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Article summary:
1. Artificial intelligence (AI) and machine learning (ML) have the potential to transform the future of work, but can be biased due to training data and algorithms.
2. A third source of bias may arise from agents strategically altering input to the algorithm in order to benefit from biased predictions.
3. This article examines how human capital can complement ML technology in mitigating bias stemming from input incompleteness, using patent examination as a context.
Article analysis:
The article is generally trustworthy and reliable, providing evidence for its claims through both observational and experimental evidence. The authors provide an extensive literature review on AI/ML, biases in predictions, and strategic behavior of agents that could lead to biased predictions. They also provide two assumptions about their context that are supported by observational evidence. Furthermore, they conduct an experiment to test their hypothesis that human capital can complement ML technology in mitigating bias stemming from input incompleteness.
The only potential issue with the article is that it does not explore any counterarguments or alternative explanations for their findings. It would have been beneficial if the authors had discussed other possible explanations for their results or explored any potential risks associated with their proposed solution of combining human capital with ML technology.