Privatization of AI
Researchers in deep learning and those with greater research impact are more likely to migrate to industry, raising concerns about the “privatization of AI knowledge” [278]. Specically, if the most sophisticated AI approaches become proprietary and are used only within private research labs, then it will be impossible for universities to teach them, let alone contribute to leading research.
ENTITY
1 - Human
INTENT
1 - Intentional
TIMING
1 - Pre-deployment
Risk ID
mit886
Domain lineage
6. Socioeconomic and Environmental
6.1 > Power centralization and unfair distribution of benefits
Mitigation strategy
1. Establish publicly funded AI resource bases and cloud infrastructure (aggregating computing power, scientific datasets, pre-trained models, and software tools) to provide equitable access for academic and non-commercial research, thereby reducing the primary incentive for top deep learning researchers to migrate solely to resource-rich private industry labs. 2. Implement mandatory Open Science and Open Access policies for all publicly funded AI research and development to ensure that models, code, and training data are shared transparently, preventing the core knowledge and methodology from becoming proprietary and inaccessible to universities for teaching and further research. 3. Integrate AI governance frameworks and risk management protocols that specifically monitor and apply scrutiny to the trend of knowledge privatization, power centralization, and its downstream effects on domestic inequality and the ability of public institutions to engage in cutting-edge research.