Am glad this is highlighted now. Back in 2015, when we wanted to build an meeting scheduling bot, we naively thought we could use only machines to get the job done. 3 months later we realized that was no way feasible, not then not in the next 10 years. So the common feedback we got was to just use low cost labor in India/Phillipines to get the job done. To us, that was a no-go because we kept privacy as the top criterion for doing anything with people's inboxes. Even after obfuscation, we didn't want any human to read or parse soeone's private messages. So we dropped the idea and shut shop. Sure we failed, but at least we were true to our conscience.
Another way to have tackled this problem was to just go down the research route and build a product once the ML was fully baked, but VCs won't fund such adventures.
To get VC funding, we had to lie through our teeth, which was a total no-go. I am not bringing the righteous argument, but for once I feel really good that our viewpoints held then are corroborated now, some kind of confirmation bias kicking in.
It's great that you stuck to your guns, but there's another ethical path forward: transparency. Many companies already pay third party employees to look at their schedule, so it's not a non-starter. And there are a few VCs out there that understand training costs for AI and are willing to engage with a journey that includes them - as long as the cards are on the table.
What we need is a set of accounting metrics about the cost of training and the rate of improvement, so that VCs can get comfy with how to make projections rather than choosing between buying snake oil and nit participating in the AI Dev cycle.
PS - emphatically not taking aim here at your specific decision, OP. Startups have different opportunities and interests, and these decisions are tough. I'm sure you know better than I do whether the ethical wizard-of-oz model above is relevant to you in particular.
Thank you. The latter point about accounting metrics that you mention is very valid. I will think on these lines. I am very much interested in reaching out to VCs who are supportive and understand training costs for AI. If you know of any please do point out.
Could you expand upon the technical problems you ran into? I would have thought it possible to build a reasonably good meeting scheduling bot with just classical constrained optimization algorithms (no AI / ML), but I'm probably missing something.
Well, for starters we are looking at question answer type natural language processing, something superior than Google smart replies. So imagine a flow something like '<Bot>, schedule a meeting with <p1> at Noon tomorrow. Now the <bot> has to figure out the availability of <p1> at Noon. This is relatively simple if <p1> has given the bot access to the calendar but that's unlikely. So the bot has to start a series of dialogs which to figure out the availability. Now you are looking at NLP + some state maintenance and it gets even more trickier if you have more than one participant, if there are no available overlaps, if the bot has to force a schedule which is dictated by the boss. Using a phone app you can solve some of these by prompting custom keys but then you run into app distribution challenges.
Another way to have tackled this problem was to just go down the research route and build a product once the ML was fully baked, but VCs won't fund such adventures.
To get VC funding, we had to lie through our teeth, which was a total no-go. I am not bringing the righteous argument, but for once I feel really good that our viewpoints held then are corroborated now, some kind of confirmation bias kicking in.