"A federal class action lawsuit filed this week in Texas accused Toyota and an affiliated telematics aggregator of unlawfully collecting drivers' information and then selling that data to Progressive," reports Insurance Journal: The lawsuit alleges that Toyota and Connected Analytic Services (CAS) collected vast amounts of vehicle data, including location, speed, direction, braking and swerving/cornering events, and then shared that information with Progressive's Snapshot data sharing program. The class action seeks an award of damages, including actual, nominal, consequential damages, and punitive, and an order prohibiting further collection of drivers' location and vehicle data.
Florida man Philip Siefke had bought a new Toyota RAV4 XLE in 2021 "equipped with a telematics device that can track and collect driving data," according to the article. But when he tried to sign up for insurance from Progressive, "a background pop-up window appeared, notifying Siefke that Progressive was already in possession of his driving data, the lawsuit says. A Progressive customer service representative explained to Siefke over the phone that the carrier had obtained his driving data from tracking technology installed in his RAV4." (Toyota told him later he'd unknowingly signed up for a "trial" of the data sharing, and had failed to opt out.) The lawsuit alleges Toyota never provided Siefke with any sort of notice that the car manufacture would share his driving data with third parties... The lawsuit says class members suffered actual injury from having their driving data collected and sold to third parties including, but not limited to, damage to and diminution in the value of their driving data, violation of their privacy rights, [and] the likelihood of future theft of their driving data.
The telemetry device "can reportedly gather information about location, fuel levels, the odometer, speed, tire pressure, window status, and seatbelt status," notes CarScoop.com. "In January, Texas Attorney General Ken Paxton started an investigation into Toyota, Ford, Hyundai, and FCA..." According to plaintiff Philip Siefke from Eagle Lake, Florida, Toyota, Progressive, and Connected Analytic Services collect data that can contribute to a "potential discount" on the auto insurance of owners. However, it can also cause insurance premiums to be jacked up.
The plaintiff's lawyer issued a press release: Despite Toyota claiming it does not share data without the express consent of customers, Toyota may have unknowingly signed up customers for "trials" of sharing customer driving data without providing any sort of notice to them. Moreover, according to the lawsuit, Toyota represented through its app that it was not collecting customer data even though it was, in fact, gathering and selling customer information. We are actively investigating whether Toyota, CAS, or related entities may have violated state and federal laws by selling this highly sensitive data without adequate disclosure or consent...

If you purchased a Toyota vehicle and have since seen your auto insurance rates increase (or been denied coverage), or have reason to believe your driving data has been sold, please contact us today or visit our website at classactionlawyers.com/toyota-tracking.
On his YouTube channel, consumer protection attorney Steve Lehto shared a related experience he had — before realizing he wasn't alone. "I've heard that story from so many people who said 'Yeah, I I bought a brand new car and the salesman was showing me how to set everything up, and during the setup process he clicked Yes on something.' Who knows what you just clicked on?!"

Thanks to long-time Slashdot reader sinij for sharing the news.

An anonymous Slashdot reader quotes a new article from Ars Technica: On Thursday, mobile security company NowSecure reported that [DeepSeek] sends sensitive data over unencrypted channels, making the data readable to anyone who can monitor the traffic. More sophisticated attackers could also tamper with the data while it's in transit. Apple strongly encourages iPhone and iPad developers to enforce encryption of data sent over the wire using ATS (App Transport Security). For unknown reasons, that protection is globally disabled in the app, NowSecure said. What's more, the data is sent to servers that are controlled by ByteDance, the Chinese company that owns TikTok...

[DeepSeek] is "not equipped or willing to provide basic security protections of your data and identity," NowSecure co-founder Andrew Hoog told Ars. "There are fundamental security practices that are not being observed, either intentionally or unintentionally. In the end, it puts your and your company's data and identity at risk...." This data, along with a mix of other encrypted information, is sent to DeepSeek over infrastructure provided by Volcengine a cloud platform developed by ByteDance. While the IP address the app connects to geo-locates to the US and is owned by US-based telecom Level 3 Communications, the DeepSeek privacy policy makes clear that the company "store[s] the data we collect in secure servers located in the People's Republic of China...."

US lawmakers began pushing to immediately ban DeepSeek from all government devices, citing national security concerns that the Chinese Communist Party may have built a backdoor into the service to access Americans' sensitive private data. If passed, DeepSeek could be banned within 60 days.

Nature magazine reports that "A study that stoked enthusiasm for the now-disproven idea that a cheap malaria drug can treat COVID-19 has been retracted — more than four-and-a-half years after it was published." Researchers had critiqued the controversial paper many times, raising concerns about its data quality and an unclear ethics-approval process. Its eventual withdrawal, on the grounds of concerns over ethical approval and doubts about the conduct of the research, marks the 28th retraction for co-author Didier Raoult, a French microbiologist, formerly at Marseille's Hospital-University Institute Mediterranean Infection (IHU), who shot to global prominence in the pandemic. French investigations found that he and the IHU had violated ethics-approval protocols in numerous studies, and Raoult has now retired.

The paper, which has received almost 3,400 citations according to the Web of Science database, is the highest-cited paper on COVID-19 to be retracted, and the second-most-cited retracted paper of any kind....

Because it contributed so much to the HCQ hype, "the most important unintended effect of this study was to partially side-track and slow down the development of anti-COVID-19 drugs at a time when the need for effective treatments was critical", says Ole Søgaard, an infectious-disease physician at Aarhus University Hospital in Denmark, who was not involved with the work or its critiques. "The study was clearly hastily conducted and did not adhere to common scientific and ethical standards...."

Three of the study's co-authors had asked to have their names removed from the paper, saying they had doubts about its methods, the retraction notice said.
Nature includes this quote from a scientific-integrity consultant in San Francisco, California. "This paper should never have been published — or it should have been retracted immediately after its publication."

"The report caught the eye of the celebrity doctor Mehmet Oz," the Atlantic reported in April of 2020 (also noting that co-author Raoult "has made news in recent years as a pan-disciplinary provocateur; he has questioned climate change and Darwinian evolution...")

And Nature points out that while the study claimed good results for the 20 patients treated with HCQ, six more HCQ-treated people in the study actually dropped out before it was finished. And of those six people, one died, while three more "were transferred to an intensive-care unit."

Thanks to Slashdot reader backslashdot for sharing the news.

Mirnotoriety writes: Amid the growing warmth and increasingly volatile weather of an approaching summer, Australia passed a remarkable milestone this week. The number of homes and businesses with a solar installation clicked past 4 million -- barely 20 years since there was practically none anywhere in the country. It is a love affair that shows few signs of stopping.

And it's a technology that is having ever greater effects, not just on the bills of its household users but on the very energy system itself. At no time of the year is that effect more obvious than spring, when solar output soars as the days grow longer and sunnier but demand remains subdued as mild temperatures mean people leave their air conditioners switched off.

Such has been the extraordinary production of solar in Australia this spring, the entire state of South Australia has -- at various times -- met all of its electricity needs from the technology.

[...] [T]here is, at times, too much solar power in Australia's electricity systems to handle.

Long-time Slashdot reader theodp shared this warning from a long-time AI researcher arguing that data science "is due" for a reckoning over whether results can be reproduced. "Few technological revolutions came with such a low barrier of entry as Machine Learning..." Unlike Machine Learning, Data Science is not an academic discipline, with its own set of algorithms and methods... There is an immense diversity, but also disparities in skill, expertise, and knowledge among Data Scientists... In practice, depending on their backgrounds, data scientists may have large knowledge gaps in computer science, software engineering, theory of computation, and even statistics in the context of machine learning, despite those topics being fundamental to any ML project. But it's ok, because you can just call the API, and Python is easy to learn. Right...?

Building products using Machine Learning and data is still difficult. The tooling infrastructure is still very immature and the non-standard combination of data and software creates unforeseen challenges for engineering teams. But in my views, a lot of the failures come from this explosive cocktail of ritualistic Machine Learning:

- Weak software engineering knowledge and practices compounded by the tools themselves;
- Knowledge gap in mathematical, statistical, and computational methods, encouraged black boxing API;
- Ill-defined range of competence for the role of data scientist, reinforced by a pool of candidates with an unusually wide range of backgrounds;
- A tendency to follow the hype rather than the science.


- What can you do?

- Hold your data scientists accountable using Science.
- At a minimum, any AI/ML project should include an Exploratory Data Analysis, whose results directly support the design choices for feature engineering and model selection.
- Data scientists should be encouraged to think outside-of-the box of ML, which is a very small box - Data scientists should be trained to use eXplainable AI methods to provide context about the algorithm's performance beyond the traditional performance metrics like accuracy, FPR, or FNR.
- Data scientists should be held at similar standards than other software engineering specialties, with code review, code documentation, and architectural designs.
The article concludes, "Until such practices are established as the norm, I'll remain skeptical of Data Science."