If the proponents of the metaverse have their way we’ll one day be lining up for healthcare or a mortgage in a virtual world run by virtual decision makers. Design of the artificial intelligence systems driving this world, still the task of humans, has real potential for harm.
Besides commercial incentives, implicit biases that exist offline based on ethnicity, gender and age are often reflected in the big data collected from the internet. Machine learning models that are trained using these bias-embedded datasets unsurprisingly adopt these biases. For instance, in 2019, Facebook (now Meta) was sued by the US Department of Housing and Urban Development for “encouraging, enabling, and causing” discrimination on race, gender, and religion through its advertising platform. Facebook later said it would take “meaningful steps” to stop such behaviour, but it continued to deliver the same discriminative ad service to over two billion users on the basis of their demographic information.
Technical flaws during data collection, sampling, and model design can further exacerbate unfairness by introducing outliers, sampling bias, and temporal bias (where a model works well at first, but fails in the future because future changes weren’t considered when building the model).
