Exploiting Interdisciplinary Research Design for Temporally Complex Big Data: Discussion of a Case‐Study Using on Heterogenous Bibliographic Big Data
Exploiting Interdisciplinary Research Design for Temporally Complex Big Data: Discussion of a Case‐Study Using on Heterogenous Bibliographic Big Data
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DOI：
10.1002/pra2.631
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发表时间：
2022-10
影响因子：
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通讯作者：
P. Mabry;B. Martinson;T. Valente;Xiaozhong Liu
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文献类型：
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作者：
P. Mabry;B. Martinson;T. Valente;Xiaozhong Liu
人工智能(AI)方法的价值在于它们能够从动态复杂的数据中预测结果。尽管有这一优点，但人工智能被广泛批评为“黑匣子”，即缺乏机械性的解释来伴随预测。我们介绍了一种新的跨学科方法，通过从四个学科角度提供共享的用例，平衡了数据驱动方法的预测能力和理论驱动的解释能力。该用例通过时间上复杂的、不同种类的书目大数据来检验科学的职业发展轨迹。讨论的主题包括：复杂问题中的数据表示，理论、假设驱动和数据驱动方法之间的权衡，人工智能可信性，模型公平性，算法可解释性和人工智能采用/可用性。将促使小组成员和听众讨论提出的方法与应对小组提出的挑战的其他方法的价值，并考虑它们的局限性和剩余的挑战。
Artificial Intelligence (AI) methods are valued for their ability to predict outcomes from dynamically complex data. Despite this virtue, AI is widely criticized as a “black box” i.e., lacking mechanistic explanations to accompany predictions. We introduce a novel interdisciplinary approach that balances the predictive power of data‐driven methods with theory‐driven explanatory power by presenting a shared use case from four disciplinary perspectives. The use case examines scientific career trajectories through temporally complex, heterogeneous bibliographic big data. Topics addressed include: data representation in complex problems, trade‐offs between theoretical, hypothesis‐driven, and data‐driven approaches, AI trustworthiness, model fairness, algorithm explainability and AI adoption/usability. Panelists and audience members will be prompted to discuss the value of approach presented versus other ways to address the challenges raised by the panel, and to consider their limitations and remaining challenges.