1. Licensing does not align AI. It creates less ethical AI.
Micaela Mantegna 24. Affiliate at the Berkman Klein Center at Harvard University (United States) and the Center for Technology and Society at San Andres University (Argentina). TED Fellow, member of the Chatham House’s Responsible AI Taskforce and the World Economic Forum’s Metaverse Council."ARTificial: Why Copyright Is Not the Right Policy Tool to Deal with Generative AI". Yale Law Journal. 4-1-2024. https://www.yalelawjournal.org/forum/artificial-why-copyright-is-not-the-right-policy-tool-to-deal-with-generative-ai#_ftnref164
6. Licenses Incentivize Data Substitution, Impacting Core AI Ethics Principles
Another consideration is that a decision made in a GAI case will have an impact well beyond it, affecting the whole AI-development ecosystem, as it might create incentives for data substitution. AI models based on ML learn from the training data. If the most adequate data to train a model requires paying a licensing fee, it would incentivize developers to cut costs and substitute that quality data for other sources that might not be as appropriate, but less costly. Doing so would be akin to changing the ingredients of a recipe with inferior substitutes. Inferior data could result in a concept commonly referred to as “garbage in, garbage out,”173 where the quality of an AI model’s output is directly affected by the quality of the input data.
Web-scraping, data collection, and databases are a vital component of the AI ecosystem. Legally, there are different approaches to regulating databases. In the EU there is a “sui generis” right to protect the content of the database, and the structure under copyright, if certain conditions are met.174 In the United States, as explained above, the content of the database will only be protectable if there is enough originality in the criteria, selection, arrangement, or composition of the data.
This difference in regulatory approaches has an enormous geopolitical impact. It influences where AI companies will locate themselves, and which databases they will use to train the systems, as companies will try to reduce the cost of having to license the data.
Strong copyright provisions and a lack of carve-outs for data mining for training purposes could potentially incentivize the use of data substitutions as a way to avoid paying for licensed datasets. This would translate directly to subpar performance from AI models for the reasons explained above, which would likely be more biased and less fair—and consequently, human beings and society will be harmed in the process.