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It's in settings->profile (your email address)->smart features.
Be smart.
They follow the approach of turning things on as a default. No opt in.
So has Qantas had another data breach recently, or was that one late last year worse than they thought?
I never got an email to say I was impacted with the breach last year, so I thought I was in the clear, but I just got a phishing email on the address I set up solely for my Qantas frequent flyer account.
I’ve not used that anywhere else, so they could have only got that from Qantas somehow.
I don't have a car, but what the holy hell is happening with on-board car computers. They're proprietary and locked down, they always know where you're going and they have a microphone that you have no way of knowing if it's on.
Privacy nightmare. Not to mention every brand ships a different shitty UI.
Are there any efforts for open standards? Are there any cars that allow you to choose your own OS?
The specific example I used to give them was the potential for a place like Amazon to leverage data from friends and family members.
For example, if something like a birthday or Christmas was coming up, a company like Amazon could see what products Person A had looked at, and adjust prices on those products for people in the same household, or who had viewed Person A’s Wishlist, or who they knew were connected in some way from other data they had.
I remember warning my marketing students a decade ago about the potential for and dangers of algorithmic personalised pricing.
In retrospect, I just hope that wasn’t me planting the seed in their heads. I always tried to give systems and stakeholder perspectives on marketing ethics, but far too many were laser focused on profits and shareholder primacy.
(Honestly though, I got the impression at the time that most took it as just the privacy guy being paranoid again.)
Utah’s SB73 holds websites liable if users bypass age verification via VPNs, effective May 6, 2026. Law assumes sites can detect and locate VPN users—technically dubious. Critics say it undermines privacy and creates impossible compliance burdens. #privacy #ageverification
We find that LLMs consistently prefer resumes generated by themselves over those written by humans or produced by alternative models, even when content quality is controlled.
Horrifying.
As artificial intelligence (AI) tools become widely adopted, large language models (LLMs) are increasingly involved on both sides of decision-making processes, ranging from hiring to content moderation. This dual adoption raises a critical question: do LLMs systematically favor content that resembles their own outputs? Prior research in computer science has identified self-preference bias -- the tendency of LLMs to favor their own generated content -- but its real-world implications have not been empirically evaluated. We focus on the hiring context, where job applicants often rely on LLMs to refine resumes, while employers deploy them to screen those same resumes. Using a large-scale controlled resume correspondence experiment, we find that LLMs consistently prefer resumes generated by themselves over those written by humans or produced by alternative models, even when content quality is controlled. The bias against human-written resumes is particularly substantial, with self-preference bias ranging from 67% to 82% across major commercial and open-source models. To assess labor market impact, we simulate realistic hiring pipelines across 24 occupations. These simulations show that candidates using the same LLM as the evaluator are 23% to 60% more likely to be shortlisted than equally qualified applicants submitting human-written resumes, with the largest disadvantages observed in business-related fields such as sales and accounting. We further demonstrate that this bias can be reduced by more than 50% through simple interventions targeting LLMs' self-recognition capabilities. These findings highlight an emerging but previously overlooked risk in AI-assisted decision making and call for expanded frameworks of AI fairness that address not only demographic-based disparities, but also biases in AI-AI interactions.
Wladimir Palant
Taran Rampersad
Kackfresh
Wesearch.press
Taggart 