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Article summary:

1. Building useful AI systems requires understanding how they work to achieve appropriate trust and reliance.

2. Explainable AI (XAI) research assumes a limited model of automatically generated explanations leading to better performance.

3. XAI systems should be informed by models of cognition and pedagogy, with Peirce's notion of abduction as an exploratory activity being a best model for XAI.

Article analysis:

The article "Psychology and AI at a Crossroads: How Might Complex Systems Explain Themselves?" discusses the challenge of building artificial intelligence (AI) systems that people can trust and rely on. The authors argue that the design of explainable AI (XAI) systems must be informed by models of cognition and pedagogy, based on empirical evidence of how people explain complex systems to others and reason out how they work.

The article's main argument is that C.S. Peirce's notion of abduction is the best model for XAI because it aligns with models of expert reasoning developed by modern applied cognitive psychologists. However, the article does not provide sufficient evidence to support this claim. While the authors briefly mention psychological research on explanatory reasoning, they do not provide any specific examples or studies to back up their argument.

Furthermore, the article seems to have a bias towards Peirce's notion of abduction as the best model for XAI without considering other potential models or counterarguments. The authors do not explore alternative approaches or discuss potential limitations or drawbacks of using Peirce's model.

Additionally, the article lacks practical examples or case studies demonstrating how XAI systems could benefit from using Peirce's model. It also does not address potential risks or ethical considerations associated with XAI systems, such as biases in data sets or unintended consequences.

Overall, while the article raises important questions about designing trustworthy AI systems, it falls short in providing sufficient evidence and exploring alternative perspectives. It would benefit from more concrete examples and a more balanced discussion of different models and potential risks associated with XAI systems.