The Future of News: Harnessing AI for Growth and Ethical Journalism—In Conversation with Chi-Chi Zhang

By Ann Blinkhorn, Blinkhorn LLC

Chi-Chi Zhang is a former Global Product Lead for News at Google. At Google, she was at the forefront of expanding AI capabilities for global newsrooms and building news products.

News and media companies have been using artificial intelligence and machine learning for over a decade. What’s different now??

Thank you, Ann. I’ve been looking forward to this conversation because I think there’s so much potential right now for newsrooms to leverage the power of AI to grow their business and reach new audiences.?

Ultimately, it’s about recent AI advancements that have led to more accessible products. I want to highlight two areas where I see the greatest potential for newsrooms to use technology to be more efficient in reporting, scaling their efforts and engaging new audiences to grow their businesses.

Natural Language Processing that leads to more efficient and accurate reporting - the ability for AI to understand, interpret, and generate human language with greater accuracy and nuance. A great example is the Pinpoint product that Google developed to analyze large collections of documents. Using Pinpoint, reporters can quickly find important stories within hundreds if not thousands of documents such as forms, handwritten memos, images, e-mail archives, PDFs, and more.

This technology is helping investigative journalists connect the dots in their reporting. As an example, AI helped power a Pulitzer Prize-winning investigation on the deadly consequences caused by states failing to track and prosecute reckless drivers in a series called Blind Spot. AI enabled the Blind Spot team to sift through 11 months of reporting, including data and public records, to uncover the magnitude of the problem.?

Machine Learning for Personalization - With AI, news organizations can better target users with personalized content. Organizations like The New York Times have been successful with developing interest-specific verticals such as Cooking. Now AI can help news organizations understand which users will most likely engage with those verticals with ease.?

At Google, we learned that AI-powered personalization unlocked user engagement and growth. Meaningful personalization also helped deepen a user's understanding. Personalization can come in the form of newsletters, notifications, landing pages, promotions, and topics a user may “follow” - helping answer the question “how” and “when.”?

But the exciting promise of AI can also lead to personalization which helps reach audiences in new languages, address different literacy levels and contextualize the news for readers at different levels of understanding on a topic.?

The biggest difference now is that medium and small-sized organizations can use AI to analyze their user data to better target audiences, whereas before this was only accessible to large media organizations with the resources to hire engineering teams.?Of course, there are also pitfalls, which is why it is so important for news organizations to adopt AI standards, conduct continuous evaluation and involve journalists at every stage of the user journey.?

What are the near-term opportunities and risks for deploying new AI tools in news organizations? Medium to long term?

The future of news and who comes out on top will rest on how these organizations are able to effectively integrate AI into their editorial workflows. Companies that choose not to embrace AI will be left in the dust.

That said, deploying new AI tools in news organizations presents opportunities and risks, both in the near-term and over the medium to long term.

Let’s talk about the near-term first. I see three areas of focus ripe for opportunity: efficiency, data analysis and content personalization.?

Efficiency and Cost Reduction: AI can automate routine tasks like data sorting, transcription, idea generation and writing outlines, leading to cost savings and allowing journalists to focus on more complex tasks.

Enhanced Content Personalization: AI algorithms can tailor content to individual user preferences, increasing engagement and time spent on the platform.

Improved Data Analysis: AI can analyze vast datasets quickly, uncovering trends and stories that might be missed by human journalists.

When it comes to medium to long term opportunities, I see three areas of opportunity:

Advanced Investigative Journalism and Predictive Analytics: AI will become a powerful tool for investigative journalism, analyzing complex data patterns to uncover stories. AI could help predict trends and public interest areas, aiding in planning and resource allocation for future stories.

An example of this could be analyzing search data. For example, during the Flint Water crisis, Flint residents were Googling symptoms and other issues related to water contamination in the community. AI could trigger an alert that lets journalists know when to dig into an issue in a community that may be rooted in a deeper problem.?

Global Reach: AI-powered translation and localization could make content accessible to a global audience, breaking language barriers.

The promise of AI with a global reach has always been to foster and deepen understanding by providing a perspective that is different from our own. I would love to see more news organizations leverage NLP to provide translation capabilities and contextualize to deepen users’ understanding around a story.?

Enhanced Audience Engagement: Sophisticated AI tools might enable more personalized and conversational interactions with users, fostering community and loyalty. Research has shown that news audiences have a strong desire to connect more deeply with the communities in which they identify with. There’s a real opportunity with AI, if done well, to strengthen community building.??

I’ve already seen companies experiment with this in building AI-powered prompts in the comments section of articles to encourage constructive discussions. Advancements in NLP can also help with moderation and providing context in guiding these discussions toward more constructive conversations.?

Regarding risks associated with deploying AI in newsrooms, there are several areas worth mentioning:

Job Displacement Concerns: The automation of routine tasks might lead to concerns about job losses or reduced roles for human journalists.

I think this is a very real risk for folks who are not interested in learning to use AI at all. For others who are thinking about all the AI opportunities and how to incorporate AI in their daily work, this will be less of a concern.?

Bias and Ethical Issues: AI systems can perpetuate biases present in their training data, leading to ethical concerns and potentially skewed reporting. I’m working with news organizations to define strong editorial standards when using AI to guide their work, which is paramount to get ahead of these risks.?

Dependence on Algorithms: Over-reliance on AI for content curation can lead to echo chambers and reduced exposure to diverse viewpoints. In an ideal world, I think we can have both AI and also news that all users should see. In the Google News app, I think we offered a balance of both headlines of the day along with articles related to a person’s personal interest. This also depends on the users, some users prefer to see more interest-based stories, so giving users some control can be a good solution.

Accuracy and Reliability: Early-stage AI tools might lack the sophistication needed to fully understand context and nuance, leading to errors in reporting.

Regulatory and Legal Challenges: As AI becomes more integrated into news production, navigating the legal and regulatory landscape will become more complex. However, I’m heartened to see that companies like NYT and AP are already getting ahead of this.?

What are the organizational implications of using AI technologies and tools in the context of news and media companies? Do you see new leadership roles being created to address AI transformation??

The organizational implications of using AI technologies and tools in news and media companies are significant. They affect various aspects of the organization, from workflow and employee roles to strategic decision-making and leadership structures. Here's a breakdown of these implications and the new leadership roles emerging to address AI transformation:

First, some of the organizational implications:

Workflow and Process Changes: AI tools can automate routine tasks, leading to changes in workflows. This requires organizations to redesign processes, integrating AI effectively while maintaining journalistic quality and integrity.

Skillset Shift: There's a growing need for employees with skills in data science, AI, and machine learning, alongside traditional journalistic skills. Newsrooms must either train existing staff or recruit new talent with these competencies.

Cultural Adaptation: Integrating AI requires a shift in organizational culture, encouraging innovation, continuous learning, and adaptability among employees.

Collaboration and Partnerships: News organizations may need to collaborate with tech companies, academic institutions, and other media companies to stay abreast of AI advancements.

In my view, there will likely be a number of new AI-related leadership roles in news companies. Some include:

Chief AI Officer (CAIO): Responsible for overseeing the strategic integration of AI technologies in the organization. This role involves decision-making around AI investments, ethical considerations, creating toolkits, and aligning AI initiatives with the company’s overall strategy.

AI Ethics Officer: Focuses on the ethical implications of AI use, ensuring that AI tools and algorithms adhere to ethical standards and journalistic principles.

Data Science and Analytics Leaders: These roles involve leading teams that analyze data to inform journalistic content, audience insights, and business strategies.

AI Project Managers: Specializing in managing AI-related projects, ensuring they align with editorial goals and are delivered effectively.

AI Training and Development Managers: Responsible for upskilling existing staff to work alongside AI technologies and understanding their capabilities and limitations.

Content Automation Strategists: Develop strategies for automated content generation, ensuring it complements human-created content and adheres to quality stand