Regulating ChatGPT and other Large Generative AI Models

“Transparent LGAIMs connected to (good-old) knowledge bases, such as AtMan [43], present a promising way forward in this direction.”

“LGAIMs are usually trained with several billion, if not hundreds of billions, parameters [43, 44], requiring large amounts of training data and computing power [45].”

“OpenAI’s “CLIP” image classifier, for example, was built using a set of 400 million image-text pairs [46].”

“Although the emergence of these models in recent years constitutes a significant technical advance (for foundations, see [39-43]), they harness, to a reasonable extent, existing technologies in a vastly increased scale and scope.”

“2 LGAIMs can often digest human text input [57, 58] and produce an output (text; image; audio; video) based on it.”

“Generally, it applies, in the US as well as the EU, in a technology-neutral way [4, 109-112, 113 ; but LGBTI, para.”

“Third, we highlight key data protection risks under the GDPR, with a particular focus on model inversion [27-29].”

“The AI models covered by this contribution are often referred to as ‘foundation models’ [36], ‘large language models’ (LLMs) [37] or ‘large generative models’ (LGAIMs–the term adopted in this article) [38].”

“By sampling from and mixing them, the model can generate content beyond the training data set–thus something new, as some commentators put it [55, 56].”

“and, in particular, transformers, more efficient [48, 49], the energy required to train models this large has triggered concerns from a climate policy perspective [25, 50-54] (see also Part 6).”

“Third, we highlight key data protection risks under the GDPR, with a particular focus on model inversion [27-29].”

“However, errors will be costly, and risks ranging from discrimination and privacy to disrespectful content need to be adequately addressed [4-6].”

“The AI models covered by this contribution are often referred to as ‘foundation models’ [36], ‘large language models’ (LLMs) [37] or ‘large generative models’ (LGAIMs–the term adopted in this article) [38].”

“Generative models, as virtually any novel technology, may be used for better (think: birthday cards) or worse purposes ( think: shitstorm) [119].”

“28-28b AI Act EP Version2) contain provisions to explicitly regulate LGAIMs, even if their providers are based outside of the EU [14, cf. also 23].”

“This would raise the typical competition law issues of vertical integration [76-78].”

“The content generated by these models can, therefore, be biased, prejudiced or harmful [15, 60].”

“AI Research has made progress in this area recently [120-122].”

“While the carbon footprint of IT and AI is significant and steadily rising [50-54], and training of LGAIMs is particularly resource intensive [136], large models may ultimately create significantly fewer greenhouse gas emissions than their smaller brethren [136, p.”

“To avoid or at least mitigate this issue, model developers need to use proper curating techniques [61, 62].”

“However, errors will be costly, and risks ranging from discrimination and privacy to disrespectful content need to be adequately addressed [4-6].”

“While the carbon footprint of IT and AI is significant and steadily rising [50-54], and training of LGAIMs is particularly resource intensive [136], large models may ultimately create significantly fewer greenhouse gas emissions than their smaller brethren [136, p.”

“The situation is slightly different with non-discrimination law, which generally applies, in the US as well as the EU, in a technology-neutral way [55-60].”

“Typically, LGAIM developers will pre-train a large model, which may then be fine-tuned by deployers, potentially in collaboration with developers [81, 82], while users ultimately make the decision what the AI system is used for specifically (e.”

“According to that rule, every simple image or speech recognition system seems to qualify, irrespective of the breadth of its capabilities; rightly, this only corresponds to a minority position in the technical GPAIS literature [14, 73].”

“AI Research has made progress in this area recently [64-66].”

“Beyond this point, only the users should be the subject of regulation and civil liability (and vice versa, for example in control-via-API cases): incentives for action only make sense where the person incentivized is actually in a position to act [89, 90].”

“To avoid or at least mitigate this issue, model developers need to use proper curating techniques [61, 62].”

“5(1)(a) GDPR, the fairness principle [103].”

“Third, we highlight key data protection risks under the GDPR, with a particular focus on model inversion [27-29].”

“Beyond this point, only the users should be the subject of regulation and civil liability (and vice versa, for example in control-via-API cases): incentives for action only make sense where the person incentivized is actually in a position to act [89, 90].”

“The AI models covered by this article are often referred to as ‘foundation models’ [18], ‘large language models’ (LLMs) [19] or ‘large generative models’ (LGAIMs) [20].”

“Importantly, more interdisciplinary research is necessary to develop markings that are easy to use and recognize, but hard to remove by average users [139].”

“[28, 53] that aim at allowing them “to find patterns and relationships”

“Due to space constraints, we cannot address all social and regulatory concerns regarding LGAIMs and have to bracket, for example, questions of IP law, power dynamics [35, 36], a deeper exploration of the comparative advantages of technology-neutral and technology-specific regulation [37], or the use of LGAIMs in milit…”

“However, we note that recent studies have shown that LGAIMs are vulnerable to inversion attacks, even to a greater extent than previous generative models, such as generative adversarial networks (GANs [116]) [28].”

“Furthermore, the speed and syntactical accuracy of LGAIMs make them the perfect tool for the mass creation of highly polished, seemingly fact-loaded, yet deeply twisted fake news [7, 17].”

“This should be coupled with research on AI-content detection to highlight such output where watermarks fail [121, 140].”

“As consent was typically not obtained [105], developers need to avail themselves of the balancing test and/or the purpose change test (Article 6(1)(f) and (4) GDPR) [106-109], potentially in conjunction with specific research exceptions (Article 89 GDPR) [110-112].”

“The liability directives proposed by the EU Commission, for example, contain elaborate evidence disclosure rules pitting the compensation interests of injured persons against the secrecy interests of AI developers and deployers [24, 77, 78].”

“Already now, LGAIMs’ unbridled capacities may be harnessed to take manipulation, fake news, and harmful speech to an entirely new level [7-11].”

“, MusicLM), while further alternatives are already in the pipeline [1-3].”

“Scholars and regulators have long suggested that technology-neutral laws may be better prepared to tackle emerging risks given the rapid pace of innovation in machine learning [144-146].”

“This is crucial not only for innovation and or consumer welfare [33, 159, 160], but also for environmental sustainability.”

“As consent was typically not obtained [105], developers need to avail themselves of the balancing test and/or the purpose change test (Article 6(1)(f) and (4) GDPR) [106-109], potentially in conjunction with specific research exceptions (Article 89 GDPR) [110-112].”

“As a result, the debate on how (not) to regulate LGAIMs is becoming increasingly intense [12-22].”

“LGAIMs are usually trained with several billion, if not hundreds of billions, parameters [43, 44], requiring large amounts of training data and computing power [45].”

“The Russian attack on Ukraine, the Corona pandemic, climate change, and the political feuds in the US and beyond already fuel hate crimes and fake news [127, 128].”

“Third, and related to the first and second aspect, individual actors in the AI value chain may simply not have the allencompassing knowledge and control that would be required if they were the sole addressees of regulatory duties [93].”

“This would raise the typical competition law issues of vertical integration [76-78].”

“However, non-discrimination provisions may apply more broadly to the pre-trained model itself (more precisely: to its developers) to mitigate bias at its data source [see also 25].”

“Compliance with the GDPR will depend on a range of factors [113-116], such as the intended purposes of the model, the type of personal data used, the likelihood of model inversion, and the probability of re-identification of concrete data subjects.”

“As consent was typically not obtained [105], developers need to avail themselves of the balancing test and/or the purpose change test (Article 6(1)(f) and (4) GDPR) [106-109], potentially in conjunction with specific research exceptions (Article 89 GDPR) [110-112].”

“, MusicLM), while further alternatives are already in the pipeline [1-3].”

“Generally, it applies, in the US as well as the EU, in a technology-neutral way [4, 109-112, 113 ; but LGBTI, para.”

“Additional factors, such as the balance of power among developers, must also be considered [149].”

“While scholars are divided about whether the use of personal data for machine learning purposes should be permitted under the balancing test (permissive view: [26]; more restrictive view: [109, 117]; see also [118]), the threat of model inversion arguably weighs in favor of data subjects.”

“This notice-and-action mechanism, structurally known from copyright law [64-66] and the DSA [32, 34, 67, 68], complements the novel, active production monitoring obligation (Articles 61, 4(2) AI Act), which had so far only been contained in the product liability tort law of some Member States [69] and will be partiall…”

“Due to space constraints, we cannot address all social and regulatory concerns regarding LGAIMs and have to bracket, for example, questions of IP law, power dynamics [35, 36], a deeper exploration of the comparative advantages of technology-neutral and technology-specific regulation [37], or the use of LGAIMs in milit…”

“AI moderator, with controversial help from Kenyan contractors , to detect and block harmful content [12].”

“While the carbon footprint of IT and AI is significant and steadily rising [26-30], and training of LGAIMs is”

“This is less of a concern for copyrighted material found in the training data by way of model inversion as the new text-and-data-mining exceptions (Articles 3 and 4 Copyright in the Digital Single Market Directive5), at least generally and where rightsholders have not opted out, allow the use of publicly accessible co…”

“Article 15(4) GDPR contains a similar provision, which by way of analogy also applies to the access right in Article 15(1) GDPR [99, 100].”

“We show that the EU’s prime instrument to combat harmful speech, the Digital Services Act (DSA) [16, 17], does not apply to LGAIMs, creating a dangerous regulatory loophole.”

“and, in particular, transformers, more efficient [48, 49], the energy required to train models this large has triggered concerns from a climate policy perspective [25, 50-54] (see also Part 6).”

“According to that rule, every simple image or speech recognition system seems to qualify, irrespective of the breadth of its capabilities; rightly, this only corresponds to a minority position in the technical GPAIS literature [14, 66].”

“The situation is slightly different with non-discrimination law, which generally applies, in the US as well as the EU, in a technology-neutral way [55-60].”

“Compliance with the GDPR will depend on a range of factors [127-130], such as the intended purposes of the model, the type of personal data used, the likelihood of model inversion, and the probability of re-identification of concrete data subjects.”

“We show that the EU’s prime instrument to combat harmful speech, the Digital Services Act (DSA) [16, 17], does not apply to LGAIMs, creating a dangerous regulatory loophole.”

“As consent was typically not obtained [105], developers need to avail themselves of the balancing test and/or the purpose change test (Article 6(1)(f) and (4) GDPR) [106-109], potentially in conjunction with specific research exceptions (Article 89 GDPR) [110-112].”

“Prompt engineering is becoming a new art to elicit any content from LGAIMs [123] and fake news is harder to detect than hate speech, even though industry efforts are underway via increased model and source transparency [124].”

“The enforcement of any user-focused transparency rule being arduous, it must be supported by technical measures such as digital rights management and watermarks imprinted by the model [138].”

“Due to space constraints, we cannot address all social and regulatory concerns regarding LGAIMs and have to bracket, for example, questions of IP law, power dynamics [35, 36], a deeper exploration of the comparative advantages of technology-neutral and technology-specific regulation [37], or the use of LGAIMs in milit…”

“Scholars and regulators have long suggested that technology-neutral laws may be better prepared to tackle emerging risks given the rapid pace of innovation in machine learning [144-146].”

“The unprovoked Russian attack on Ukraine, the Corona pandemic, climate change, and the political feuds in the US and beyond already fuel hate crimes and fake news [152, 153].”

“Compliance with the GDPR will depend on a range of factors [113-116], such as the intended purposes of the model, the type of personal data used, the likelihood of model inversion, and the probability of re-identification of concrete data subjects.”

“with developers [73, 74], while users ultimately make the decision what the AI system is used for specifically (e.”

“Extensive literature and practical experience concerning this problem exists in the realm of the US pretrial discovery system [88-92].”

“As consent was typically not obtained [119], developers need to avail themselves of the balancing test and/or the purpose change test (Article 6(1)(f) and (4) GDPR) [120-123], potentially in conjunction with specific research exceptions (Article 89 GDPR) [124-126].”

“This is crucial not only for innovation and or consumer welfare [16, 68, 69], but also for environmental sustainability.”

“Similar effects have already been established concerning the GDPR [47].”

“LGAIM risk regulation, in our view, should generally focus on deployed applications, not the pre-trained model [12, 25].”

“While scholars are divided about whether the use of personal data for machine learning purposes should be permitted under the balancing test (permissive view: [26]; more restrictive view: [109, 117]; see also [118]), the threat of model inversion arguably weighs in favor of data subjects.”

“” While the notice and action mechanism applies to all hosting services, instruments like trusted flaggers, obligatory dispute resolution, and risk management systems are reserved for the narrower group of “online platforms” [131].”

“Already now, LGAIMs’ unbridled capacities may be harnessed to take manipulation, fake news, and harmful speech to an entirely new level [7-11].”

“The liability directives proposed by the EU Commission, for example, contain elaborate evidence disclosure rules pitting the compensation interests of injured persons against the secrecy interests of AI developers and deployers [25, 70, 71].”

“This notice-and-action mechanism, structurally known from copyright law [64-66] and the DSA [32, 34, 67, 68], complements the novel, active production monitoring obligation (Articles 61, 4(2) AI Act), which had so far only been contained in the product liability tort law of some Member States [69] and will be partiall…”

“Extensive literature and practice concerning this problem exists in the realm of the US pretrial discovery system [49-53].”

“unsurmountable trade secrets or IP rights [96-98].”

“The liability directives proposed by the EU Commission, for example, contain elaborate evidence disclosure rules pitting the compensation interests of injured persons against the secrecy interests of AI developers and deployers [25, 70, 71].”

“Hence, we introduce the following distinction within the category of users: o professional user: an entity using AI output for professional purposes, generally as defined in EU consumer law [89, 90].”

“Hence, we introduce the following distinction within the category of users: o professional user: an entity using AI output for professional purposes, generally as defined in EU consumer law [83, 84].”

“Even the most stringent application of DSA enforcement mechanisms, potentially coupled with GDPR provisions on erasure of data (Article 17(2) and 19 GDPR), cannot undo the harm done, and often cannot prevent the forward replication of problematic content [133].”

“, MusicLM), while further alternatives are already in the pipeline [1-3].”

“In details, regulation should focus on concrete high-risk applications, and not the pre-trained model itself, and should include (i) obligations regarding transparency, (ii) risk management, (iii) non-discrimination provisions, and (iv) content moderation rules 15 .”

“Regarding relevant policies and regulations, Hacker et al. [25] discussed the nature and rules of large generative AI models, including ChatGPT, which are rapidly changing the way we communicate, explain, and create.”

“However, they also promote reliability, legal accountability, and ethical standards in AI development, fostering trust among users and stakeholders and encouraging the responsible and wider adoption of AI technologies in healthcare [135].”