I could make a video claiming that I saw this coming…

https://www.npr.org/2023/05/08/1174132413/people-are-trying-to-claim-real-videos-are-deepfakes-the-courts-are-not-amused

People are trying to claim real videos are deepfakes. The courts are not amused

… The liar's dividend is a term coined by law professors Bobby Chesney and Danielle Citron in a 2018 paper laying out the challenges deepfakes present to privacy, democracy, and national security.

The idea is, as people become more aware of how easy it is to fake audio and video, bad actors can weaponize that skepticism.

"Put simply: a skeptical public will be primed to doubt the authenticity of real audio and video evidence," Chesney and Citron wrote.

In Musk's case, the judge did not buy his lawyers' claims.

"What Tesla is contending is deeply troubling to the Court," Judge Evette Pennypacker wrote in a ruling ordering Musk to testify under oath.

"Their position is that because Mr. Musk is famous and might be more of a target for deep fakes, his public statements are immune," she wrote. "In other words, Mr. Musk, and others in his position, can simply say whatever they like in the public domain, then hide behind the potential for their recorded statements being a deep fake to avoid taking ownership of what they did actually say and do. The Court is unwilling to set such a precedent by condoning Tesla's approach here."

New technologies force a rethink (and a restructure) of processes.

https://sloanreview.mit.edu/article/ai-is-helping-companies-redefine-not-just-improve-performance/

AI Is Helping Companies Redefine, Not Just Improve, Performance

… Kaushik’s team used supervised machine learning techniques — classification trees, specifically — to identify connections and correlations they had missed. “Because we didn’t even know what questions to ask, this kind of unsupervised machine learning algorithm was a really good approach,” he says. “We let the algorithm find the patterns.”

What the algorithm found surprised Kaushik and his team: The KPIs they had thought were most essential to optimize actually weren’t. “Which metrics were most influential, the order of their importance, and in which ranges we need to play for individual metrics was a revelation to us,” he says. Among these surprising metrics was the significance of available headroom for the brand metric, which was not on the team’s consideration list of top influencers.1 A second was the strong impact of audible and visible on complete (AVOC), a measure of the percentage of impressions in which a person viewed and heard a full ad. If the AVOC was below a certain percentage, the marketing campaign was doomed to fail. If the percentage was higher, the campaign had a chance for success.

“Six months after we implemented the algorithm’s recommendations, there was a 30-point improvement in performance. That is an insane performance improvement,” Kaushik says. “It’s because instead of the humans figuring out what questions we should ask of the data, we simply said, ‘Hey, why don’t you figure out what the trouble is?’”