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AI ethics research is funny. It's obviously important but it's also kind of ... obvious. I am surprised they get paid so much. * Lots of processing uses more e

by eecks 6y ago

AI ethics research is funny. It's obviously important but it's also kind of ... obvious. I am surprised they get paid so much. * Lots of processing uses more energy.. * Large amounts of data might contain bad data (garbage in, garbage out) * Wealthy countries/communities have the most 'content' online so less wealthy countries/communities will be unrepresented. here's some more: * AI being forced to chose between two "bad" scenarios will result in an unfavorable outcome for one party * AI could reveal truths people don't want to hear e.g. it might say the best team for a project is an all white male team between 25 - 30 rather than a more diverse team. It might say that a 'perfect' society needs a homogeneous population. * AI could disrupt a lot of lower paid jobs first without governments having proper supports and retraining structures in place

ur-whale 6y ago
>AI ethics research is funny. Calling "AI ethics" research makes it feel like it's actual science. It's in practice much closer to "social science" than actual science. And just like "social science" it isn't funny at all but rather a giant hoax.
- flybrand 6y ago
  It almost reads like ‘Ethics Philosophy applied to AI’
- andybak 6y ago
  As much as I agree social "science" is often flawed, your flippant dismissal of an entire field with an extensive pedigree strikes me as unlikely to come from a place of knowledge and familiarity.
- UncleMeat 6y ago
  She's got a PhD in CS from Stanford. She is published in CS conferences. Even if you want to shit on the social sciences for some reason, it is clear that this work is computer science.
- oblio 6y ago
  The most important problems in life are ill-defined. We still need to work at solving them, with the limited means we have. If we'd only work with hard sciences, life would never get better. Many things in our lives have advanced based on what could be considered guesses (for example capitalism doesn't really have a rigorously defined mathematical model, yes, they are some hand-wavy explanations but nothing you'd put in a solid math or physics paper).
qsort 6y ago
Playing the devil's advocate here, I'm with you that half of AI ethics is obvious and the other half is wrong, but is't it the goal of the field to try and give meaning to things that aren't obviously well defined? To make an example that's en vogue right now, AI explainability. Nobody even has a definition of what it means for a model to be explainable (is a linear regression "more explainable" than ML? isn't Google search far less explainable than any model of anything ever?), but a reasonable framework for that concept could certainly be interesting. Obviously, serious frameworks are done with definitions and math, not with words and storytelling, but all the air around those things seems to me more the fault of politicians, crap journalists and freaking idiots on social media (the current term is 'influencer', I reckon) rather than an issue with the field itself.
- ionwake 6y ago
  Sorry to ask, but I don't understand the points you are making. Could you please elaborate on them? I had trouble with the last 2 paragraphs. Thanks
  - qsort 6y ago
    AI explainability is a concept that's being thrown around frequently these days; it revolves around the idea that machine learning models should be "explainable", that is, their predictions should be traceable not just to the mathematical operations that define it, but also to some properties of the input which should be understandable by a human. While I won't deny that the concept is interesting, it's terribly difficult to understand what it would translate to, technically speaking. It's true that machine learning models make predictions that are hard to check (and sometimes even understand), but they aren't inherently "less explainable" than even the simplest statistical models, like linear regressions. For example, it's pretty weird to be angry that "ML is not explainable" but to be okay with things like Google search that have literally zero transparency. My main problem with it is that people with poor understanding of computer science and math in general - let alone machine learning - throw around the term like it's obvious what they mean, when their real goal is generating social media clout in the case of journalists and influencers, or, more darkly, to enforce political control of the industry in the case of politicians.
derangedHorse 6y ago
It's always obvious in hindsight, much like what can be said of something like Dijkstra's algorithm, but the fact of the matter is not everyone can spend the energy to both understand the context of the problem and direct their attention towards the evaluation of ethics within that context. Some people even find it hard to understand the situations of others enough to identify where their technologies can be used for harm. The problem that AI ethics research addresses are ethical problems that executives and employees aren't paying attention to. It may seem obvious when stated explicitly because of the amount of ease it takes to grasp the concepts (and the seemingly simple derivation of cause and effect relationships), but I assure you it is not obvious to a lot of people I know in the field at least. There's also a clear misunderstanding of what ethics research should entail: * AI being forced to chose between two "bad" scenarios will result in an unfavorable outcome for one party This is a trivial result that doesn't hold much value as a standalone observation and probably wouldn't be touted as a research point in a respectable publication of AI ethics. * AI could reveal truths people don't want to hear e.g. it might say the best team for a project is an all white male team between 25 - 30 rather than a more diverse team. It might say that a 'perfect' society needs a homogeneous population. The fact that you made this comment may be a cause for an ethics discussion in itself. You used "truths" to describe the statement "the best team for a project is an all white male team between 25 - 30 rather than a more diverse team." This shows a disregard for the reality that most data is contextualized and biased. Using terms like "best team" and "more diverse team" make the statement like the one you made at risk for having took a misguided conclusion from data. Maybe the following revised statement would be closer to what we can call a contextualized "truth" generated by an ML model: "Teams that comprise of white males between 25 - 30 have a statistically larger chance of meeting milestones set by leadership rather than teams with one or more non-white male." Even statements like that aren't complete as my definition of "meeting milestones" could be sourced from self-reported data (in which case it could mean that white males just self-report more milestone completions). * AI could disrupt a lot of lower paid jobs first without governments having proper supports and retraining structures in place A problem to consider, sure, but this is one of the more popular observations and has been echoed over time within the context of technological advancements in general.
- eecks 6y ago
slim 6y ago
maybe there's less obvious and more interesting findings in the paper that the blog post author have chosen to ignore.

by eecks 6y ago

ur-whale 6y ago

>AI ethics research is funny. Calling "AI ethics" research makes it feel like it's actual science. It's in practice much closer to "social science" than actual science. And just like "social science" it isn't funny at all but rather a giant hoax.

flybrand 6y ago
It almost reads like ‘Ethics Philosophy applied to AI’
andybak 6y ago
As much as I agree social "science" is often flawed, your flippant dismissal of an entire field with an extensive pedigree strikes me as unlikely to come from a place of knowledge and familiarity.
UncleMeat 6y ago
She's got a PhD in CS from Stanford. She is published in CS conferences. Even if you want to shit on the social sciences for some reason, it is clear that this work is computer science.
oblio 6y ago
The most important problems in life are ill-defined. We still need to work at solving them, with the limited means we have. If we'd only work with hard sciences, life would never get better. Many things in our lives have advanced based on what could be considered guesses (for example capitalism doesn't really have a rigorously defined mathematical model, yes, they are some hand-wavy explanations but nothing you'd put in a solid math or physics paper).

qsort 6y ago

Playing the devil's advocate here, I'm with you that half of AI ethics is obvious and the other half is wrong, but is't it the goal of the field to try and give meaning to things that aren't obviously well defined? To make an example that's en vogue right now, AI explainability. Nobody even has a definition of what it means for a model to be explainable (is a linear regression "more explainable" than ML? isn't Google search far less explainable than any model of anything ever?), but a reasonable framework for that concept could certainly be interesting. Obviously, serious frameworks are done with definitions and math, not with words and storytelling, but all the air around those things seems to me more the fault of politicians, crap journalists and freaking idiots on social media (the current term is 'influencer', I reckon) rather than an issue with the field itself.

ionwake 6y ago
Sorry to ask, but I don't understand the points you are making. Could you please elaborate on them? I had trouble with the last 2 paragraphs. Thanks
- qsort 6y ago
  AI explainability is a concept that's being thrown around frequently these days; it revolves around the idea that machine learning models should be "explainable", that is, their predictions should be traceable not just to the mathematical operations that define it, but also to some properties of the input which should be understandable by a human. While I won't deny that the concept is interesting, it's terribly difficult to understand what it would translate to, technically speaking. It's true that machine learning models make predictions that are hard to check (and sometimes even understand), but they aren't inherently "less explainable" than even the simplest statistical models, like linear regressions. For example, it's pretty weird to be angry that "ML is not explainable" but to be okay with things like Google search that have literally zero transparency. My main problem with it is that people with poor understanding of computer science and math in general - let alone machine learning - throw around the term like it's obvious what they mean, when their real goal is generating social media clout in the case of journalists and influencers, or, more darkly, to enforce political control of the industry in the case of politicians.

derangedHorse 6y ago

It's always obvious in hindsight, much like what can be said of something like Dijkstra's algorithm, but the fact of the matter is not everyone can spend the energy to both understand the context of the problem and direct their attention towards the evaluation of ethics within that context. Some people even find it hard to understand the situations of others enough to identify where their technologies can be used for harm. The problem that AI ethics research addresses are ethical problems that executives and employees aren't paying attention to. It may seem obvious when stated explicitly because of the amount of ease it takes to grasp the concepts (and the seemingly simple derivation of cause and effect relationships), but I assure you it is not obvious to a lot of people I know in the field at least. There's also a clear misunderstanding of what ethics research should entail: * AI being forced to chose between two "bad" scenarios will result in an unfavorable outcome for one party This is a trivial result that doesn't hold much value as a standalone observation and probably wouldn't be touted as a research point in a respectable publication of AI ethics. * AI could reveal truths people don't want to hear e.g. it might say the best team for a project is an all white male team between 25 - 30 rather than a more diverse team. It might say that a 'perfect' society needs a homogeneous population. The fact that you made this comment may be a cause for an ethics discussion in itself. You used "truths" to describe the statement "the best team for a project is an all white male team between 25 - 30 rather than a more diverse team." This shows a disregard for the reality that most data is contextualized and biased. Using terms like "best team" and "more diverse team" make the statement like the one you made at risk for having took a misguided conclusion from data. Maybe the following revised statement would be closer to what we can call a contextualized "truth" generated by an ML model: "Teams that comprise of white males between 25 - 30 have a statistically larger chance of meeting milestones set by leadership rather than teams with one or more non-white male." Even statements like that aren't complete as my definition of "meeting milestones" could be sourced from self-reported data (in which case it could mean that white males just self-report more milestone completions). * AI could disrupt a lot of lower paid jobs first without governments having proper supports and retraining structures in place A problem to consider, sure, but this is one of the more popular observations and has been echoed over time within the context of technological advancements in general.

eecks 6y ago

slim 6y ago

maybe there's less obvious and more interesting findings in the paper that the blog post author have chosen to ignore.