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Valentina Gianera

November 29th, 2020

Creating a pipeline for emerging technology in the newsroom

0 comments | 20 shares

Estimated reading time: 10 minutes

Valentina Gianera

November 29th, 2020

Creating a pipeline for emerging technology in the newsroom

0 comments | 20 shares

Estimated reading time: 10 minutes

Alyssa Zeisler is R&D Chief and a Senior Product Manager at The Wall Street Journal, a combined role she undertook as the pandemic was hitting the world. In this new episode of our interview series with women working on the intersection of AI and journalism, Alyssa shares how, in spite of the uncertainty of this moment in time, she is creating opportunities for the use of emerging technologies like AI throughout the WSJ Newsroom.

JournalismAI: Alyssa, you are Chief of R&D but also in charge of newsroom tools. How should we understand your role?

Alyssa: It was two unique teams we brought together a few months back. Historically, there was an R&D team as well as an editorial tools team. R&D focused on proof-of-concept projects and computational journalism using emerging technology. The editorial tools team, on the other hand, was responsible for production tools: the charting tools, our live coverage tools, the image cropping and publishing tools, etc. Bringing these teams together was an opportunity to embed the R&D approach into our legacy systems and operations,s and at the same time bring in a little bit more product thinking and tooling to R&D. So it really created a pipeline to bring in emerging technology and innovation throughout the newsroom in a more seamless way. 

By working hand-in-hand with reporters and journalists, we can understand what they need and articulate their requirements as features on existing tools or even new products. In that sense, we like to think of ourselves as a problem-solving team, with a focus on augmenting our reporting (aka super powering reporters), creating efficiencies and helping the newsroom to reach and engage our audiences.

So what is your own responsibility in that? What do you do on a daily basis? 

It very much depends! I see my role as future-proofing the newsroom. What are the types of products, systems and features we should be looking at now to make sure that we are in a really strong place, three, five, ten years down the line? From there, I work with the engineers on my team to specify the work we’ll do in the next 3-6 months.

Do you experience any particular challenges or resistances in this role?

Change is always hard and we are a team that brings in change to the newsroom – from new approaches, like that in R&D, to new processes, like that with newsroom tools. But we have been truly fortunate to work with people who are excited about the opportunity inherent in this work. They’ve helped us to bring about some of these more innovative experiments and then translate them through the rest of the newsroom.

Could you give us some examples of AI-driven tools that have been implemented in the newsroom?

One of the best examples is Talk2020. The development of the tool started with a journalist who put together a natural language processing tool to look at previous Trump rallies and the commonalities and differences between them. By collaborating with Factiva, we were able to bring in all of the transcripts from different candidates and other political figures, and categorise them through the use of machine learning. This way, we created a tool for the whole newsroom, to help journalists find relevant content and do rhetorical analysis leading up to the primaries and the 2020 elections. About two months ago, we expanded the access to Talk2020 beyond our newsroom, turning it into a user-facing tool.

We’ve also had a lot of success with new approaches in computer-assisted reporting. For instance, a tool that merges different levels of geometry was used in this recent piece to connect previously non-comparable regional data and in this piece to analyse the impact of Black Banks in the US. A new approach to information and data gathering was used in this piece showing that Google is giving preference to YouTube over other video sources.

If there is a customer interface that readers can go to in order to analyse things for themselves, what role is left for the journalists? 

This is now designed as a tool for our readers to retrieve additional information about a particular political figure. But it’s still our reporters and editors who find the stories to tell. We like to think of automation and computation as a what that allows our reporters to think more about the why. By helping reporters to find relevant information, this tool allows them to then put the context and the analysis around it more efficiently. We know our readers come to us for that analysis, not just for the numbers that are readily available elsewhere.

Does the use of AI challenge the need for journalists in any way?

I personally believe there is no AI that could replace a journalist nor would we want it to. At the WSJ we are looking at how to use AI to support and augment the work of journalists, not to replace it. 

AI still needs the so-called ‘human-in-the-loop’ or ‘human-as-a-backup’, doesn’t it? 

Yes. The real potential resides in the collaboration between human and machine. In the context of machine learning, we often forget how important the human is at the beginning of the data collection and training process, not merely as a backup. It’s important not to overlook the human part throughout the cycle. It’s not that the human does one thing and the machine another one: it is the coming together of different skill sets throughout the whole process.

How did you end up working at the intersection of journalism and AI?

When I began my career in media I asked: what are the largest problems that we are seeing in the industry? The lack of a user-centred approach at the core product level (the newsroom) became immediately apparent. So I started thinking about how to help people in the newsroom understand and engage more with their audiences. By joining or creating audience engagement teams at the FT and later at Barron’s, I was able to translate audience data into creating new editorial products like video series, podcasts, newsletters and new opportunities for community engagement. Product development led me to innovation and from there I started exploring the potential of AI and machine learning tools.

It’s not an easy time to work in journalism. Where do you find your motivation?

I truly believe in the mission of journalism, such as holding power to account, sharing the information that people need, and giving a voice to those who don’t have one. I want to support the business in any way that I can. 

Alyssa speaking about AI in journalism to a group of students of the Universidad Católica Argentina

There is still a significant gender gap in technical careers. Was this ever a challenge you had to face as a woman working in this field?

Earlier on in my career, I was more aware of being the only woman – young woman, especially! – at the table. But at the WSJ we actually have a majority of women who are product managers, which is really exciting. There is still a lot of work to be done in that regard, especially in the AI space. It is incredibly important to encourage diverse groups to contribute. One of the big questions we have revolves around bias inherent in some of these tools. We need to understand and mitigate those biases and make sure that we are creating algorithms that bring in more diverse voices rather than marginalising them. 

On a personal level, I have really benefited from having fantastic mentors and sponsors throughout my career, so I try to pay it forward and would encourage everyone reading this to do the same!

Talking of mentorship, you are also a coach with the JournalismAI Collab. What do you think projects like this can do for newsrooms? 

The coming together of people across different fields and competencies, abilities and companies, is really exciting. It has been really wonderful to see how all of these different perspectives unite to imagine innovative ways to use AI technologies! I am very excited to see what the different teams have been doing over the last couple of months at the JournalismAI Festival in December.

The interview was conducted by Valentina Gianera, POLIS intern and LSE’s MSc Student. It is part of a JournalismAI interview series with women working at the intersection of journalism and artificial intelligence.

Alyssa is a coach in our JournalismAI Collab, a global collaboration to experiment with AI. If you want to stay informed about the Collab and follow our activities, you can sign up for the monthly newsletter.

JournalismAI is a project of POLIS, supported by the Google News Initiative.

About the author

Valentina Gianera

Posted In: JournalismAI | Women in AI-journalism