Daniel Solove’s Post

“Scraping is generally anathema to the core principles of privacy that form the backbone of most privacy laws, frameworks, and codes. With the zealous pursuit and astronomical growth of AI, we are in the midst of what we call the ‘great scrape.’ There must now be a great reconciliation.” - From my article, “The Great Scrape" with Woody Hartzog, https://lnkd.in/eN9xKKke

For effective navigation of the privacy paradox presented by AI, a sophisticated, multifaceted approach is necessary. The role of lawmakers and policymakers in this context cannot be overstated. We are tasked with the onerous duty of revisiting existing laws, with an eye toward evolving them to accommodate the unique challenges presented by AI. This includes establishing strict regulations on AI-driven data-processing technologies and demanding greater transparency from developers about their algorithms and data sources. https://lnkd.in/d7ih7mY9

Preliminary enforcement actions around artificial intelligence companies sent waves around the nascent industry, but stakeholders are just beginning to grapple with what regulation will look like in the future. That was the theme of two panels focused on regulators’ views on AI at the IAPP Global Privacy Summit 2024, held in Washington, D.C. While speakers said there are already clear areas where AI is regulated, how to address those overlapping areas, find global agreement and gather the resources to enforce policies poses a challenge to stakeholders both in the private and public spheres. https://lnkd.in/eQthfsTJ

3 things privacy professionals should consider at the intersection of AI and data privacy The Checks team shares three things that privacy professionals should consider when thinking about AI and privacy in 2024.

3 things privacy professionals should consider at the intersection of AI and data privacy The Checks team shares three things that privacy professionals should consider when thinking about AI and privacy in 2024.

The privacy paradox with AI

A common topic of discussion among privacy lawyers is whether current privacy laws are enough to deal with the risks posed to privacy by artificial intelligence. The article below by Professor Solove, from late last month, makes a great case for why we might need a new way to approach privacy regulation in the age of AI. For instance, the article says that the way we currently ask for someone's consent to use their data is not the best when it comes to practices like data scraping. This is, literally, scraping data off of websites to train AI systems. When it comes to generative AI, that's a lot of data, belonging to a lot of people, and it is very difficult to get everyone's explicit consent or to notify them. Different jurisdictions approach this issue differently, so we might have to wait a bit for a one-size-fits-all solution (if there is one at all).

"By advocating for collaboration, transparency, and accountability, we can harness the potential benefits of AI technology responsibly, while preserving an unwavering commitment to safeguarding the fundamental rights of individuals. The AI privacy paradox represents one of the most significant challenges of our time. As we move forward, we must ensure that our pursuit of technological advancement does not come at the cost of our privacy rights" #SafetyTech #AI https://lnkd.in/gzwU2pNr

After criticism, Liberals now say they will provide text of amendments to privacy, AI bill | SaltWire: The Liberal government of Canada has stated that it will provide the text of amendments to its proposed privacy and AI bill. Initially, they had planned to wait until after MPs heard from experts, but opposition MPs objected to this. The government now plans to publish the text of the amendments as soon as possible. - Artificial Intelligence topics! #ai #artificialintelligence #intelligenzaartificiale

The Office of the Privacy Commissioner just dropped a "principles" document that makes very direct points about the interface between Generative AI and current Canadian privacy law. It includes some important details about personal information in training data, including that used to train down foundational models. There's no wiggle room for "it was from the open Internet" or "the vendor didn't/won't disclose" arguments. Both industry and government implementers are eyed by this piece. EDIT: the document isn't just from the federal OPC, the provincial partners are also authors. #genai #privacy #governmenttechnology