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Top-level
@brianonbarrington @Wolven Ah, OK. An *attempt* to cut out the human, initially forgetting that you don't actually have any non-human data to fit to. Are people often discussing the problem of time here by the way? If you're fitting to 30-year old data, you're fitting to a different society. 30 years ago is not a very reliable guide to what's going on in any specific neighbourhood (at least not here in Helsinki). 8 comments
@TorbjornBjorkman @brianonbarrington @Wolven Or maybe some kind of audit process would work (clearly I have no expertise whatever). It still seems a lot like the problem in most cases is acknowledging there’s a problem at all. @oscarjiminy @brianonbarrington @Wolven I think the paradigmatic shift needed is to not think that throwing mathematics at things necessarily helps. And to regulate the ways in which risk calculating businesses are allowed to take and spread their risks. Set boundary conditions for their optimization problems such that their solutions generate a sane outcome when agregated. @TorbjornBjorkman @brianonbarrington @Wolven Ok but that brings us back to excluding the tool altogether. I don’t object to regulation but I doubt that’s likely in the US for eg anytime soon. @oscarjiminy @brianonbarrington @Wolven It could well be that it won't be very soon, agreed. But I still think that path will work sooner than waiting for US financial institutions to spontaneously develop sound thinking about the various risks they take on behalf of all of society. @oscarjiminy @TorbjornBjorkman @Wolven That’s the big problem with FICO. Back during the Great Financial Crisis there was a reverse correlation between owners surrendering their homes to mortgage holders and FICO. Deep underwater borrowers with high FICOs were more likely to do the financially rational thing and walk away from their houses… not exactly what the predictive model intended. Now there are so many versions that it’s rather a joke. @TorbjornBjorkman @Wolven Oh without question, old data will skew. But that’s how AI “learns.” Without a data set of “what to do,” it struggles to develop an outcome. I’m not an AI expert but at the time, “greenfield AI for underwriting” was science fiction. It probably still is. The most interesting innovation I’m aware of in the space was what SoFi did with student loans. |
Gregory's Server
@TorbjornBjorkman @brianonbarrington @Wolven How else should an AI/algorithm be trained? If not on existing datasets (the larger the better) then how?
The cost of orienting a tool like this to an otherwise human task would likely be enormous, no? And also likely wouldn’t solve the problem.
The discrepancy is knowledge/ understanding of human bias which requires awareness/acknowledgement of bias.
How easy is it to assemble a team of engineers who are versed in such things? A team skilled in countering such things might well require fundamentally diverse background and experience but how does that kind of approach square with a typical management team or, indeed the culture more broadly.
Seems a lot like a paradigmatic shift is required.
@TorbjornBjorkman @brianonbarrington @Wolven How else should an AI/algorithm be trained? If not on existing datasets (the larger the better) then how?
The cost of orienting a tool like this to an otherwise human task would likely be enormous, no? And also likely wouldn’t solve the problem.
The discrepancy is knowledge/ understanding of human bias which requires awareness/acknowledgement of bias.