Connect with us

FACEBOOK

Nick Clegg admits that Facebook’s fact checker may be politically biased

Published

on

Facebook Vice President Nick Clegg said the company’s fact checkers hired to eradicate “fake news” suspicions from social media platforms could be biased and pursue its own political agenda. Acknowledged, according to a European Commission document obtained by email on Sunday.

The former Deputy Prime Minister admitted to EU authorities in November during a discussion about how the tech giant was working on the false information flooding the site.

Facebook announced fact-checking measures in 2016, claiming that its failure to remove misleading content helped support Donald Trump in the US presidential election that year.

Many have praised the steps that allow users to warn Facebook about content that appears to be fake.

Facebook Vice President Nick Clegg said the company’s fact checkers hired to eradicate “fake news” suspicions from social media platforms could be biased and pursue its own political agenda. Acknowledged, according to a European Commission document obtained by email on Sunday

Advertisement
free widgets for website

The former Deputy Prime Minister admitted to EU authorities in November during a discussion about how the tech giant was working on the false information flooding the site. Facebook announced fact-checking measures in 2016, claiming that its failure to remove misleading content helped to favor Donald Trump in the US presidential election that year.

But critics blamed the intervention, Facebook relied on a left-wing fact checker, and warned that the project would be a “disaster.”

Since then, Facebook has been accused of curbing legitimate talk and curbing public debate.

Advertisement
free widgets for website

Over the last few weeks, there has been a great deal of controversy over the decision to censor a story that claims that Covid-19 is artificial and may have leaked from the Wuhan Institute.

See also  'La Pulga' Facebook Groups, a growing tool for local small businesses

For months, the warning label was removed or affixed to an article suggesting that the virus originated from a Chinese laboratory, until President Joe Biden overturned the decision when he ordered an investigation into the allegations last month.

Under the fact-checking scheme, Facebook uses a network of 80 organizations around the world, including three in the United Kingdom, to flag false information on the platform.

Stories that are considered false or misleading are not removed from the site, but are flagged by the user through a series of warning labels. Facebook’s sophisticated algorithms can push such stories far below the site, so few people will see them again.

Now, the minutes of the meeting between Mr. Craig and the power broker in Brussels reveal how he questioned the ability of fact checkers to make fair decisions.

Advertisement
free widgets for website

Over the last few weeks, there has been a great deal of controversy over Facebook’s decision to censor stories claiming that Covid-19 is artificial and may have leaked from the Wuhan Institute. For months, the warning label was removed or affixed to an article suggesting that the virus originated from a Chinese laboratory, until President Joe Biden overturned the decision when he ordered the investigation of the complaint last month.

The document shows that Craig and Vice-President of the European Commission, Bella Jouroba, discussed how Facebook countered disinformation in the 2020 US presidential election two weeks ago.

However, the minutes are added as follows. [Mr Clegg] He also emphasized that independent fact checkers are not always objective because they have their own agenda.

See also  Facebook suspends Saad Rafique's account for sharing Burhan Wani's photo

Former Cabinet Minister David Jones said he was “extremely worried” about Mr. Craig’s comment.

Advertisement
free widgets for website

He added: ‘Enrollment completely destroys the credibility of Facebook’s own procedures. It does not provide the right to appeal to news organizations when it censors them, even if they act on the advice of fact checkers motivated by “their own agenda.”

Facebook said last night:’Nick never suggested that our fact-checking program was prejudiced. He explained that one of the benefits of having different independent fact-checking partners is the different disciplines of different countries and the areas of problem they bring.

Facebook began issuing fact-checking warnings to the story about potential leaks in the lab at the start of the pandemic. Then in February, the tech giant announced that it would remove the “Facebook and Instagram false allegations” suggesting that the Covid-19 was artificial or manufactured. (Above, 2019 Craig and Facebook CEO Mark Zuckerberg)

Craig’s comment raises concerns that Facebook is shutting out public debate.

Advertisement
free widgets for website

Facebook began issuing fact-checking warnings to the story about potential leaks in the lab at the start of the pandemic. Then in February, the tech giant announced that it would remove the “Facebook and Instagram false allegations” suggesting that the Covid-19 was artificial or manufactured.

Some of the articles labeled “False Information” were written by award-winning MoS journalist Ian Birrell on the UnHerd website. The tech giant later apologized for the “mistake.”

In March, Facebook put a warning label on an article about herd immunity written by a US surgeon in The Wall Street Journal.

See also  Facebook (FB) Says It Has Spent $13 Billion on Safety, Security - Bloomberg

An opinion piece by Dr. Martin McCali, a professor at Johns Hopkins University in Baltimore, predicted that Covid-19 would “nearly disappear by April” in the United States.

Facebook has added a “missing context” label to Dr. Makary’s work as a result of a study by one of the third-party fact checkers, Health Feedback. “Independent fact checkers say this information can be misleading,” the label added.

Advertisement
free widgets for website

The ferocious Wall Street Journal has accused Facebook of being a “fact-checking dissenting opinion.” Dr. Macari said he made predictions rather than assertions, and Facebook was doing “cherry-picking” research “to support their opinion.”

Facebook said: “If someone feels that the fact check is inappropriate, they can sue it, and the fact checker has the discretion to change the label if there is a benefit.”

Source link Nick Clegg admits that Facebook’s fact checker may be politically biased

Read More

Advertisement
free widgets for website
Continue Reading
Advertisement free widgets for website
Click to comment

Leave a Reply

Your email address will not be published.

FACEBOOK

Resources for Completing App Store Data Practice Questionnaires for Apps That Include the Facebook or Audience Network SDK

Published

on

By

resources-for-completing-app-store-data-practice-questionnaires-for-apps-that-include-the-facebook-or-audience-network-sdk

Resources for Completing App Store Data Practice Questionnaires for Apps That Include the Facebook or Audience Network SDK

First seen at developers.facebook.com

See also  Meta, formerly called Facebook, reportedly working on smartwatch - USA Today
Continue Reading

FACEBOOK

Resources for Completing App Store Data Practice Questionnaires for Apps That Include the Facebook or Audience Network SDK

Published

on

By

resources-for-completing-app-store-data-practice-questionnaires-for-apps-that-include-the-facebook-or-audience-network-sdk

Updated July 18: Developers and advertising partners may be required to share information on their app’s privacy practices in third party app stores, such as Google Play and the Apple App Store, including the functionality of SDKs provided by Meta. To help make it easier for you to complete these requirements, we have consolidated information that explains our data collection practices for the Facebook and Audience Network SDKs.

Facebook SDK

To provide functionality within the Facebook SDK, we may receive and process certain contact, location, identifier, and device information associated with Facebook users and their use of your application. The information we receive depends on what SDK features 3rd party applications use and we have structured the document below according to these features.

App Ads, Facebook Analytics, & App Events

Facebook App Events allow you to measure the performance of your app using Facebook Analytics, measure conversions associated with Facebook ads, and build audiences to acquire new users as well as re-engage existing users. There are a number of different ways your app can use app events to keep track of when people take specific actions such as installing your app or completing a purchase.

With Facebook SDK, there are app events that are automatically logged (app installs, app launches, and in-app purchases) and collected for Facebook Analytics unless you disable automatic event logging. Developers determine what events to send to Facebook from a list of standard events, or via a custom event.

When developers send Facebook custom events, these events could include data types outside of standard events. Developers control sending these events to Facebook either directly via application code or in Events Manager for codeless app events. Developers can review their code and Events Manager to determine which data types they are sending to Facebook. It’s the developer’s responsibility to ensure this is reflected in their application’s privacy policy.

Advertisement
free widgets for website

Advanced Matching

Developers may also send us additional user contact information in code, or via the Events Manager. Advanced matching functionality may use the following data, if sent:

  • email address, name, phone number, physical address (city, state or province, zip or postal code and country), gender, and date of birth.
See also  Missing out on cancel culture and Facebook Jail

Facebook Login

There are two scenarios for applications that use Facebook Login via the Facebook SDK: Authenticated Sign Up or Sign In, and User Data Access via Permissions. For authentication, a unique, app-specific identifier tied to a user’s Facebook Account enables the user to sign in to your app. For Data Access, a user must explicitly grant your app permission to access data.

Note: Since Facebook Login is part of the Facebook SDK, we may collect other information referenced here when you use Facebook Login, depending on your settings.

Device Information

We may also receive and process the following information if your app is integrated with the Facebook SDK:

  • Device identifiers;
  • Device attributes, such as device model and screen dimensions, CPU core, storage size, SDK version, OS and app versions, and app package name; and
  • Networking information, such as the name of the mobile operator or ISP, language, time zone, and IP address.

Audience Network SDK

We may receive and process the following information when you use the Audience Network SDK to integrate Audience Network ads in your app:

  • Device identifiers;
  • Device attributes, such as device model and screen dimensions, operating system, mediation platform and SDK versions; and
  • Ad performance information, such as impressions, clicks, placement, and viewability.

First seen at developers.facebook.com

Continue Reading

FACEBOOK

Enabling Faster Python Authoring With Wasabi

Published

on

By

enabling-faster-python-authoring-with-wasabi

This article was written by Omer Dunay, Kun Jiang, Nachi Nagappan, Matt Bridges and Karim Nakad.


Motivation

At Meta, Python is one of the most used programming languages in terms of both lines of code and number of users. Everyday, we have thousands of developers working with Python to launch new features, fix bugs and develop the most sophisticated machine learning models. As such, it is important to ensure that our Python developers are productive and efficient by giving them state-of-the-art tools.

Introducing Wasabi

Today we introduce Wasabi, a Python language service that implements the language server protocol (LSP) and is designed to help our developers use Python easier and faster. Wasabi assists our developers to write Python code with a series of advanced features, including:

  • Lints and diagnostics: These are available as the user types.
  • Auto import quick fix: This is available for undefined-variable lint.
  • Global symbols autocomplete: When a user types a prefix, all symbols (e.g. function names, class names) that are defined in other files and start with that prefix will appear in the autocomplete suggestion automatically.
  • Organize Imports + Remove unused: A quick fix that removes all unused imports and reformats the import section according to pep8 rules. This feature is powered by other tools that are built inside Meta such as libCST that helps with safe code refactoring.
  • Python snippets: Snippet suggestions are available as the user types for common code patterns.

Additionally, Wasabi is a surface-agnostic service that can be deployed into multiple code repositories and various development environments (e.g., VSCode, Bento Notebook). Since its debut, Wasabi has been adopted by tens of thousands of Python users at Meta across Facebook, Instagram, Infrastructure teams and many more.

Figure 1: Example for global symbols autocomplete, one of Wasabi’s features

Language Services at Meta Scale

A major design requirement for language services is low latency / user responsiveness. Autocomplete suggestions, lints and quickFixes should appear to the developer immediately as they type.

Advertisement
free widgets for website

At Meta, code is organized in a monorepo, meaning that developers have access to all python files as they develop. This approach has major advantages for the developer workflow including better discoverability, transparency, easier to share libraries and increased collaboration between teams. It also introduces unique challenges for building developer tools such as language services that need to handle hundreds of thousands of files.

See also  Facebook appoints Spoorthi Priya as grievance officer for India amid row over new IT rules

The scaling problem is one of the reasons that we tried to avoid using off-the-shelf language services available in the industry (e.g., pyright, jedi) to perform those operations. Most of those tools were built in the mindset of a relatively small to medium workspace of projects, maybe with the assumptions of thousands of files for large projects for operations that require o(repo) information.

For example, consider the “auto import” quick fix for undefined variables. In order to suggest all available symbols the language server needs to read all source files, the quick fix parses them and keeps an in-memory cache of all parsed symbols in order to respond to requests.

While this may scale to be performed in a single process on the development machine for small-medium repositories, this approach doesn’t scale in the monorepo use case. Reading and parsing hundreds of thousands of files can take many minutes, which means slow startup times and frustrated developers. Moving to an in-memory cache might help latency, but also may not fit in a single machine’s memory.

For example, assume an average python file takes roughly 10ms to be parsed and to extract symbols in a standard error recoverable parser. This means that on 1000 files it can take 10 seconds to initialize which is a fairly reasonable startup time. Running it on 1M files would take 166 minutes which is obviously a too lengthy startup time.

Advertisement
free widgets for website

How Wasabi Works

Offline + Online Processing:

In order to support low latency in Meta scale repositories, Wasabi is powered by two phases of parsing, background processing (offline) done by an external indexers, and local processing of locally changed “dirty files” (online):

  1. A background process indexes all committed source files and maintains the parsed symbols in a special database (glean) that is designed for storing code symbol information.
  2. Wasabi, which is a local process running on the user machine, calculates the delta between the base revision, stack of diffs and uncommitted changes that the user currently has, and extracts symbols only out of those “dirty” files. Since this set of “dirty” files is relatively small, the operation is performed very fast.
  3. Upon an LSP request such as auto import, Wasabi parses the abstract syntax tree (AST) of the file, then based on the context of the cursor, creates a query for both glean and local changes symbols, merges the results and returns it to the user.
See also  Facebook Could Face an SEC Problem Next. Here's What We Know. | Barron's

As a result, all Wasabi features are low latency and available to the user seamlessly as they type.

Note: Wasabi currently doesn’t handle the potential delta between the revision that glean indexed (happens once every few hours) and the locally base revision that the user currently has. We plan on adding that in the future.

Figure 2: Wasabi’s high level architecture

Ranking the Results

In some cases, due to the scale of the repository, there may be many valid suggestions in the set of results. For example, consider “auto import” suggestions for the “utils” symbol. There may be many modules that define a class named “utils” across the repository, therefore we invest in ranking the results to ensure that users see the most relevant suggestions on the top.

Advertisement
free widgets for website

For example, auto import ranking is done by taking into account:

  • Locality:
    • The distance of the suggested module directory path from the directory paths of modules that are already imported in this file.
    • The distance of the suggested module directory path from the current directory path of the local file.
    • Whether the file has been locally changed (“dirty” files are ranked higher).
  • Usage: The number of occurrences the import statement was used by other files in the repository.

To measure our success, we measured the index in the suggestion list of an accepted suggestion and noted that in almost all cases the accepted suggestion was ranked in one of top 3 suggestions.

Positive feedbacks from developers

After launching Wasabi to several pilot runs inside Meta, we have received numerous positive feedbacks from our developers. Here is one example of the quote from a software engineer at Instagram:

“I’ve been using Wasabi for a couple months now, it’s been a boon to my productivity! Working in Instagram Server, especially on larger files, warnings from pyre are fairly slow. With Wasabi, they’re lightning fast 😃!”

“I use features like spelling errors and auto import several times an hour. This probably makes my development workflow 10% faster on average (rough guess, might be more, definitely not less), a pretty huge improvement!”

As noted above, Wasabi has made a meaningful change to keep our developers productive and make them feel delightful.

Advertisement
free widgets for website

The metric to measure authoring velocity

In order to quantitatively understand how much value Wasabi has delivered to our Python developers, we have considered a number of metrics to measure its impact. Ultimately, we landed on a metric that we call ‘Authoring Velocity’ to measure how fast developers write code. In essence, Authoring Velocity is the inverse function of the time taken on a specific diff (a collection of code changes) during the authoring stage. The authoring stage starts from the timestamp when a developer checks out from the source control repo to the timestamp when the diff is created. We have also normalized it against the number of lines of code changed in the diff, as a proxy for diff size, to offset any possible variance. The greater the value for ‘Authoring Velocity,’ the faster we think developers write their code.

See also  Facebook blocks Hamas-affiliated news agency based in Gaza Strip

Figure 3: Authoring Velocity Metric Formula

The result

With the metric defined, we ran an experiment to measure the difference that Wasabi brings to our developers. Specifically, we selected ~700 developers who had never used Wasabi before, and then randomly put them into two independent groups at a 50:50 split ratio. For these developers in the test group, they were enabled with Wasabi when they wrote in Python, whereas there was no change for those in the control group. For both groups, we compare the changes in relative metric values before and after the Wasabi enablement. From our results, we find that for developers in the test group, the median value of authoring velocity has increased by 20% after they started using Wasabi. Meanwhile, we don’t see any significant change in the control group before and after, which is expected.

Figure 4: Authoring Velocity measurements for control and test groups, before and after Wasabi was rolled out to the test group.

Summary

With Python’s unprecedented growth, it is an exciting time to be working in the area to make it better and handy to use. Together with its advanced features, Wasabi has successfully improved developers’ productivity at Meta, allowing them to write Python faster and easier with a positive developer experience. We hope that our prototype and findings can benefit more people in the broader Python community.

Advertisement
free widgets for website

To learn more about Meta Open Source, visit our open source site, subscribe to our YouTube channel, or follow us on Twitter, Facebook and LinkedIn.

First seen at developers.facebook.com

Continue Reading

Trending