To better understand what their intent is, I recommend clicking on the little hamburger menu at the top and reading the “Issues” page listed there.
Show HN: Identify car crash editorial anti-patterns using NLP
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Re: Show HN: Identify car crash editorial anti-patterns using NLP
#12As feedback, speaking of contextualizing things, it would help if you would contextualize your colored highlighting by bringing some of the explanations of issues onto the page itself where the content is marked up. Or have links (maybe have the highlighted words be links) going to a writeup telling why those words got that color.
Re: Show HN: Identify car crash editorial anti-patterns using NLP
#13Instead of just "a woman was killed" it could mention the devastating effect to her family.
Re: Show HN: Identify car crash editorial anti-patterns using NLP
#14I gather that the intent is not to make the reporting more neutral or accurate, but to change the framing in a direction that vision zero finds more appealing. E.g. in the first article we should view the woman as a vulnerable road user, bearing no responsibility for being struck by a vehicle even though video evidence shows that she fell into the street.
Yeah, this is a little odd to me. The app frames itself as a way to improve reporting, but the way it intends to improve it is by imposing an inherently less neutral position on the language used. There's no fact-checking going on here that would lead the program to decide the original report was incorrect and needed updating, except that it doesn't frame the story in the designer's point of view. An extremely postmodern app.
It's possible (and not unlikely) that this is how more news will be processed in the future.
Re: Show HN: Identify car crash editorial anti-patterns using NLP
#15Worth noting the history behind this issue of biased reporting:
> In the late 1920s and ’30s, a consortium of automobile manufacturers, insurers, and fuel companies known as the National Automobile Chamber of Commerce funded a wire service that provided free reporting on crashes to short-staffed Depression-era newspapers. Reporters could send in a few basic details about a local collision, and the wire service would craft a narrative that exonerated the driver, blamed any pedestrians who were involved, and — crucially — transformed virtually every “crash” into an understandable or even inevitable “accident.” Newspapers around the country published the industry-approved stories, often without edits.
Source: https://usa.streetsblog.org/2020/03/05/streetsblog-101-how-j...
Re: Show HN: Identify car crash editorial anti-patterns using NLP
#16Instead of just "a woman was killed" it could mention the devastating effect to her family.
How does the program know how the family reacted?
Re: Show HN: Identify car crash editorial anti-patterns using NLP
#17Very nice, I like it! And as a frequent cyclist and writing instructor, really appreciate this kind of work on many levels. Good to see NLP towards social good like this.
Re: Show HN: Identify car crash editorial anti-patterns using NLP
#18I like the general idea, and the use of NLP to implement it.
But I am dubious about some of the principles behind it.
- For example, I find it inaccurate to say "the driver hit the pedestrian", which to me suggests a collision between two people, not between a person and a vehicle. (Of course this does not apply to a phrase like "the vehicle fled the scene" - it is clear that it was the driver who fled the scene). While it's obvious the driver is responsible for the trajectory of their vehicle, it's also clear that the injuries and deaths are caused by the fact that one of the elements involved in the collusion is a 1+ ton piece of steel, and the other one a 70kg human being.
- Regarding the term "accident", I see in the Merriam-Webster that it is defined in this context as: "an unfortunate event resulting in particular from negligence or ignorance". It seems to me that this definition does not exonerate the driver from responsibility (lack of vigilance or competence).
Re: Show HN: Identify car crash editorial anti-patterns using NLP
#19I like the general idea, and the use of NLP to implement it. But I am dubious about some of the principles behind it. - For example, I find it inaccurate to say "the driver hit the pedestrian", which to me suggests a collision between two people, not between a person and a vehicle. (Of course this does not apply to a phrase like "the vehicle fled the scene" - it is clear that it was the driver who fled the scene). Wh…
I find "the vehicle fled the scene" as a useful phrase that's used to distinguish this situation from the very different "the driver fled the scene" which is commonly used in the (not that rare) cases where the driver abandons the wrecked car after an accident, in some cases to hide that they were intoxicated.
Re: Show HN: Identify car crash editorial anti-patterns using NLP
#20I gather that the intent is not to make the reporting more neutral or accurate, but to change the framing in a direction that vision zero finds more appealing. E.g. in the first article we should view the woman as a vulnerable road user, bearing no responsibility for being struck by a vehicle even though video evidence shows that she fell into the street.
Yes, I do not think you bear responsibility if you collapse and someone strikes you with their car and then flees the scene.