
The New York Times
Originally posted 9 March 20

Here is an excerpt:

The Department of Health and Human Services said the new system was intended to make it as easy for people to manage their health care on smartphones as it is for them to use apps to manage their finances.

Giving people access to their medical records via mobile apps is a major milestone for patient rights, even as it may heighten risks to patient privacy.

Prominent organizations like the American Medical Association have warned that, without accompanying federal safeguards, the new rules could expose people who share their diagnoses and other intimate medical details with consumer apps to serious data abuses.

Although Americans have had the legal right to obtain a copy of their personal health information for two decades, many people face obstacles in getting that data from providers.

Some physicians still require patients to pick up computer disks — or even photocopies — of their records in person. Some medical centers use online portals that offer access to basic health data, like immunizations, but often do not include information like doctors’ consultation notes that might help patients better understand their conditions and track their progress.

The new rules are intended to shift that power imbalance toward the patient.

The info is here.

A.I. Systems Echo Biases They’re Fed, Putting Scientists on Guard

Cade Metz
The New York Times
Originally published Nov 11, 2019

Here is the conclusion:

“This is hard. You need a lot of time and care,” he said. “We found an obvious bias. But how many others are in there?”

Dr. Bohannon said computer scientists must develop the skills of a biologist. Much as a biologist strives to understand how a cell works, software engineers must find ways of understanding systems like BERT.

In unveiling the new version of its search engine last month, Google executives acknowledged this phenomenon. And they said they tested their systems extensively with an eye toward removing any bias.

Researchers are only beginning to understand the effects of bias in systems like BERT. But as Dr. Munro showed, companies are already slow to notice even obvious bias in their systems. After Dr. Munro pointed out the problem, Amazon corrected it. Google said it was working to fix the issue.

Primer’s chief executive, Sean Gourley, said vetting the behavior of this new technology would become so important, it will spawn a whole new industry, where companies pay specialists to audit their algorithms for all kinds of bias and other unexpected behavior.

“This is probably a billion-dollar industry,” he said.

The whole article is here.

Advice for technologists on promoting AI ethics

Joe McKendrick
www.zdnet.com
Originally posted July 13, 2019

Ethics looms as a vexing issue when it comes to artificial intelligence (AI). Where does AI bias spring from, especially when it's unintentional? Are companies paying enough attention to it as they plunge full-force into AI development and deployment? Are they doing anything about it? Do they even know what to do about it?

Wringing bias and unintended consequences out of AI is making its way into the job descriptions of technology managers and professionals, especially as business leaders turn to them for guidance and judgement. The drive to ethical AI means an increased role for technologists in the business, as described in a study of 1,580 executives and 4,400 consumers from the Capgemini Research Institute. The survey was able to make direct connections between AI ethics and business growth: if consumers sense a company is employing AI ethically, they'll keep coming back; it they sense unethical AI practices, their business is gone.

Competitive pressure is the reason businesses are pushing AI to its limits and risking crossing ethical lines. "The pressure to implement AI is fueling ethical issues," the Capgemini authors, led by Anne-Laure Thieullent, managing director of Capgemini's Artificial Intelligence & Analytics Group, state. "When we asked executives why ethical issues resulting from AI are an increasing problem, the top-ranked reason was the pressure to implement AI." Thirty-four percent cited this pressure to stay ahead with AI trends.

The info is here.

Mark Zuckerberg Has No Way Out of Facebook's Quagmire

Leonid Bershidsky
Bloomberg News
Originally posted March 21, 2018

Here is an excerpt:

"Making sure time spent on Facebook is time well spent," as Zuckerberg puts it, should lead to the collection of better-quality data. If nobody is setting up fake accounts to spread disinformation, users are more likely to be their normal selves. Anyone analyzing these healthier interactions will likely have more success in targeting commercial and, yes, political offerings to real people. This would inevitably be a smaller yet still profitable enterprise, and no longer a growing one, at least in the short term. But the Cambridge Analytica scandal shows people may not be okay with Facebook's data gathering, improved or not.

The scandal follows the revelation (to most Facebook users who read about it) that, until 2015, application developers on the social network's platform were able to get information about a user's Facebook friends after asking permission in the most perfunctory way. The 2012 Obama campaign used this functionality. So -- though in a more underhanded way -- did Cambridge Analytica, which may or may not have used the data to help elect President Donald Trump.

Many people are angry at Facebook for not acting more resolutely to prevent CA's abuse, but if that were the whole problem, it would have been enough for Zuckerberg to apologize and point out that the offending functionality hasn't been available for several years. The #deletefacebook campaign -- now backed by WhatsApp co-founder Brian Acton, whom Facebook made a billionaire -- is, however, powered by a bigger problem than that. People are worried about the data Facebook is accumulating about them and about how these data are used. Facebook itself works with political campaigns to help them target messages; it did so for the Trump campaign, too, perhaps helping it more than CA did.

The article is here.

First Question: Should you stop using Facebook because they violated your trust?

Second Question: Is Facebook a defective product?

Apple’s Move to Share Health Care Records Is a Game-Changer

Aneesh Chopra and Safiq Rab
wired.com
Originally posted February 19, 2018

Here is an excerpt:

Naysayers point out the fact that Apple is currently displaying only a sliver of a consumer’s entire electronic health record. That is true, but it's largely on account of the limited information available via the open API standard. As with all standards efforts, the FHIR API will add more content, like scheduling slots and clinical notes, over time. Some of that work will be motivated by proposed federal government voluntary framework to expand the types of data that must be shared over time by certified systems, as noted in this draft approach out for public comment.

Imagine if Apple further opens up Apple Health so it no longer serves as the destination, but a conduit for a patient's longitudinal health record to a growing marketplace of applications that can help guide consumers through decisions to better manage their health.

Thankfully, the consumer data-sharing movement—placing the longitudinal health record in the hands of the patient and the applications they trust—is taking hold, albeit quietly. In just the past few weeks, a number of health systems that were initially slow to turn on the required APIs suddenly found the motivation to meet Apple's requirement.

The article is here.

Best-Ever Algorithm Found for Huge Streams of Data

Kevin Hartnett
Wired Magazine
Originally published October 29, 2017

Here is an excerpt:

Computer programs that perform these kinds of on-the-go calculations are called streaming algorithms. Because data comes at them continuously, and in such volume, they try to record the essence of what they’ve seen while strategically forgetting the rest. For more than 30 years computer scientists have worked to build a better streaming algorithm. Last fall a team of researchers invented one that is just about perfect.

“We developed a new algorithm that is simultaneously the best” on every performance dimension, said Jelani Nelson, a computer scientist at Harvard University and a co-author of the work with Kasper Green Larsen of Aarhus University in Denmark, Huy Nguyen of Northeastern University and Mikkel Thorup of the University of Copenhagen.

This best-in-class streaming algorithm works by remembering just enough of what it’s seen to tell you what it’s seen most frequently. It suggests that compromises that seemed intrinsic to the analysis of streaming data are not actually necessary. It also points the way forward to a new era of strategic forgetting.

The article is here.