“The world will never deny itself a journalist who knows something about a field”, for Iona Manolescu

Through his “Fake and cause” series of interviews, 20 minutes illuminates themes around conspiracy, fact-checking and issues for democracy. 20 minutes provides a platform for researchers, associations, experts or other members of civil society to open the debate.

During the Seine Media Day, organized by The Parisian and France Info, we took advantage of a discussion on technology and information to meet Ioana Manolescu. For almost ten years, the computer science researcher, also a professor at the Ecole Polytechnique, has been trying to cultivate a healthier relationship between journalists and data processing.

Iona Manolescu, researcher at INRIA – S. Erôme

You were one of the first researchers to take an interest in the ways in which technology and especially data processing can help verify facts. Where did your observation come from?

I made two observations at once. The first is that in France and more broadly in Europe, we have very high quality open databases developed with taxpayers’ money. We have every right to access them and they contain very interesting information. On the other hand, by reading the press every day, I realized that I was asking myself some questions that in principle we could answer from the available open data, but I did not see them addressed in media.

Maybe you have an example?

During the 2008 crisis, I heard the government promise that by investing a certain number of billions in the auto industry, we would keep jobs in the industry. Maybe this is a good idea, but I don’t have an answer. After a few years, I said to myself that we have all the numbers necessary to know if the investment has preserved the jobs and how many. However, in the press, no one questioned the evolution of work in the automotive industry when we had all the data.

Is it a lack of awareness of data availability or lack of free data available for use?

In the European democratic and technocratic sphere, open data is something of great importance. In France – although we are not the only country – we are fortunate to have an interministerial department for digital technology that strongly encourages the creation of open data sets at the level of all French administrations, but also the development of small that software brick. which allows specific tests to be performed and which is made open source. As a reminder, open source is free access programs. It is often confused with open data, data that does not move. Open source is more like code. All these tools exist, so does the data. So why isn’t anyone using them? That’s when I learned that in many newsrooms, no tools exist to process the data.

You’ve been working ever since The world and now on Radio France. What does your media work consist of?

Our first approach was to ask journalists what they wanted to use the data for. Therefore, we worked on the request by adding it to our existing model which is mainly used to exploit statistics. Every IT project starts small. Ours is growing quite a bit and will continue to grow. When we form a team of researchers, we look at how it works, we ask ourselves what needs to be changed or whether it will continue like this. This is a very iterative process.

IT and journalism do not necessarily use the same language. How would you adapt your work to information professionals?

Often, when I start working with journalists, I ask them what data they use. There is one thing that a computer scientist does not immediately understand, it is the notion of data quality. Some of my scientific colleagues are developing verification systems that take “we ask Google” as a data source. We look at the first 1,000 requests, take an average and select the most popular. It’s technically feasible, but it’s worthless for journalists. That’s something we learned quickly.

Journalists fear that they will soon be replaced by artificial intelligence. But is technology really dangerous for the profession?

There is no perfect Artificial Intelligence (AI). You should know that if you have a very good Artificial Intelligence, by playing with it for two hours, you will definitely make it stupid. What we commonly call “AI” are systems that learn from many examples. On the contrary, the examples it did not see, the AI ​​did not learn them. This is a loss. The second thing is that the AI ​​has no idea what it’s talking about. If we say “now the sky is…”, most AIs will complete the word “blue”. But they won’t understand why if we don’t explain it to them. He didn’t understand what “heaven” meant, he just calculated probabilities.

Finally, does the person always have to be present to understand the need for information?

Of course, you always have to check by hand. Every time we provide a figure from the Insee database, we provide the link to the page for double checking. If we take the case of policemen who died doing their job in France, there are two different data: mission deaths and service deaths. It’s not the same statistic because “deaths in service” includes road accidents… which can really make a difference. But to understand it, you need to have some expertise in the field. The world will never lack a journalist with knowledge in a field, with expertise. The same human expertise will make it possible to identify contacts and the right resources. What I plead is that from there we apply quick tools but we must keep in the hands of someone who knows the field. The computer will generally not succeed in making a story interesting.

In recent years, the number of fake news, but also an exponential number of data. How to ensure perfect data verification?

I believe the first step is to work with the right data sources. In computing, we say “garbage in, garbage out”: if the data is wrong, nothing good comes out. Above all it is necessary to move towards the right data sources and nothing else. From there, you need to see what questions you need to answer and what tool you put in place to review. It is still up to the journalist to make the distinction between what is said and what is found in the sources.

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