Background Artificial Intelligence (AI) is transforming the process of scientific research. AI, coupled with availability of large datasets and increasing computational power, is accelerating progress in areas such as genetics, climate change and astronomy [NeurIPS 2019 Workshop Tackling Climate Change with Machine Learning, Vancouver, Canada; Hausen R, Robertson BE. Morpheus: A deep learning framework for the pixel-level analysis of astronomical image data. Astrophys J Suppl Ser. 2020;248:20; Dias R, Torkamani A. AI in clinical and genomic diagnostics. Genome Med. 2019;11:70.]. The application of AI in behavioral science is still in its infancy and realizing the promise of AI requires adapting current practices. Purposes By using AI to synthesize and interpret behavior change intervention evaluation report findings at a scale beyond human capability, the HBCP seeks to improve the efficiency and effectiveness of research activities. We explore challenges facing AI adoption in behavioral science through the lens of lessons learned during the Human Behaviour-Change Project (HBCP). Methods The project used an iterative cycle of development and testing of AI algorithms. Using a corpus of published research reports of randomized controlled trials of behavioral interventions, behavioral science experts annotated occurrences of interventions and outcomes. AI algorithms were trained to recognize natural language patterns associated with interventions and outcomes from the expert human annotations. Once trained, the AI algorithms were used to predict outcomes for interventions that were checked by behavioral scientists. Results Intervention reports contain many items of information needing to be extracted and these are expressed in hugely variable and idiosyncratic language used in research reports to convey information makes developing algorithms to extract all the information with near perfect accuracy impractical. However, statistical matching algorithms combined with advanced machine learning approaches created reasonably accurate outcome predictions from incomplete data. Conclusions AI holds promise for achieving the goal of predicting outcomes of behavior change interventions, based on information that is automatically extracted from intervention evaluation reports. This information can be used to train knowledge systems using machine learning and reasoning algorithms.

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窥镜下的人工智能和行为科学：现实世界应用的挑战

背景 人工智能 (AI) 正在改变科学研究的过程。人工智能，加上大型数据集的可用性和不断增长的计算能力，正在加速遗传学、气候变化和天文学等领域的进步[NeurIPS 2019 Workshop Tackling Climate Change with Machine Learning，加拿大温哥华；豪森 R，罗伯逊 BE。Morpheus：一种用于天文图像数据像素级分析的深度学习框架。Astrophys J 增刊系列。2020；248：20；Dias R, Torkamani A. AI 在临床和基因组诊断中的应用。基因组医学。2019;11:70.]。人工智能在行为科学中的应用仍处于起步阶段，实现人工智能的前景需要适应当前的实践。目的 通过使用 AI 在超出人类能力的范围内综合和解释行为改变干预评估报告的结果，HBCP 旨在提高研究活动的效率和有效性。我们通过人类行为改变项目 (HBCP) 期间吸取的经验教训，探索行为科学中采用 AI 所面临的挑战。方法 该项目使用人工智能算法开发和测试的迭代循环。使用已发表的行为干预随机对照试验研究报告的语料库，行为科学专家对干预的发生和结果进行了注释。AI 算法经过训练，可以识别与专家人类注释的干预和结果相关的自然语言模式。一旦训练，人工智能算法用于预测行为科学家检查的干预措施的结果。结果 干预报告包含许多需要提取的信息项，这些信息以研究报告中用于传达信息的巨大可变和特殊语言表达，使得开发算法以近乎完美的准确性提取所有信息是不切实际的。然而，统计匹配算法与先进的机器学习方法相结合，从不完整的数据中创建了相当准确的结果预测。结论 人工智能有望实现基于从干预评估报告中自动提取的信息来预测行为改变干预结果的目标。