4 ms·

> Unacceptable risk AI systems are systems considered a threat to people and will be banned. They include: * Cognitive behavioural manipulation of people or sp

> Unacceptable risk AI systems are systems considered a threat to people and will be banned. They include: * Cognitive behavioural manipulation of people or specific vulnerable groups: for example voice-activated toys that encourage dangerous behaviour in children * Social scoring: classifying people based on behaviour, socio-economic status or personal characteristics * Biometric identification and categorisation of people * Real-time and remote biometric identification systems, such as facial recognition That seems... reasonable? I know regulation is a trigger word for some folks around here, but I think preventing a Chinese-style social credit system seems like a positive thing?

_3u10 3y ago
Yeah it will be an EU style credit score.
tensor 3y ago
Social scoring is an interesting topic. E.g. why is it not banned if you don't use AI? I'd guess that most social scoring systems today are not actually AI based. Perhaps they have some components that come from AI, but it's easy enough to make one with simpler mechanisms. Also, I assume insurance risk scoring is not banned, even though that is already almost a social scoring system. E.g. you live in a dangerous poor neighbourhood, higher insurance! You are a given age, higher insurance!
- xpe 3y ago
  How do you find a meaningful definition of “simpler” mechanisms? / How do you differentiate ML from AI? (Many view ML as a subtype of AI.)
  - tensor 3y ago
    These are excellent questions. I was using AI in the seemingly typical way people use to today, to refer to large neural network systems. But yes, part of the issue around these laws and indeed the whole discussion is a lack of nuance about what exactly people even mean when they say AI. If I were to switch to my computer science context, then yes, I view ML as a subset of AI, an even view logic systems and rules systems as a subset of AI too. I doubt that the average person is thinking of PCA or even word embeddings when they say "AI" today, and that's a huge problem. Many of the risks these AI regulation laws are worried about apply equally well to both simpler than LLM systems, and even apply to basic statistics and data aggregating. In my opinion risk mitigation laws should not be specific to AI, but be problem focused instead. Similarly, "AI training bans" for say web content would also be far improved with more nuance. Many laypeople argue that banning AI training doesn't ban web indexing. But based on my professional training I would not personally implement even a basic tf-idf system on any data that has "AI training banned" as tf-idf is a basic AI system according to the strict definition. We'd do far better being more explicit, and banning perhaps "generative AI systems" or "AI systems that are capable of generating substantial subsets of the content." It wouldn't be perfect but it would be a hell of a lot better than the trajectory we're on today.
    - xpe 3y ago
      I appreciate your comments. / I agree that the mainstream understanding is, as would be expected, too often an ignorant jumbled mess. But this is to be expected. I'm reading and ingesting as much as I can on AI safety from many angles. At this point, I don't have a "take" on the status of the legal clarity here. / Happy to continue the conversation as well.
ensignavenger 3y ago
It all comes down to the details. A meme sharing site that uses a computerized recommendation engine to recomend memes to users might be considered "cognitive behavioral manipulation".
xoa 3y ago
From an admittedly brief look (while interesting it has no direct immediate relevance to me), I can't tell if this applies universally regardless of scale, or if there is any sort of gradient. Does anyone know? Some of it seems reasonable period at any scale, but some of it would be worrisome or strike me as unreasonable/negative if it applied to individuals as opposed to larger network where emergent effects happen. To take a personal example, I selfhost Blue Iris and am using some AI recognition in my home security system, which I also use for wildlife monitoring. It's really awesome vs the old plain motion or even motion zones, and as there is zero exposure to the internet, nor even any view of public property at all (end of 1/3 mile driveway with woods). It is useful to be able to classify known (me, family, regular neighbors) vs unknown people as well. I think the societal harm potential of an isolated system like that vs the individual benefit is low, and thus I'd lean towards skepticism if that too fell under their banner of "Biometric identification and categorisation of people" or "Real-time and remote biometric identification systems, such as facial recognition". But of course in a public setting, and/or networked with lots of others with enough scale to start effectively following people through space and time not just capturing one private slice, the metrics all change. So as I watch various AI efforts I'm always thinking about how they address that (or not). I don't think "profit" is even the deciding thing here, something can not be commercial but still potentially a problem at enough scale.

_3u10 3y ago

Yeah it will be an EU style credit score.

tensor 3y ago

Social scoring is an interesting topic. E.g. why is it not banned if you don't use AI? I'd guess that most social scoring systems today are not actually AI based. Perhaps they have some components that come from AI, but it's easy enough to make one with simpler mechanisms. Also, I assume insurance risk scoring is not banned, even though that is already almost a social scoring system. E.g. you live in a dangerous poor neighbourhood, higher insurance! You are a given age, higher insurance!

xpe 3y ago
How do you find a meaningful definition of “simpler” mechanisms? / How do you differentiate ML from AI? (Many view ML as a subtype of AI.)
- tensor 3y ago
  These are excellent questions. I was using AI in the seemingly typical way people use to today, to refer to large neural network systems. But yes, part of the issue around these laws and indeed the whole discussion is a lack of nuance about what exactly people even mean when they say AI. If I were to switch to my computer science context, then yes, I view ML as a subset of AI, an even view logic systems and rules systems as a subset of AI too. I doubt that the average person is thinking of PCA or even word embeddings when they say "AI" today, and that's a huge problem. Many of the risks these AI regulation laws are worried about apply equally well to both simpler than LLM systems, and even apply to basic statistics and data aggregating. In my opinion risk mitigation laws should not be specific to AI, but be problem focused instead. Similarly, "AI training bans" for say web content would also be far improved with more nuance. Many laypeople argue that banning AI training doesn't ban web indexing. But based on my professional training I would not personally implement even a basic tf-idf system on any data that has "AI training banned" as tf-idf is a basic AI system according to the strict definition. We'd do far better being more explicit, and banning perhaps "generative AI systems" or "AI systems that are capable of generating substantial subsets of the content." It wouldn't be perfect but it would be a hell of a lot better than the trajectory we're on today.
  - xpe 3y ago
    I appreciate your comments. / I agree that the mainstream understanding is, as would be expected, too often an ignorant jumbled mess. But this is to be expected. I'm reading and ingesting as much as I can on AI safety from many angles. At this point, I don't have a "take" on the status of the legal clarity here. / Happy to continue the conversation as well.

ensignavenger 3y ago

It all comes down to the details. A meme sharing site that uses a computerized recommendation engine to recomend memes to users might be considered "cognitive behavioral manipulation".

xoa 3y ago

From an admittedly brief look (while interesting it has no direct immediate relevance to me), I can't tell if this applies universally regardless of scale, or if there is any sort of gradient. Does anyone know? Some of it seems reasonable period at any scale, but some of it would be worrisome or strike me as unreasonable/negative if it applied to individuals as opposed to larger network where emergent effects happen. To take a personal example, I selfhost Blue Iris and am using some AI recognition in my home security system, which I also use for wildlife monitoring. It's really awesome vs the old plain motion or even motion zones, and as there is zero exposure to the internet, nor even any view of public property at all (end of 1/3 mile driveway with woods). It is useful to be able to classify known (me, family, regular neighbors) vs unknown people as well. I think the societal harm potential of an isolated system like that vs the individual benefit is low, and thus I'd lean towards skepticism if that too fell under their banner of "Biometric identification and categorisation of people" or "Real-time and remote biometric identification systems, such as facial recognition". But of course in a public setting, and/or networked with lots of others with enough scale to start effectively following people through space and time not just capturing one private slice, the metrics all change. So as I watch various AI efforts I'm always thinking about how they address that (or not). I don't think "profit" is even the deciding thing here, something can not be commercial but still potentially a problem at enough scale.