Fairness & Privacy in an Age of Generative AI - Xiang, Alice (Science and Technology Law Review, 2024)
Generative AI technologies have made tremendous strides recently and have captured the public’s imagination with their ability to mimic what was previously thought to be a fundamentally human capability: creativity. While such technologies hold great promise to augment human creativity and automate tedious processes, they also carry risks that stem from their development process. In particular, the reliance of foundation models on vast amounts of typically uncurated, often web-scraped training data has led to concerns around fairness and privacy. Algorithmic fairness in this context encompasses concerns around potential biases that can be learned by models due to skews in their training data and then reflected in their generated outputs. For example, without intervention, image generation models are more likely to generate images of lighter skin tone male individuals for professional occupations and images of darker skin tone female individuals for working class occupations. This further raises questions around whether there should be legal protections from such pernicious stereotypical representations. Privacy is also a concern as generative AI models can ingest large amounts of personal and biometric information in the training process, including face and body biometrics for image generation and voice biometrics for speech generation. This Essay will discuss the types of fairness and privacy concerns that generative AI raises and the existing landscape of legal protections under anti-discrimination law and privacy law to address these concerns. This Essay argues that the proliferation of generative AI raises challenging and novel questions around (i) what protections should be offered around the training data used to develop such systems and (ii) whether representational harms should be protected against in an age of AI-generated content.