Is this just a combination of anchoring with confirmation bias? Or have I misunderstood.

Thank you for responding Catherine! It’s very much appreciated.

This should therefore be easily transferable into feedback to the grantee.

I think this is where we disagree - this written information often isn’t in a good shape to be shared with applicants and would need significant work before sharing.

I think this is my fundamental concern. Reasoning transparency and systematic processes to record grant maker’s judgments and show how they are updating their position should be intrinsic to how they are evaluating the applications. Otherwise they can’t have much confidence in the quality of their decisions or hope to learn from what judgment errors they make when determining which grants to fund (as they have no clear way to track back why they made a grant and whether or not that was a predictor for its success/failure).

I am not so aware of the assumption you make up front, and would agree with you that anyone making such an assumption is being naive. Not least because humans on average (and even supers under many conditions) are objectively inaccurate at forecasting - even if relatively good given we don’t have anything better yet.

I think the more interesting and important when it comes to AI forecasting and claiming they are “good”, is to look at the reasoning process that they undertaken to do that. How are they forming reference classes, how are they integrating specific information, how are they updating their posterior to form an accurate inference and likelihood of the event occurring? Right now, they can sort of do (1), but from my experience don’t do well at all at integration, updating, and making a probabilistic judgment. In fairness, humans often don’t either. But we do it more consistently than current AI.

For your post, this suggests to me that AI could be used to help base rate/reference class creation, and maybe loosely support integration.

Thank you for posting this, is it definitely nice to get a funders perspective on this!

From the other side (someone who has applied for grants and received little to no feedback on them), and having been involved in very large scale grant making through my governmental role, I fear your point (1) below is likely to be the greatest influence on grantmakers not providing feedback. Unfortunately, this I find (and did when I was a grantmaker and was prevented/unable to provide feedback) is often a cover for a lack of transparent and good reasoning practice in the grant decision process. 

The vast majority of EAs are aware of reasoning transparency and good Bayesian reasoning practices. I'd hope, as I assume many members of the EA community do, that EA grant makers have a defined method to record the grantmakers judgments and what is updating their view of a grants potential impact and likelihood of success. Not least because this would allow them to identify errors and any systematic biases that grantmakers may have, and thus improve as necessary. This should therefore be easily transferable into feedback to the grantee. 

The fact this isn't done raises questions for me. Are there such systematic processes? If not, how do grantmakers have confidence in their decision making a priori? If there are such processes to record reasoning, why can't they be summarised and provided for feedback?

The post you linked by Linch and the concern he raises that by being transparent about the reasons for not making a grant may risk applicants overupdating on the feedback seems unfounded/unevidenced. I also question how relevant given they weren't funded anyway, so why would you be concerned they'd over update? If you don't tell them they were a near miss and what changes may change your mind, then instead the risk is they either update randomly or the project is just completely canned - which feels worse for edge cases.
 

There might have been a lot of factors that led to a decision. 

Sometimes there are multiple small reasons to be hesitant about a person doing a particular project – none of which are “deal breakers”. There is often so much uncertainty and decision-makers realistically have so little information such that the only option is to rely on small bits of information to update their view on the person/project. Each of these factors on their own might just be a small grain of sand on a scale, or be felt with low confidence, but together they might build up to tip the scale. 

I tend to agree with you, though would rather people were more on the “close early” side of the coin than the “hold out”. Simply because the sunk cost fallacy and confirmation bias in your own idea is incredibly strong and I see no compelling reason for how current funders in the EA space help counteract these (beyond maybe being aware of them more than the average funder).

In an ideal system the funders should be driving most of these decisions by requiring clear milestones and evaluation processes for who they fund. If the funder did this they would be able to identify predictive signals of success and help avoid early or late closures (e.g. “we see on average policy advocacy groups that have been successful have met fewer/more comparable milestones and recommend continued/stopping funding”). This can still allow the organisation to pitch for why they are outside of the average, but the funder should be in the best position to know what is signalling success and what isn’t.

Unfortunately I don’t see such a system and I fear the incentives aren’t aligned in the EA ecosystem to create it. The organisations getting funded enjoy the looser, less funder involved setup. And funders de-risk their reputational risk by not properly evaluating what is working and why, and they can continue funding projects they are personally interested in but have questionable causal impact chains. *noting I think EA GHD has much less of this issue mainly because funders anchor on GiveWell assessments which is to a large degree delivering the mechanism I outline above.

The science underpinning this study is unfortunately incredibly limited. For instance, there isn’t even basic significance testing provided. Furthermore, the use of historic events to check forecasting accuracy, and the limited reporting of proper measures to prevent the model utilising knowledge not available to the human forecasters (with only a brief mention of the researchers providing the search link with pre-approved) is also very poor.

I’m all for AI tools improving decision making and have undertaken several side-projects myself on this. But studies like this should be highlighted for their lack of scientific standards and thus, we should be sceptical of how much we use them to update our judgments of how good AI should be at forecasting currently (which to me is quite low given they struggle to causally reason)

What are some of the best (relatively) small podcasts on AI and/or policy that people would recommend? I know of all the big ones, but keen to see if there are any nascent ones that are worth sharing with others.

Thanks for posting this. Sounds like an exciting project.

There have been a number of experimental studies done on improving individual and group forecasting, and a couple of organisations currently provide training in a classroom/workshop setting. From my knowledge the framework being proposing is quite different to these (albeit I respect it can only explain it at a high level here). What’s the main evidence base guiding this approach and what’s the expected increase in accuracy attendees can expect the course to have?

I think this is a really important area, and it is great someone is thinking whether EAIF could expand more into this sort of area. 

To provide some thoughts of my own having explored working with a few EA entities through our consultancy (https://www.daymark-di.com/) that works to help organisations improve their decision making processes, along with discussions I've had with others on similar endeavours:

1. Funding is a key bottleneck, which isn't surpising. I think there is naturally an aversion to consultancy-type support in EA organisations, mostly driven by lack of funds to pay for it and partly because I think they are concerned how it'll look in progress reports if they spend money on consultants. 
   1. EAIF funding could make this easier, as it'll remove the entire (or a large part) of the cost above.

2. There does appear a fairly frequent assumption that EA organisations suffer less from poor epistemics and decision making practices, which my experience suggests is somewhat true but unfortunately not entirely. I want to repeat what Jona from cFactual commented below that there are lots of actions EA organisations take that are very positive, such as BOTECs, likely failure models, and using decision matrices. This should be praised as many organisations don't even do these. However, the simple existence of these is too often assumed to mean good analysis and judgment will naturally occur and the systems/processes that are needed to make them useful are often lacking. To be more concrete, as an example few BOTECs incorporate second order probability/confidence correctly (or it is conflated with first order probability) and they subsequently fail to properly account for the uncertainty of the calculation and the accurate comparisons that can be made between options. 

   It has been surprising to observe the difference between some EA organisations and non-EA institutions when it comes to interest in improving their epistemics/decision making. With large institutions (including governmental) being more receptive and proactive in trying to improve - often those institutions are mostly being constrained by their slow procurement processes as opposed appetite.

When it comes to future projects, my recommendations of those with highest value add would be:

1. Projects that help organisations improve their accountability, incentive, and prioritisation mechanisms. In particular helping to identify and implement internal processes that properly link workstreams/actions/decisions/judgments to the goals of the organisation and that role's objectives. This is something which is most useful to larger organisations (and especially those that make funding/granting decisions or recommendations), but can also be something which smaller organisations would benefit from.
2. Projects that help organisations improve their processes to more accurately and efficiently reason under uncertainty, namely assisting with defining the prediction/diagnostic they are trying to make a judgment on, identify the causal chain and predictors (and their relative importance) that underpins their judgment, and provide them with a framework to communicate their uncertainty and reasoning in a transparent and consistent way.
3. Projects that provide external scrutiny and advanced decision modelling capabilities. I think at a low level there are some relatively easy wins that can be gained from having an entity/service which red teams and provides external assessment of big decisions that organisations are looking at making (e.g. new requests for new funding). At a greater level there should be more advanced modelling (including using tools such as Bayesian models) which can provide transparent and updatable analysis which can expose conditional relationships that we can't properly account for in our minds.
   1. I think funding entities like EA Funds could utilise such an entity/entities to inform their grant making decisions (e.g. if such analysis was instrumental in the decision EA Funds made on a grant request, they'd pay half of the cost of the analysis).

I'm considering my future donation options, either directly to charities or through a fund. I know EA Funds is still somewhat cash constrained but I'm also a little concerned with the natural variance in grant quality. 

I'd be interested in why others have or have not chosen to donate to EA Funds, and if so, would they again in the future?

I respect people may prefer to answer this by DM, so please do feel free to drop me a message there if posting here feels uncomfortable.