Max Planck Institute for Human Development, Berlin, Germany.
Perspect Psychol Sci. 2024 Sep;19(5):839-848. doi: 10.1177/17456916231180597. Epub 2023 Jul 31.
Psychological artificial intelligence (AI) applies insights from psychology to design computer algorithms. Its core domain is decision-making under uncertainty, that is, ill-defined situations that can change in unexpected ways rather than well-defined, stable problems, such as chess and Go. Psychological theories about heuristic processes under uncertainty can provide possible insights. I provide two illustrations. The first shows how recency-the human tendency to rely on the most recent information and ignore base rates-can be built into a simple algorithm that predicts the flu substantially better than did Google Flu Trends's big-data algorithms. The second uses a result from memory research-the paradoxical effect that making numbers less precise increases recall-in the design of algorithms that predict recidivism. These case studies provide an existence proof that psychological AI can help design efficient and transparent algorithms.
心理人工智能（AI）将心理学的见解应用于设计计算机算法。其核心领域是不确定性下的决策，即定义不明确的情况，这些情况可能以意想不到的方式变化，而不是定义明确、稳定的问题，如国际象棋和围棋。关于不确定性下启发式过程的心理学理论可以提供可能的见解。我提供两个例子。第一个例子展示了如何将近期性（人类倾向于依赖最新信息而忽略基本比率）构建到一个简单的算法中，该算法可以大大提高对流感的预测准确性，而不是谷歌流感趋势的大数据算法。第二个例子利用记忆研究中的一个结果——使数字变得不那么精确会增加回忆——在设计预测累犯的算法中。这些案例研究提供了一个存在的证明，即心理人工智能可以帮助设计高效和透明的算法。
