5 Key discussion points regarding ethics in data journalism
A look at Rebekah E.D. McBride’s paper which raises 5 ethical points every data journalist should cover.
1: Take responsibility for the data you are publishing…
“First and foremost, journalists must take responsibility for the data they are planning to publish. That means going through the data with a fine-toothed comb and giving it, as Waite called it, the “data smell test” to be sure that everything seems legitimate.”
You wouldn’t publish an article without checking it for bias, legal issues (such as liability or slander) so let me ask you, would you do the same for a data journalism article?
Put it this way, just because you’re putting it through Excel (or another program) it does not mean that the data is 100% accurate. Nor does it mean that the data you get is truthful (see point 2 for more detail).
This is perhaps the most common, yet obvious rule, but one that you must remember. All you need to do is make sure your check the data, cover all the bases and remember to treat any data you get with some salt.
2: The 5 W’s still apply to data journalism…
Journalists should also consider where the data is coming from, who put the data together, what were the person’s motives, etc.
Every time you write a data story, ask yourself the following points:
What: Are you sure you know what the data is? Are you clear what the data is showing, is it really showing you what you want and is the data accurate for the story you are writing?
Who: Simply put, who has published the data? Could it be accurate, could they be presenting the data in a way which twists the truth? Have they even presented you with all the data?
Where: This could mean a number of things. Is the data published in a place that might present bias? Is the data applicable to the area that you are writing about? Where within the data is the set that applies to your story the most?
When: When did the data come out? Is the data relevant or was it published a while ago and does this mean the figures are still relevant to the story?
Why: You might even ask yourself why they put the data up. Was it because it’s routinely published online or was it because someone FOI’d it? Could you expand the request or does the request present a risk of bias?
3: Is the data all there?
During this process, a journalist should also look for missing data and values and try to determine how that affects the end result.
This is a simple one, but one that the paper refers to as a common mistake that people can make. Every time that you look at data you should take five minutes to make sure the data is all there and that you’re not missing any of it.
4: Expand on the story…
Journalists should not draw from the tail end of the data by interviewing or focusing on a stereotype or an exception. Instead, they must become embedded in the community and speak with sources that range in age and experiences.
This is a problem that I faced in my first year as a data journalist, expanding the story beyond simply the numbers within the data.
When creating a story expand on it. How do you do that you ask? Well, in my domestic abuse story once I had gathered the details via the data I had gathered, I became embedded in the community from a victim’s perspective by contacting charities and getting their side of the story and also including case-studies of victims who had suffered from domestic abuse.
In the end, the numbers were the least important part of the story. It was the human element provided by the charity and case-studies which really put the 88% rise into perspective and is perhaps the reason the story did so well.
5: Don’t just focus on the numbers…
Take part in systemic reporting that addresses the larger issues and not just the numbers or one individual behind the numbers. In other words, address the societal issues surrounding the data and explore solutions.
There’s not much to say about this one as it is covered in point 4, but it is one that the paper brings up a number of times.
Think of the bigger issues at hand and not just at the numbers.
Again, we can look at my domestic abuse example. Yes, the number of male victims going to the police had increased by 88% but why? According to a charity (that was featured in the article), it was because the stigma of it being a crime that only woman are impacted by was disappearing and with it featuring in soap operas more men were able to identify themselves as a victim.
Yes, the numbers were the core of the article, but the charities comments provided real-life social context to the issue and helped provide a human voice to the data and helped me to explore the meaning behind it.