プライバシーの乱獲

もともとのプライバシーは空間概念と一体になっている。 つまり私的領域と公的領域を区別することである。 しかしインターネット（特に Web 2.0 以降）は情報は空間概念から切り離される。 場所が不定になるのだ。 だから，それをコントロールしたければ情報へのアクセス性を対象にせざるを得なくなる。

Microsoft (MSFT.O), opens new tab said in its report it had tracked hacking groups affiliated with Russian military intelligence, Iran’s Revolutionary Guard, and the Chinese and North Korean governments as they tried to perfect their hacking campaigns using large language models. Those computer programs, often called artificial intelligence, draw on massive amounts of text to generate human-sounding responses.

The company announced the find as it rolled out a blanket ban on state-backed hacking groups using its AI products.

In the security community, the immediate questions weren’t about how hackers were using the tools (that was utterly predictable), but about how Microsoft figured it out. The natural conclusion was that Microsoft was spying on its AI users, looking for harmful hackers at work.

If you call this “spying”, you should read the TOS of the OpenAI API again. If you, as a “hacker”, still use their API

One scientist, Daniel Pauly, realized that researchers studying fish populations were making a major error when trying to determine acceptable catch size. It wasn’t that scientists didn’t recognize the declining fish populations. It was just that they didn’t realize how significant the decline was. Pauly noted that each generation of scientists had a different baseline to which they compared the current statistics, and that each generation’s baseline was lower than that of the previous one.

[…]

Pauly called this “shifting baseline syndrome” in a 1995 paper. The baseline most scientists used was the one that was normal when they began their research careers. By that measure, each subsequent decline wasn’t significant, but the cumulative decline was devastating. Each generation of researchers came of age in a new ecological and technological environment, inadvertently masking an exponential decline.

Historically, people controlled their computers, and software was standalone. The always-connected cloud-deployment model of software and services flipped the script. Most apps and services are designed to be always-online, feeding usage information back to the company. A consequence of this modern deployment model is that everyone—cynical tech folks and even ordinary users—expects that what you do with modern tech isn’t private. But that’s because the baseline has shifted.

[…]

Shifting baselines are at the heart of our collective loss of privacy. The U.S. Supreme Court has long held that our right to privacy depends on whether we have a reasonable expectation of privacy. But expectation is a slippery thing: It’s subject to shifting baselines.

AI chatbots are the latest incarnation of this phenomenon: They produce output in response to your input, but behind the scenes there’s a complex cloud-based system keeping track of that input—both to improve the service and to sell you ads.

Fisheries scientists, armed with knowledge of shifting-baseline syndrome, now look at the big picture. They no longer consider relative measures, such as comparing this decade with the last decade. Instead, they take a holistic, ecosystem-wide perspective to see what a healthy marine ecosystem and thus sustainable catch should look like. They then turn these scientifically derived sustainable-catch figures into limits to be codified by regulators.

Instead of comparing to a shifting baseline, we need to step back and look at what a healthy technological ecosystem would look like: one that respects people’s privacy rights while also allowing companies to recoup costs for services they provide. Ultimately, as with fisheries, we need to take a big-picture perspective and be aware of shifting baselines.

A scientifically informed and democratic regulatory process is required to preserve a heritage—whether it be the ocean or the Internet—for the next generation.