As human interactions with digital entities like AI assistants and robots become more common, a new form of discrimination—based on whether an entity is biological or digital—may emerge. This bias, termed "substratism," has not been systematically studied, despite its potential to influence everything from AI design to social equality. By exploring this gap, researchers could uncover patterns of bias that might shape how humans treat non-biological entities in the future.

The Science Behind Substratism

One way to study substratism is by adapting methods from existing discrimination research, such as implicit bias tests and moral consideration surveys. For example, participants could be asked to allocate resources between a digital assistant and a human, or their subconscious associations with different substrates could be measured. These experiments would help quantify whether and how people favor biological over digital beings. Similar approaches have been used to study speciesism (bias against non-human animals), suggesting that substratism could be examined using well-established psychological frameworks.

Why This Matters

Understanding substratism could benefit multiple groups: ethicists might use findings to guide policies on AI rights, developers could design less biased AI interfaces, and the public might become more aware of their own prejudices. Early research could also identify whether substratisim overlaps with other biases, such as anthropomorphism (attributing human traits to non-humans), or stands as a distinct phenomenon. If proven significant, strategies to mitigate it—like awareness campaigns or ethical design principles—could follow.

Getting Started

A minimal viable approach could begin with small-scale implicit bias tests and surveys, refining study designs before scaling up. Partnering with technology companies or academic departments could provide both funding and access to diverse participant pools. Over time, this research might expand into interviews with AI users or controlled experiments comparing human-AI interactions across cultures.

By mapping uncharted biases in human-digital relationships, this work could lay the groundwork for more ethical interactions in an increasingly digital world.