very-good-science / data-hazards

Data Hazards is a project to find a shared vocabulary for talking about worst-case scenarios of data science - and to use that vocabulary to help people understand and avoid Data Hazards.
https://datahazards.com
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Could there be a hazard for not doing the research/work? #147

Open Lextuga007 opened 1 year ago

Lextuga007 commented 1 year ago

It came up from one of the talks I did introducing the hazards to NHS analysts where an analyst made the great suggestion of the hazard of not doing the work. That could, I suppose, fall into General Hazard and it might also be that this falls into the usual justifications for research but because we often don't need to make those justifications for data analysis as analysts in health and care we don't list that anywhere as such. I'd be very interested to hear people's views on this 🙂

NatalieZelenka commented 1 year ago

Hi Zoe, this actually isn't the first time this has been mentioned to us. Thank you for documenting it here!

My thoughts are:

I'd love to find out if other people have different opinions. I'll share this on the slack and see if anyone else wants to add their views.

Lextuga007 commented 1 year ago

I love these counterpoints! Point 3 is a bit like opportunity cost I guess, if we did A we couldn't do B or C for example? Those are very problem-solving approaches to a question rather than an ethical consideration. Really important distinction that is not always easy to see as we spend so much time problem-solving - it's hard to step back and stop doing that!

ninadicara commented 5 months ago

Coming late back to this as I review issues, but something that has come up whilst doing the JGI Seedcorn projects is that many of them are intended to address Data Hazards. While they still have their own Hazards, we have also allowed a section in the form for people to explain how the work they are doing is aiming for improvements.

I used to feel strongly that we shouldn't let people do that because it drew attention away from the Hazards, but now I feel like allowing people to say what they're addressing is a nice compromise on this area, because it may also allow for a collation of projects working to improve the status quo around the Data Hazard themes, which could be a powerful way of seeing gaps and opportunities for things we want to work on.

Also, in terms of the culture of research around this, I've been doing some work on this project about researcher well-being, and one of the helpful measures for people who work in distressing areas is hope that what you are doing makes a difference. In the current context of AI and data science (over)development, I think being able to say what we are doing to push back might be even more important.

The most important thing (I think) is that people working to address one or more of the labels don't fall into the assumption that because they are trying to improve things, their work can't have the same or other Hazards :D