People and machines can only correct each other in a representation both can read. Malone prefers Bayesian networks to neural nets for cyber-human sensing because both people and machines can understand and adjust the same probabilities, while a net can explain itself only as millions of weights.

Evidence 2 passages

• groundsSuperminds (audiobook machine transcript), Thomas W. Malone

  One of the advantages of using a technology like Bayesian networks is that both humans and computers can understand and estimate the same probabilities.

• backsSuperminds (audiobook machine transcript), Thomas W. Malone

  Another limitation of the neural net approach is that even if machines could learn some of these patterns, people wouldn't usually be able to understand how the machines reached their conclusions.

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