This does not necessarily mean getting people to join the EA movement, as there are some dangers to rapid movement growth. This does mean, however, getting the ideas of EA-style effective giving to a broad audience. As an example, this article encourage people to give effectively, and only briefly mention Effective Altruism. Doing so balances the benefits of using marketing tactics to channel money to effective charities, while not heavily promoting EA itself to ameliorate the dangers of rapid movement growth.
For example, here is a link to the outcome of an Intentional Insights collaboration with The Life You Can Save to spread effective giving to the secular community through Giving Games. Giving Games are a participatory workshop where participants learn about a few pre-selected charities, think about and discuss their relative merits and evidence for each, and choose which charity will get a donation of about $10 per participant, with donations sponsored by an outside party, in this case The Life You Can Save. We have launched a pilot program with the Secular Student Alliance to bring Giving Games to over 300 secular student groups throughout the world, with The Life You Can Save dedicating $10,000 to the pilot program, and easily capable of raising more if it works well.
Josh JUsing the database of all 501(c)3 organizations in the US, I plan to create ratings for every charity, based on effectiveness.
Rationale: GiveWell and EA in general have had trouble with mass-appeal in part because tools that work on overhead ratio are more popular. These, such as Charity Navigator, have great appeal in large part because people can get a score for nearly any organization. This will create an EA alternative. The version 0 of this can be created quite easily and still be meaningful, while iterations will much improve the product.
Limitations: Initial scores will lack confidence as they will be based pretty much on just the organization name. Long-term scores will never approach GiveWell levels of confidence. Organizations with names with non-obvious purposes (i.e. Kiva) will have no rating until a manual review.
To address these limitations, clear and prominent communication on levels of confidence will be central, and manual reviews will be ongoing.
Data / evaluation
Use charity's names to tag them for effectiveness indicators
Examples: a disease name, a location name, a cause area name, type of action taken, etc.
i.e. if organization name contains 'Malaria', it's more likely to be effective
i.e. if organization name contains 'Congo', it's more likely to be effective
i.e. if organization name contains 'Homeopaths', it's likely to not be effective
Manually program in obvious scores, such as AMF and Homeopaths without Borders
Based on the tags and number of tags assign a rating of effectiveness and a level of confidence in the rating
Begin manually evaluating charities to turn this into a supervised machine learning problem, and update ratings and confidence levels for charities that have not yet been manually rated
Front-end presentation of charity ratings
Front-end index &/or search page
Implement a back-end that reads from data file and updates the view
Needed: person or technical service for part 2b
Needed: manual evaluators comfortable providing opinions on charity effectiveness based on the limited available public data (instructions and guidelines will be given)
Optional: Data scientists to review / coordinate on data implementation
Optional: Someone to lead the front-end presentation from the design standpoint