Philippe Dufresne, the Privacy Commissioner of Canada, has found OpenAI was not compliant with Canadian federal and provincial privacy laws in the training of its AI models. Following an investigation, Dufresne and his counterparts in Alberta, Quebec and British Columbia say OpenAI's approach to things like data collection and consent stepped on multiple laws, including Canada's Personal Information Protection and Electronic Documents Act (PIPEDA), which governs how companies collect and use personal information during the normal course of business. The commissioners participating in the investigation identified multiple privacy issues with OpenAI's approach, including that the company gathered vast amounts of personal information without adequate safeguards to prevent use of that information to train its models, and that it failed to acquire consent to collect and use that personal information in the first place. Warnings in ChatGPT note that interactions with the AI could be used in training, but third-party data OpenAI has purchased or scraped also includes personal details people likely aren't even aware of. The fact that ChatGPT users have no way to access, correct or delete that data was another issue that the commissioners identified, according to a summary of the investigation's findings, along with OpenAI's lackluster attempts to acknowledge the inaccuracy of some of ChatGPT's responses.

Insurance apps often collect driving, location and health data in exchange for premium discounts. Adjusting app permissions can help limit what information is shared.

Family tree platforms collect sensitive details that can spread online and be used in identity theft or impersonation scams.

A quick inbox search may uncover unused online accounts still tied to your email and personal data, Kurt "CyberGuy" Knutsson writes for Fox News.

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Cybercriminals buy personal data from brokers to personalize tax scams with fake IRS messages, making fraudulent emails feel legitimate for identity theft.

Ideally, we would like methods that train LMs only once ( i.e., one-shot) with a fixed objective, as in

The ShinyHunters hacking group claims to have accessed Bumble and Match systems, highlighting how phishing attacks can threaten major online platforms.