Difference between revisions of "Data in the project lifecycle"

Revision as of 22:57, 2 October 2014

Subtitle: one sentence on what it does, who is it for, and what is its goal

Outputs

A lightweight tool for data project implementers to help plan their projects and check whether they are being responsible with their data at every step, both for planned and emerging risks.

The pipeline was designed for civic data projects with some social mission. We specifically excluded projects run by governments and commercial projects - though it would be interesting to also try it with those.

Journalism was an interesting source of debate - in the end we decided that there was two sorts of journalism:

1. Journalism by conscientious journalists - who want to change the world with their work. 2. Journalism designed to sell newspapers.

We figured that the second might be a bit of a lost cause in terms of uptake - so we bore in mind only projects which we thought fitted into the first category.

Connection to previous RDFs

RDF NYC Primer for Responsible Data for Development Practitioners: https://github.com/nshamas/Responsible-Development-Data-Digital-Edition

Geeks without Bounds Whitepaper (RDF Oakland) http://gwob.org/blog/2014/07/19/publications/

Notes

We separated the projects into three main phases, and multiple phases within those projects.

Pre-Life

Getting to Go

• Is there a theory of change?
  • Does that theory of change cover both utopian and distopian versions of reality?
• Are there potential legal risks?
• Have we done a stakeholder map and a power analysis map?
• Are there clear user stories? (Note: believe that this is a different exercise to stakeholder mapping)
• Have we specified how lessons learned will be shared?
• Are we the best people to collect / manage / work with the data?
• Who in the team will internalise data responsibility? Who will not?
  • Do we need to give anyone any training?
• Have we specified a retirement plan? OR Is our plan to live forever realistic?
• Are we targeting anyone specific with our project?
  • If 'yes' - what is the risk of collateral?

Life

Data After Life

Meta Zone

DISCLAIMER: This pipeline may be reductive / more useful in some contexts than others. For cases where we are not yet sure whether the methodology will work, or for cases for which the methodology was not designed - we have created a parking lot below - so that we can work out later whether we need to tweak the methodology or not.

Free Parking

The following items were 'parking lotted' - as we have not yet tested whether they would be caught out by this pipeline - please add yours here!

• 'Big Data Projects' (yes, we know Big Data is not a thing - but the question was raised)
• [Add yours here]

Audience

This is for the implementers of a data project. (We considered breaking down specific roles and defining who should be asking which question when - but ran out of time).

Next steps

• Define who should be asking each question
• Define whether there are any checkpoints / gates for projects run by NGOs, volunteers or lone wolves. Projects with / without funders.

Contributors

Food for thought

• concepts, problems
• questions to ask frequently
• preventions: what do you actually do in concrete terms to prevent these things from happening
• reactions: responsible responses for when things go wrong

Resources (we <3 links!)

Feel free to link any and all background material, additional info, useful resources, etc. The more the merrier!

• Blog post on outputs from the Ethics of Data conference: https://www.theengineroom.org/the-big-picture-key-milestones-and-questions-for-ethical-data-projects/

• Spreadsheet of milestones and questions that came out of the Ethics of Data conference: https://docs.google.com/spreadsheets/d/12Uqeh4hobw-5KLeyNfpsp7IYhaNrb_psLifCQqSjXDc/edit?pli=1#gid=0

• 'Inserting Ethics into Data Projects': http://textontechs.com/2014/09/infusing-ethics-into-data-projects/