Generative AI & Competition Law Concerns: The New Kid on the Block!
Bibliographic record
Abstract
Artificial intelligence (AI) is not a new concept. It dates back to 1956 to the Dartmouth Summer Conference on AI. John McCarthy and his colleagues defined AI as the ‘science and engineering of making intelligent machines’. Russel and Norvig define AI as computers and machines that seek to act rationally, think rationally, act and think like a human. Generative AI is a sub-category of AI that allows machines to generate new content rather than simply analyse data. By using models trained on a vast amount of data, generative AI can create content such as text, photos, audio, or video, which may be akin to the content created by humans. Foundation models are a form of generative AI, which generate output from one or more inputs (prompts) in the form of human language instructions. Large language models (LLMs), including chatbots and other text-based AI tools, are good examples of foundation models. Large language models are no longer limited to simple tasks or scripted responses. They possess the ability to understand context, learn from interactions, and adapt their responses. Recent developments in AI and its potential legal implications have been on the radar of regulators in the EU, in the UK, the USA and around the world. On March 13, 2024, the European Parliament adopted the Artificial Intelligence Act (AI Act).On March 13, 2024, the European Parliament adopted the Artificial Intelligence Act (AI Act). This marks a regulatory milestone, aiming to set EU-wide standards on data quality, transparency, human oversight, and accountability. However, this legislation, along with other international efforts like the USA's Algorithmic Accountability Act of 2023 and Canada's Directive on Automated Decision-Making, still leaves gaps as they primarily focus on the safety of AI systems and do not address competition concerns. On 23 July 2024, 4 key agencies namely the European Commission, the UK Competition and the Markets Authority (CMA) the US Department of Justice (DOJ) and the US Federal Trade Commission(FTC) released a joint statement on competition in generative AI foundation models and AI products and acknowledged that AI could benefit citizens, boost innovation and drive economic growth. However,, to reap these benefits competition authorities must remain vigilant and provide safeguards against tactics that could undermine competition. Against this dynamic backdrop, this work assesses the regulatory concerns in generative AI, and whether EU competition law may help address the competition concerns therein.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.024 |
| Scholarly communication | 0.014 | 0.019 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.015 | 0.017 |
| Insufficient payload (model declined to judge) | 0.030 | 0.005 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".