I don’t think we’d suffer nearly as much as a machine learning system. When we classify things we draw in a lot of extraneous information. Remember these aren’t photos of objects sitting on white backgrounds, but photos of homes. So the dish soap will be next to the sink, the tooth paste will be near the toothbrushes.
But the way we make machine learning systems now they don’t classify toothpaste by thinking about what room it is in. That kind of thinking reduces accuracy when looking at familiar objects (by giving a chance that some extraneous piece of information will corrupt an otherwise easy guess), and only helps when dealing with unfamiliar situations, which machine learning just doesn’t learn to do. Until we have a significant paradigm shift in how we do machine learning, it’s always going to have things that look like weird blind spots to us.
I think you’ve got this backwards. India is definitely objectively poorer than the United States, but it is also substantially more equal. This wikipedia article has a chart with UN, World Bank and CIA calculations of ratio of incomes and Gini coefficients and the US has higher inequality on all measures.
I think there are two problems, one is just a matter of computational power and getting more data, the other is systematic and can’t be solved within our current approach. If we pictures of every household thing as a dataset then this AI wouldn’t have a problem (and if it did then the answer would just be more computational power).
But when someone came invented a great new way to package toothpaste (it doesn’t even matter if you squeeze from the middle anymore!) there is a chance that the AI would just utterly fail to recognize it, having been trained to look for things that are totally unrelated to anything we would normally thing of. Data can only predict the future if the future happens to be like the past in a relevant way, and it isn’t always.
