Conclusion

The explosive use of generative AI tools has led to the development of productivity gains from art to medicine. Despite the breakthroughs, generative AI tools have also fomented a steady stream of misinformation, disinformation, and mal-information, causing an uptick of socioeconomic issues within the realms of labor, national security, and data privacy. An explainability tool to combat the lack of transparency and understandability when engaging with a generative AI tool is needed. This research seeks to showcase the Simplified Algorithms for User Learning (SAUL) label, an explainability tool developed using universal design features. SAUL displays three sections of information including: tool functionality, potential harms of use, and data protection policies. In addition to the creation of the SAUL label, policy recommendations are included to aid in the adoption of the SAUL label.

Implementing SAUL at scale would not only democratize access to information but could also build a foundation for a more consumer-led tech landscape where consumers have a voice. Although there will never be a unified set of principles that all consumers agree on, the research conducted in this report sheds light on the fact that consumers want accessible information on the data policies of emerging tech tools such as generative AI tools. Often, consumer technology is built without the consent of consumers, with tech companies believing they know what is best for users. Utilizing the SAUL label or a similar label could help rebalance the share of power between tech companies and consumers.