Foundation Models Report (2021) › Risks and Challenges

The report provided equally thorough analysis of risks and challenges. Technical risks included bias and failures under adversarial examples or distribution shift. Foundation models trained on internet-scale data captured biases present in that data, potentially perpetuating and amplifying harmful stereotypes. They could be brittle, failing in unpredictable ways when faced with adversarial examples or distribution shift. Their decision-making processes were often opaque, making it difficult to understand why they produced particular outputs or to ensure they behaved correctly.

Social risks included concentration of power and job displacement. Training foundation models required massive computational resources, concentrating development in the hands of well-funded organizations. This centralization could exclude smaller organizations and academic researchers, limiting diversity in AI development. Foundation models' capabilities also raised questions about job displacement: if these systems could perform many tasks previously requiring human expertise, what would this mean for employment and economic opportunity?

Ethical risks included misuse and environmental impact. These powerful systems could be used for harmful purposes: generating disinformation, creating deepfakes, automating harassment, or producing sophisticated phishing attacks. The report highlighted how foundation models' capabilities amplified risks because their outputs could be more convincing and harder to detect than those from previous systems. Environmental risks stemmed from the massive energy consumption required for training and inference. The carbon footprint of training large foundation models was substantial, raising questions about sustainability.

The report's analysis of these risks was particularly important for informing policy discussions and research directions. It provided policymakers with a framework for understanding potential negative consequences, helping them make more informed decisions about regulation and governance. It helped researchers prioritize which risks to address first and which mitigation strategies might be most effective. The analysis balanced acknowledging real risks with avoiding excessive fear that might stifle beneficial innovation.