An illustration of the complex system of AI-infused social networks, and a model of amplification of biases in networks (IMAGE)
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A: an illustration of the complex system of AI-infused social networks. B: a model of amplification of biases in networks due to feedback between algorithm and human decision over time. B (i), Simulations are performed on a synthetic network of size 2000 with 30% nodes being minorities and minority homophily and majority homophily are set to 0.7 each. In homophilic networks minorities are less presented in the top ranks compared to their size, 30%. In each time step, 5 random links are removed from the network. They are then rewired, with higher-ranked nodes having a greater probability of being chosen as targets for the new connection. The ranking is recalculated and this process of link rewiring is repeated over many feedback loops. The fraction of minorities in the upper ranks (eg: the top 10%) reduces over time. The results are averaged over 10 independent experiments. In part (ii), we can observe that the fraction of minorities in the top 10% goes from 23.4% down to 21.8% in 60 iterations. Part (iii) measures demographic parity where demonstrates how far in the rank 30% of minorities are present as we expect, all else equal. At the beginning of the process, we achieve 30% representation of the minorities when we arrive at the top 56% of the nodes. At the end of the process, we need to include the top 71% to get this fair representation.
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