A new era of AI-assisted journalism at Bloomberg

Abstract

Artificial intelligence (AI) is impacting and has the potential to upend entire business models and structures. The adoption of such new technologies to support newsgathering processes is established practice for newsrooms. For AI specifically, we are seeing a new era of AI-assisted journalism emerge with trust in the AI-driven analyses and accuracy of results as core tenets.

In Part I of this position paper, we discuss the contributions of six recently published research papers co-authored by Bloomberg's Artificial Intelligence Engineering team that show the intricacies of training AI models for reliable newsgathering processes. The papers investigate (a) the creation of models for updated headline generation, showing that headline generation models benefit from access to the past state of the article, (b) sequentially controlled text generation, which is a novel task and we show that in general, more structured awareness results in higher control accuracy and grammatical coherence, (c) chart summarization, which looks into identifying the key message and generating sentences that describe salient information in the multimodal documents, (d) a semistructured natural language inference task to develop a framework for data augmentation for tabular inference, (e) the introduction of a human-annotated dataset (ENTSUM) for controllable summarization with a focus on named entities as the aspect to control, and (f) a novel defense mechanism against adversarial attacks (ATINTER). We also examine Bloomberg's research work, building its own internal, not-for-commercial-use large language model, BloombergGPT, and training it with the goal of demonstrating support for a wide range of tasks within the financial industry.

In Part II, we analyze the evolution of automation tasks in the Bloomberg newsroom that led to the creation of Bloomberg's News Innovation Lab. Technology-assisted content creation has been a reality at Bloomberg News for nearly a decade and has evolved from rules-based headline generation from structured files to the constant exploration of potential ways to assist story creation and storytelling in the financial domain. The Lab now oversees the operation of hundreds of software bots that create semi- and fully automated stories of financial relevance, providing journalists with depth in terms of data and analysis, speed in terms of reacting to breaking news, and transparency to corners of the financial world where data investigation is a gigantic undertaking. The Lab recently introduced new tools that provide journalists with the ability to explore automation on demand while it continues to experiment with ways to assist story production.

In Part III, we conceptually discuss the transformative impact that generative AI can have in any newsroom, along with considerations about the technology's shortcomings in its current state of development. As with any revolutionary new technology, as well as with exciting research opportunities, part of the challenge is balancing any potential positive and negative impacts on society. We offer our principles and guidelines used to inform our approach to experimenting with the new generative AI technologies. Bloomberg News’ style guide reminds us that our “journalism is aimed at possibly the most sophisticated audience in the world, for whom accuracy is essential.”