Making Artificial Intelligence Work for Investigative Journalism
Making Artificial Intelligence Work for Investigative Journalism
复制标题
让人工智能为调查新闻工作
DOI：
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
J. Stray
中科院分区：
文献类型：
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作者：
J. Stray
摘要 许多人设想使用人工智能方法在大量数据中发现公共利益的隐藏模式，从而大大降低调查性新闻的成本。但到目前为止，只有少数调查报道以相对狭窄的方式利用了人工智能方法。本文调查了使用人工智能技术进行调查报道所取得的成就，为什么难以应用更先进的方法，以及人工智能在短期内可以解决哪些调查新闻问题。新闻问题通常是特定故事所独有的，这意味着训练数据不易获得，复杂模型的成本无法在多个项目中摊销。与故事相关的许多数据无法公开获取，而是掌握在政府和私人实体手中，通常需要收集、谈判或购买。新闻推断需要非常高的准确性或大量的人工检查，以避免诽谤的风险。使某些事实具有“新闻价值”的因素具有深刻的社会政治性，因此难以进行计算编码。人工智能在调查性新闻领域的近期最大潜力在于数据准备任务，例如从不同文档中提取数据和概率跨数据库记录链接。
Abstract Many have envisioned the use of AI methods to find hidden patterns of public interest in large volumes of data, greatly reducing the cost of investigative journalism. But so far only a few investigative stories have utilized AI methods, in relatively narrow ways. This paper surveys what has been accomplished in investigative reporting using AI techniques, why it has been difficult to apply more advanced methods, and what sorts of investigative journalism problems might be solved by AI in the near term. Journalism problems are often unique to a particular story, which means that training data is not readily available and the cost of complex models cannot be amortized over multiple projects. Much of the data relevant to a story is not publicly accessible but in the hands of governments and private entities, often requiring collection, negotiation, or purchase. Journalistic inference requires very high accuracy, or extensive manual checking, to avoid the risk of libel. The factors that make some set of facts “newsworthy” are deeply sociopolitical and therefore difficult to encode computationally. The biggest near-term potential for AI in investigative journalism lies in data preparation tasks, such as data extraction from diverse documents and probabilistic cross-database record linkage.