They sure will. Here's a quick analysis of how, from my perspective as someone who studies societal impacts of natural language technology:

First, some guesses about system components, based on current tech: it will include a very large language model (akin to GPT-3) trained on huge amounts of web text, including Reddit and the like.
It will also likely be trained on sample input/output pairs, where they asked crowdworkers to create the bulleted summaries for news articles.
The system will be some sort of encoder-decoder that "reads" the news article and then uses its resulting internal state to output bullet points. Likely no controls to make sure the bullet points are each grounded in specific statements in the article.
(This is sometimes called "abstractive" summarization, as opposed to "extractive", which has to use substrings of the article. Maybe they're doing the latter, but based on what the research world is all excited about right now, I'm guessing the former.)
So, in what ways will these end up racist?
1) The generator, giving the outputs, will be heavily guided by its large language model. When the input points it at topics it has racist training data for, it may spit out racist statement, even if they aren't supported by the article.
2) If sample input/output pairs will likely have been created by people who haven't done much reflecting on their own internalized racism, the kinds of things they choose to highlight will probably reflect a white gaze which the system will replicate.
[Ex: A Black person is murdered by the police. The article includes details about their family, education, hobbies, etc as well as statements by the police about possibly planted evidence. Which ends up in the summary?]
1&2 are about being racist in the sense of saying racist things. It will also likely be racist in the sense of disparate performance:
3) The system, trained mostly on mainstream/white-gaze texts, when asked to provide a summary for an article written from a BIPOC point of view, won't perform as well.
4) When the system encounters names that are infrequent in American news media, they may not be recognized as names of people, sending the generator down weird paths.
1-4 are all about system performance, but what about its impact in the world?
5) System output probably won't come with any indication of its degree of uncertainty/general level of accuracy/accuracy with the type of text being fed in. So people will pick up 'factoids' from these summaries that are wrong (and racist).
6) System output won't indicate what kinds of things usually make it into the summary, so the already racist patterns of e.g. how Black victims of police violence are described in the press as consumed by readers will get worse (see pt. 2).
I'm sure there's more, but that's my quick analysis for tonight.

More from Tech

I could create an entire twitter feed of things Facebook has tried to cover up since 2015. Where do you want to start, Mark and Sheryl? https://t.co/1trgupQEH9


Ok, here. Just one of the 236 mentions of Facebook in the under read but incredibly important interim report from Parliament. ht @CommonsCMS
https://t.co/gfhHCrOLeU


Let’s do another, this one to Senate Intel. Question: “Were you or CEO Mark Zuckerberg aware of the hiring of Joseph Chancellor?"
Answer "Facebook has over 30,000 employees. Senior management does not participate in day-today hiring decisions."


Or to @CommonsCMS: Question: "When did Mark Zuckerberg know about Cambridge Analytica?"
Answer: "He did not become aware of allegations CA may not have deleted data about FB users obtained through Dr. Kogan's app until March of 2018, when
these issues were raised in the media."


If you prefer visuals, watch this short clip after @IanCLucas rightly expresses concern about a Facebook exec failing to disclose info.
After getting good feedback on yesterday's thread on #routemobile I think it is logical to do a bit in-depth technical study. Place #twilio at center, keep #routemobile & #tanla at the periphery & see who is each placed.


This thread is inspired by one of the articles I read on the-ken about #postman API & how they are transforming & expediting software product delivery & consumption, leading to enhanced developer productivity.

We all know that #Twilio offers host of APIs that can be readily used for faster integration by anyone who wants to have communication capabilities. Before we move ahead, let's get a few things cleared out.

Can anyone build the programming capability to process payments or communication capabilities? Yes, but will they, the answer is NO. Companies prefer to consume APIs offered by likes of #Stripe #twilio #Shopify #razorpay etc.

This offers two benefits - faster time to market, of course that means no need to re-invent the wheel + not worrying of compliance around payment process or communication regulations. This makes entire ecosystem extremely agile
So we had to develop technologies like this to barely manage control over limited areas in Iraq's few urban centers. Only ~8 in 100 Iraqi adults owns a personal vehicle. That rate is > 1 car/adult in America yet I have never seen any doctrine paper or work of fiction address this


We've seen and struggled in civil conflicts with instant, local, universal, distributed communications (cell phone era, basically every conflict since 2000). We've seen and struggled in conflicts with instant, global, universal distributed communications (everything since 2011).

The world's most overfunded military and glow in the dark agencies struggle and largely fail to contain conflicts where fhe vast, vast majority of people are locked into a ~5mi radius of their home.

How can they possibly contain a conflict in a nation with universal car ownership and the most developed road network in the world? The average car can travel over 400 miles on one tank of gas, how can you contain the potential of that kind of mobility?

I think that's partially why the system was so freaked out by 1/6. Yes, most of it is histrionics but you don't decide to indefinitely turn your capital into the Baghdad Green Zone with fortifications and 25k troops over histrionics alone.

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12 TRADING SETUPS which experts are using.

These setups I found from the following 4 accounts:

1. @Pathik_Trader
2. @sourabhsiso19
3. @ITRADE191
4. @DillikiBiili

Share for the benefit of everyone.

Here are the setups from @Pathik_Trader Sir first.

1. Open Drive (Intraday Setup explained)


Bactesting results of Open Drive


2. Two Price Action setups to get good long side trade for intraday.

1. PDC Acts as Support
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Recently, the @CNIL issued a decision regarding the GDPR compliance of an unknown French adtech company named "Vectaury". It may seem like small fry, but the decision has potential wide-ranging impacts for Google, the IAB framework, and today's adtech. It's thread time! 👇

It's all in French, but if you're up for it you can read:
• Their blog post (lacks the most interesting details):
https://t.co/PHkDcOT1hy
• Their high-level legal decision: https://t.co/hwpiEvjodt
• The full notification: https://t.co/QQB7rfynha

I've read it so you needn't!

Vectaury was collecting geolocation data in order to create profiles (eg. people who often go to this or that type of shop) so as to power ad targeting. They operate through embedded SDKs and ad bidding, making them invisible to users.

The @CNIL notes that profiling based off of geolocation presents particular risks since it reveals people's movements and habits. As risky, the processing requires consent — this will be the heart of their assessment.

Interesting point: they justify the decision in part because of how many people COULD be targeted in this way (rather than how many have — though they note that too). Because it's on a phone, and many have phones, it is considered large-scale processing no matter what.