@mcCronjaeger@BloombergME The list is much too long for a Twitter thread.
I'll leave that for FB's comm people to do.
@mcCronjaeger@BloombergME More importantly, the whole premise of the article is wrong.
The SAIL / Responsible AI group's role *never* was to deal with hate speech and misinformation.
That's in the hands of other groups with *hundreds* of people in them.
In fact, "integrity" involves over 30,000 people...
@mcCronjaeger@BloombergME So the central theme of the article, that RespAI wasn't given the necessary resources to do its job is patently false.
Second, AI is heavily used for content moderation: filtering hate speech, polarizing content, violence, bullying, etc...
@mcCronjaeger@BloombergME The idea that these things are not taken down is false.
For example, 94.7% of hate speech was taken down preemptively by AI systems in Q4 2020.
The rest was taken down after user flagging and manual review....
@mcCronjaeger@BloombergME Then there are independent academic studies that show that the growth of polarization in the US (and Canada) has been happening for the last 4 decades or so and is *not* correlated with social network usage. pnas.org/content/114/40…
@mcCronjaeger@BloombergME What's more, other OECD countries have seen a *decrease* of polarization over the last several decades (e.g. Sweden, Norway, Germany...). The US/Canada are a special case.
This can't be explained by the use of social media. nber.org/papers/w26669
@mcCronjaeger@BloombergME Then, the idea that FB shows polarizing content because it's good for "engagement" is ridiculous.
The criteria on which Newsfeed is based were completely overhauled in late 2017/early 2018, *precisely* to minimize polarizing content, clickbait, information bubbles, etc.
@mcCronjaeger@BloombergME The one thing that's true is that FB assumed good intent from user for too long, and exposed itself to being exploited by people with nefarious intent: stoking ethnic conflicts, influencing foreing elections, etc....
@mcCronjaeger@BloombergME But once the company realized what was happening it quickly took corrective measures: protecting the integrity of elections, taking down Myanmar government pages spewing hateful propaganda, etc.
@mcCronjaeger@BloombergME The thing is, it's not easy to do all this. You need content moderator that speak all the world's languages, or else you need AI for translation and hate speech detection in all languages.
FB actually has the world's best Burmese-to-English translation system.
Guess why!
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Right:
Each colored point-cloud is a country
Each point (x,y) is 1 hour of electricity production with x=energy produced in kWh; y=CO2 emission in g/kWh.
Left:
bar graphs of the mix of production methods for select countries.
1/N
France: low overall CO2 emissions, low variance on emissions, relying essentially on nuclear energy with a bit of hydro [reminder: nuclear produce essentially no CO2].
2/N
Germany: despite having a large proportion of renewables, has high emissions and a high variance of emissions: when there is no wind nor sun, it has to rely on fossil fuel, having abandoned and phased out nuclear production.
3/N
I came first to work at Bell Labs on a J-1 visa, because I thought I'd stay only a year or two.
But I stayed longer and got an H1-B visa.
Then I got a green card.... 1/N
I hesitated to take up citizenship during the GW Bush years, waiting for the country to become respectable again.
But after Bush's re-election, I just wanted to be able to vote and kick out the neocon bastards.
So I became a citizen just in time to vote for Barack Obama.
2/N
As an immigrant, scientist, academic, liberal, atheist, and Frenchman, I am a concentrate of everything the American Right hates.
3/N
@timnitGebru If I had wanted to "reduce harms caused by ML to dataset bias", I would have said "ML systems are biased *only* when data is biased".
But I'm absolutely *not* making that reduction.
1/N
@timnitGebru I'm making the point that in the *particular* *case* of *this* *specific* *work*, the bias clearly comes from the data.
2/N
@timnitGebru There are many causes for *societal* bias in ML systems
(not talking about the more general inductive bias here). 1. the data, how it's collected and formatted. 2. the features, how they are designed 3. the architecture of the model 4. the objective function 5. how it's deployed
We often hear that AI systems must provide explanations and establish causal relationships, particularly for life-critical applications.
Yes, that can be useful. Or at least reassuring....
1/n
But sometimes people have accurate models of a phenomenon without any intuitive explanation or causation that provides an accurate picture of the situation. In many cases of physical phenomena, "explanations" contain causal loops where A causes B and B causes A.
2/n
A good example is how a wing causes lift. The computational fluid dynamics model, based on Navier-Stokes equations, works just fine. But there is no completely-accurate intuitive "explanation" of why airplanes fly.
3/n
Some folks still seem confused about what deep learning is. Here is a definition:
DL is constructing networks of parameterized functional modules & training them from examples using gradient-based optimization.... facebook.com/722677142/post…
This definition is orthogonal to the learning paradigm: reinforcement, supervised, or self-supervised.
Don't say "DL can't do X" when what you really mean is "supervised learning needs too much data to do X"....
....Extensions (dynamic networks, differentiable programming, graph NN, etc) allow the network architecture to change dynamically in a data-dependent way.