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During the application, they break down what they mean by "responsible AI" to mean: > Agency: Is your AI is designed with personal agency in mind? Do people ha

During the application, they break down what they mean by "responsible AI" to mean: > Agency: Is your AI is designed with personal agency in mind? Do people have control over how they use the AI, over how their data is used, and over the algorithm’s output? > Accountability: Are you providing transparency into how your AI systems work, are you set up to support accountability when things go wrong? > Privacy: How are you collecting, storing and sharing people’s data? > Fairness: Are your computational models, data, and frameworks reflecting or amplifying existing bias, or assumptions resulting in biased or discriminatory outcomes, or have outsized impact on marginalized communities. Are computing and human labor used to build your AI system vulnerable to exploitation and overwork? Is the climate crisis being accelerated by your AI through energy consumption or speeding up the extraction of natural resources. > Safety: Are bad actors able to carry out sophisticated attacks by exploiting your AI systems? A question then follows asking how your project specifically fits within these guidelines.

boringuser2 4y ago
This statement is incredibly biased -- dripping with it: "Are your computational models, data, and frameworks reflecting or amplifying existing bias, or assumptions resulting in biased or discriminatory outcomes, or have outsized impact on marginalized communities. Are computing and human labor used to build your AI system vulnerable to exploitation and overwork? Is the climate crisis being accelerated by your AI through energy consumption or speeding up the extraction of natural resources."
- sgift 4y ago
  > This statement is incredibly biased That is correct. But the question is: Why is that a problem? Biased against exploitation and overwork is good. Biased against accelerating the climate crisis is good. Biased against discrimination is good. I fail to see which of these biases is bad here.
  - version_five 4y ago
    When you clearly indicate you're not neutral, you lose all credibility. Nobody wants a ML model that gives them the climate warrior version of the "truth". Neutrality is extremely important in order to be broadly taken seriously. It's exactly the kind of criticism that's been leveled against chatGPT
    - kokanee 4y ago
      Ah yes, I'll just reference the list of my non-neutral biases as I choose the moderation rules for my AI. My bias against swear words is neutral, so I will include that rule, but my bias against pollution is not neutral, so I will skip those moderation rules. Obviously categorizing beliefs into "neutral" and "not neutral" is impossible. Your statement is a classic example of the false consensus effect -- everyone thinks they are neutral. https://en.wikipedia.org/wiki/False_consensus_effect https://en.wikipedia.org/wiki/False_consensus_effect
      - haberman 4y ago
        > Obviously categorizing beliefs into "neutral" and "not neutral" is impossible. There is one distinction that matters a lot, and that is empirical vs. moral. Also known as vs. ought (https://en.wikipedia.org/wiki/Is%E2%80%93ought_problem https://en.wikipedia.org/wiki/Is%E2%80%93ought_problem). When exploring empirical claims, one can aspire for neutrality. Even if perfect neutrality is not possible, it's certainly possible to do better or worse at it. Moral claims cannot really be evaluated neutrally, I agree. But an AI striving for neutrality could attempt to avoid taking sides on contentious issues.
    - notahacker 4y ago
      The irony with people talking about "neutrality" is the tendency of the people making such demands to be even more obsessed with distorting the input data to produce outcomes censored to take into account their viewpoint than the 'AI safety' and PR people. I mean, how much censorship (or artificial curation) would you need to avoid an ML model giving "the climate warrior version" of questions about whether the world was getting warmer?!
- dmix 4y ago
  It’s Mozilla, what do you expect? That’s their whole schtick these days.
- jamilton 4y ago
  The other statements are biased too, but they're biased in favor of privacy and transparency. Being biased, here, just means having values and applying them - do you disagree with having values or the values themselves?
paulddraper 4y ago
> reflecting or amplifying existing bias, or assumptions resulting in biased or discriminatory outcomes, or have outsized impact on marginalized communities (◔_◔)
thomastjeffery 4y ago
Are we talking about algorithms or AI? An algorithm is a set of predetermined logic. The thing itself does not "make decisions", it applies decisions that were already made by writing it. An AI is an Artificial Intelligence. A non-human thinker. Something that can "make decisions". Such a thing does not exist. --- This is the problem with "AI research". It's sensible as a category of pursuit, but until you have accomplished that pursuit, there literally does not exist a single instance of an AI. Somehow, that distinction has been ignored from the word "go". Every project in the pursuit of AI is itself already called an AI! No wonder people are so confused! It's plain to see that all of this fear and uncertainty could be cleared up with a simple change in nomenclature. Stop calling projects "AI", and we can all move on from this silly debate.
- Avicebron 4y ago
  I fully second this sentiment. But we know people will fight tooth and nail over the gilding that masks their normalcy.

boringuser2 4y ago

This statement is incredibly biased -- dripping with it: "Are your computational models, data, and frameworks reflecting or amplifying existing bias, or assumptions resulting in biased or discriminatory outcomes, or have outsized impact on marginalized communities. Are computing and human labor used to build your AI system vulnerable to exploitation and overwork? Is the climate crisis being accelerated by your AI through energy consumption or speeding up the extraction of natural resources."

sgift 4y ago
> This statement is incredibly biased That is correct. But the question is: Why is that a problem? Biased against exploitation and overwork is good. Biased against accelerating the climate crisis is good. Biased against discrimination is good. I fail to see which of these biases is bad here.
- version_five 4y ago
  When you clearly indicate you're not neutral, you lose all credibility. Nobody wants a ML model that gives them the climate warrior version of the "truth". Neutrality is extremely important in order to be broadly taken seriously. It's exactly the kind of criticism that's been leveled against chatGPT
  - kokanee 4y ago
    Ah yes, I'll just reference the list of my non-neutral biases as I choose the moderation rules for my AI. My bias against swear words is neutral, so I will include that rule, but my bias against pollution is not neutral, so I will skip those moderation rules. Obviously categorizing beliefs into "neutral" and "not neutral" is impossible. Your statement is a classic example of the false consensus effect -- everyone thinks they are neutral. https://en.wikipedia.org/wiki/False_consensus_effect https://en.wikipedia.org/wiki/False_consensus_effect
    - haberman 4y ago
      > Obviously categorizing beliefs into "neutral" and "not neutral" is impossible. There is one distinction that matters a lot, and that is empirical vs. moral. Also known as vs. ought (https://en.wikipedia.org/wiki/Is%E2%80%93ought_problem https://en.wikipedia.org/wiki/Is%E2%80%93ought_problem). When exploring empirical claims, one can aspire for neutrality. Even if perfect neutrality is not possible, it's certainly possible to do better or worse at it. Moral claims cannot really be evaluated neutrally, I agree. But an AI striving for neutrality could attempt to avoid taking sides on contentious issues.
  - notahacker 4y ago
    The irony with people talking about "neutrality" is the tendency of the people making such demands to be even more obsessed with distorting the input data to produce outcomes censored to take into account their viewpoint than the 'AI safety' and PR people. I mean, how much censorship (or artificial curation) would you need to avoid an ML model giving "the climate warrior version" of questions about whether the world was getting warmer?!
dmix 4y ago
It’s Mozilla, what do you expect? That’s their whole schtick these days.
jamilton 4y ago
The other statements are biased too, but they're biased in favor of privacy and transparency. Being biased, here, just means having values and applying them - do you disagree with having values or the values themselves?

paulddraper 4y ago

> reflecting or amplifying existing bias, or assumptions resulting in biased or discriminatory outcomes, or have outsized impact on marginalized communities (◔_◔)

thomastjeffery 4y ago

Are we talking about algorithms or AI? An algorithm is a set of predetermined logic. The thing itself does not "make decisions", it applies decisions that were already made by writing it. An AI is an Artificial Intelligence. A non-human thinker. Something that can "make decisions". Such a thing does not exist. --- This is the problem with "AI research". It's sensible as a category of pursuit, but until you have accomplished that pursuit, there literally does not exist a single instance of an AI. Somehow, that distinction has been ignored from the word "go". Every project in the pursuit of AI is itself already called an AI! No wonder people are so confused! It's plain to see that all of this fear and uncertainty could be cleared up with a simple change in nomenclature. Stop calling projects "AI", and we can all move on from this silly debate.

Avicebron 4y ago
I fully second this sentiment. But we know people will fight tooth and nail over the gilding that masks their normalcy.