Modern artificial intelligence (AI) systems rely on input from people. Human feedback helps train models to perform useful tasks, guides them toward safe and responsible behavior, and is used to assess their performance. While hailing the recent AI advancements, we should also ask: which humans are we actually talking about? For AI to be most beneficial, it should reflect and respect the diverse tapestry of values, beliefs, and perspectives present in the pluralistic world in which we live, not just a single “average” or majority viewpoint. Diversity in perspectives is especially relevant when AI systems perform subjective tasks, such as deciding whether a response will be perceived as helpful, offensive, or unsafe. For instance, what one value system deems as offensive may be perfectly acceptable within another set of values.
Since divergence in perspectives often aligns with socio-cultural and demographic lines, preferentially capturing certain groups’ perspectives over others in data may result in disparities in how well AI systems serve different social groups. For instance, we previously demonstrated that simply taking a majority vote from human annotations may obfuscate valid divergence in perspectives across social groups, inadvertently marginalizing minority perspectives, and consequently performing less reliably for groups marginalized in the data. How AI systems should deal with such diversity in perspectives depends on the context in which they are used. However, current models lack a systematic way to recognize and handle such contexts.
With this in mind, here we describe our ongoing efforts in pursuit of capturing diverse perspectives and building AI for the pluralistic society in which we live. We start with understanding the varying perspectives in the world and, ultimately, we develop effective ways to integrate these differences into the modeling pipeline. Each stage of the AI development pipeline — from conceptualization and data collection to training, evaluation, and deployment — offers unique opportunities to embed diverse perspectives, but also presents distinct challenges. A truly pluralistic AI cannot rely on isolated fixes or adjustments; it requires a holistic, layered approach that acknowledges and integrates complexity at every step. Having scalability in mind, we set out to (1) disentangle systematic differences in perspectives across social groups, (2) develop an in-depth understanding of the underlying causes for these differences, and (3) build effective ways to integrate meaningful differences into the machine learning (ML) modeling pipeline.
