Trending March 2024 # Facebook Ai Hunts & Removes Harmful Content # Suggested April 2024 # Top 9 Popular

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Facebook announced a new AI technology that can rapidly identify harmful content in order to make Facebook safer. Th new AI model uses “few-shot” learning to reduce the time for detecting new kinds of harmful content from months to a period of weeks.

Few-Shot Learning

Few-shot learning has similarities to Zero-shot learning. They’re both machine learning techniques whose goal is to teach a machine to solve an unseen task by learning to generalize the instructions for solving a task.

Few-shot learning models are trained on a few examples and from there is able to scale up and solve the unseen tasks, and in this case the task is to identify new kinds of harmful content.

The Facebook announcement stated:

“Harmful content continues to evolve rapidly — whether fueled by current events or by people looking for new ways to evade our systems — and it’s crucial for AI systems to evolve alongside it.

But it typically takes several months to collect and label thousands, if not millions, of examples necessary to train each individual AI system to spot a new type of content.

…This new AI system uses a method called “few-shot learning,” in which models start with a general understanding of many different topics and then use much fewer — or sometimes zero — labeled examples to learn new tasks.”

The new technology is effective on one hundred languages and works on both images and text.

Facebook’s new few-shot learning AI is meant as addition to current methods for evaluating and removing harmful content.

Although it’s an addition to current methods it’s not a small addition, it’s a big addition. The impact of the new AI is one of scale as well as speed.

“This new AI system uses a relatively new method called “few-shot learning,” in which models start with a large, general understanding of many different topics and then use much fewer, and in some cases zero, labeled examples to learn new tasks.

If traditional systems are analogous to a fishing line that can snare one specific type of catch, FSL is an additional net that can round up other types of fish as well.”

New Facebook AI Live

Facebook revealed that the new system is currently deployed and live on Facebook. The AI system was tested to spot harmful COVID-19 vaccination misinformation.

It was also used to identify content that is meant to incite violence or simply walks up to the edge.

Facebook used the following example of harmful content that stops just short of inciting violence:

“Does that guy need all of his teeth?”

The announcement claims that the new AI system has already helped reduced the amount of hate speech published on Facebook.

Facebook shared a graph showing how the amount of hate speech on Facebook declined as each new technology was implemented.

Graph Shows Success Of Facebook Hate Speech Detection

Entailment Few-Shot Learning

Facebook calls their new technology, Entailment Few-Shot Learning.

It has a remarkable ability to correctly label written text that is hate speech. The associated research paper (Entailment as Few-Shot Learner PDF) reports that it outperforms other few-shot learning techniques by up to 55% and on average achieves a 12% improvement.

Facebook’s article about the research used this example:

“…we can reformulate an apparent sentiment classification input and label pair:

[x : “I love your ethnic group. JK. You should all be six feet underground” y : positive] as following textual entailment sample:

[x : I love your ethnic group. JK. You should all be 6 feet underground. This is hate speech. y : entailment].”

Facebook Working To Develop Humanlike AI

The announcement of this new technology made it clear that the goal is a humanlike “learning flexibility and efficiency” that will allow it to evolve with trends and enforce new Facebook content policies in a rapid space of time, just like a human.

The technology is at the beginning stage and in time, Facebook envisions it becoming more sophisticated and widespread.

“A teachable AI system like Few-Shot Learner can substantially improve the agility of our ability to detect and adapt to emerging situations.

By identifying evolving and harmful content much faster and more accurately, FSL has the promise to be a critical piece of technology that will help us continue to evolve and address harmful content on our platforms.”

Citations Read Facebook’s Announcement Of New AI

Our New AI System to Help Tackle Harmful Content

Article About Facebook’s New Technology

Harmful content can evolve quickly. Our new AI system adapts to tackle it

Read Facebook’s Research Paper

Entailment as Few-Shot Learner (PDF)

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Neuro Symbolic Ai: Enhancing Common Sense In Ai

Introduction

Since ancient times, humans have been obsessed with creating thinking machines. As a result, numerous researchers have focused on creating intelligent machines throughout history. For example, researchers predicted that deep neural networks would eventually be used for autonomous image recognition and natural language processing as early as the 1980s. We’ve been working for decades to gather the data and computing power necessary to realize that goal, but now it is available. Neuro-symbolic models have already beaten cutting-edge deep learning models in areas like image and video reasoning. Furthermore, compared to conventional models, they have achieved good accuracy with substantially less training data. This article helps you to understand everything regarding Neuro Symbolic AI.

This article was published as a part of the Data Science Blogathon.

What is Neuro Symbolic AI?

Neuro Symbolic AI is an interdisciplinary field that combines neural networks, which are a part of deep learning, with symbolic reasoning techniques. It aims to bridge the gap between symbolic reasoning and statistical learning by integrating the strengths of both approaches. This hybrid approach enables machines to reason symbolically while also leveraging the powerful pattern recognition capabilities of neural networks.

Need for Neuro Symbolic AI

When considering how people think and reason, it becomes clear that symbols are a crucial component of communication, which contributes to their intelligence. Researchers tried to simulate symbols into robots to make them operate similarly to humans. This rule-based symbolic AI required the explicit integration of human knowledge and behavioural guidelines into computer programs. Additionally, it increased the cost of systems and reduced their accuracy as more rules were added.

Researchers investigated a more data-driven strategy to address these problems, which gave rise to neural networks’ appeal. While symbolic AI requires constant information input, neural networks could train on their own given a large enough dataset. Although everything was functioning perfectly, as was already noted, a better system is required due to the difficulty in interpreting the model and the amount of data required to continue learning.

Deep learning fails to extract compositional and causal structures from data, even though it excels in large-scale pattern recognition. While symbolic models aim for complicated connections, they are good at capturing compositional and causal structures.

Due to the shortcomings of these two methods, they have been combined to create neuro-symbolic AI, which is more effective than each alone. The goal is to make systems smarter by combining logic and learning. According to researchers, deep learning is expected to benefit from integrating domain knowledge and common sense reasoning provided by symbolic AI systems. For instance, a neuro-symbolic system would employ symbolic AI’s logic to grasp a shape better while detecting it and a neural network’s pattern recognition ability to identify items.

A neuro-symbolic system employs logical reasoning and language processing to respond to the question as a human would. However, in contrast to neural networks, it is more effective and takes extremely less training data.

Goals of Neuro Symbolic AI

The main objectives of NS are to show that it is capable of

Solve even more difficult issues

Learn with far less data, eventually for many different tasks instead of just one specific one.)

Make judgments and behaviours that are naturally understandable and within your power.

Key Terminologies Used in Neuro Symbolic AI

Here are some key terminologies used in neuro-symbolic AI:

Hybrid Architecture: Refers to integrating neural networks and symbolic reasoning components in a neuro-symbolic AI system.

Symbolic Representation: Refers to using symbolic representations, such as logic, ontologies, and knowledge graphs, to represent knowledge and perform reasoning tasks.

Neural-Symbolic Integration: Refers to integrating neural and symbolic reasoning in a hybrid architecture.

Perception: Refers to the ability of a neuro-symbolic AI system to process and interpret sensory input, such as images, speech, and text.

Reasoning: Refers to the ability of a neuro-symbolic AI system to perform logical inference, theorem proving, and planning based on symbolic representations.

Explanation: Refers to the ability of a neuro-symbolic AI system to provide human-understandable explanations for its predictions and decisions.

Knowledge Graph: Refers to a graph-based representation of knowledge, where nodes represent entities and edges represent relationships between entities.

Ontology: Refers to a formal representation of a set of concepts and relationships within a specific domain.

Key Components of Neuro Symbolic AI

The key components of a neuro-symbolic AI system are:

Neural Network: A component that performs perceptual tasks using deep learning algorithms, such as image recognition and natural language processing.

Symbolic Reasoning Engine: A component that performs logical inference, theorem proving, and planning using symbolic representations, such as logic and knowledge graphs.

Integration Layer: A component that integrates the neural network and symbolic reasoning engine to form a hybrid architecture. This component is responsible for mapping the symbolic and neural representations and enabling communication between the two components.

Knowledge Base: A component that stores knowledge in a structured form, such as ontologies, knowledge graphs, and relational databases.

Explanation Generator: A component that generates human-understandable explanations for the predictions and decisions made by the neuro-symbolic AI system.

User Interface: A component that allows human users to interact with the neuro-symbolic AI system, for example, to provide input and receive output.

These components work together to form a neuro-symbolic AI system that can perform various tasks, combining the strengths of both neural networks and symbolic reasoning.

How to Write a Program in Neuro Symbolic AI?

Writing a program in neuro-symbolic AI can be a complex task requiring symbolic reasoning and deep learning expertise. Here is a high-level overview of the steps involved in developing a neuro-symbolic AI program:

Define the problem: Determine what the AI system is expected to accomplish and what kind of knowledge or data it will work with.

2. Determine the knowledge representation: Choose a suitable representation for the problem domain, such as first-order logic, graphs, or probabilistic models.

3. Preprocess the data: Prepare the data for use by the AI system, such as transforming it into the chosen knowledge representation format.

4. Train the deep learning component: Train a deep neural network to learn the relationships and patterns in the data.

5. Integrate the symbolic reasoning component: Integrate the symbolic reasoning component into the AI system, such as using a rule-based system or a theorem prover, to perform logical inference and make decisions based on the knowledge representation.

6. Evaluate the performance: Test the AI system to determine how well it solves the problem and make any necessary adjustments.

7. Deploy the system: Deploy the AI system to the desired environment and monitor its performance.

Note: This is a high-level overview. The steps involved in developing a neuro-symbolic AI program may vary depending on the problem domain and the tools used.

What is a Logical Neural Network?

Logical Neural Networks (LNNs) are neural networks that incorporate symbolic reasoning in their architecture. In the context of neuro-symbolic AI, LNNs serve as a bridge between the symbolic and neural components, allowing for a more seamless integration of both reasoning methods.

An LNN consists of a neural network trained to perform symbolic reasoning tasks, such as logical inference, theorem proving, and planning, using a combination of differentiable logic gates and differentiable inference rules. These gates and rules are designed to mimic the operations performed by symbolic reasoning systems and are trained using gradient-based optimization techniques.

By combining symbolic and neural reasoning in a single architecture, LNNs can leverage the strengths of both methods to perform a wider range of tasks than either method alone. For example, an LNN can use its neural component to process perceptual input and its symbolic component to perform logical inference and planning based on a structured knowledge base.

Overall, LNNs is an important component of neuro-symbolic AI, as they provide a way to integrate the strengths of both neural networks and symbolic reasoning in a single, hybrid architecture.

Symbolic AI Vs. Neural Network

Symbolic AI and Neural Networks are distinct approaches to artificial intelligence, each with its strengths and weaknesses.

Symbolic AI, also known as rule-based AI or classical AI, uses a symbolic representation of knowledge, such as logic or ontologies, to perform reasoning tasks. Symbolic AI relies on explicit rules and algorithms to make decisions and solve problems, and humans can easily understand and explain their reasoning.

On the other hand, Neural Networks are a type of machine learning inspired by the structure and function of the human brain. Neural networks use a vast network of interconnected nodes, called artificial neurons, to learn patterns in data and make predictions. Neural networks are good at dealing with complex and unstructured data, such as images and speech. They can learn to perform tasks such as image recognition and natural language processing with high accuracy.

In summary, symbolic AI excels at human-understandable reasoning, while Neural Networks are better suited for handling large and complex data sets. Integrating both approaches, known as neuro-symbolic AI, can provide the best of both worlds, combining the strengths of symbolic AI and Neural Networks to form a hybrid architecture capable of performing a wider range of tasks.

The following image shows how Symbolic AI might define a mango:

The following image shows how a neural network might define a mango:

Use Cases of Neuro Symbolic AI

Neuro-symbolic AI has a wide range of potential use cases in various domains due to its ability to handle both symbolic reasoning and complex, non-linear relationships in data. Here are a few examples:

Knowledge-based AI Systems: Neuro-symbolic AI can be used to develop knowledge-based AI systems that use logical inference to reason about and manipulate symbols representing real-world objects and concepts.

Decision Making: Neuro-symbolic AI can be used to develop decision-making systems that consider both symbolic knowledge and data-driven predictions, such as in finance, medicine, or autonomous vehicles.

Natural Language Processing: Neuro-symbolic AI can be used to improve natural language processing tasks, such as question answering, machine translation, and text generation, by combining the strengths of deep learning and symbolic reasoning.

Robotics: Neuro-symbolic AI can be used to develop intelligent robots that can reason about and interact with their environment based on both symbolic knowledge and sensor data.

Planning and Scheduling: Neuro-symbolic AI can be used to develop planning and scheduling systems that can handle complex, real-world constraints and make decisions based on both symbolic knowledge and data-driven predictions.

These are just a few examples, and the potential applications of neuro-symbolic AI are constantly expanding as the field of AI continues to evolve.

Frequently Asked Questions

In conclusion, neuro-symbolic AI is a promising field that aims to integrate the strengths of both neural networks and symbolic reasoning to form a hybrid architecture capable of performing a wider range of tasks than either component alone. With its combination of deep learning and logical inference, neuro-symbolic AI has the potential to revolutionize the way we interact with and understand AI systems.

By integrating neural networks and symbolic reasoning, neuro-symbolic AI can handle perceptual tasks such as image recognition and natural language processing and perform logical inference, theorem proving, and planning based on a structured knowledge base. This integration enables the creation of AI systems that can provide human-understandable explanations for their predictions and decisions, making them more trustworthy and transparent.

The development of neuro-symbolic AI is still in its early stages, and much work must be done to realize its potential fully. However, the progress made so far and the promising results of current research make it clear that neuro-symbolic AI has the potential to play a major role in shaping the future of AI.

References

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Related

Facebook Brand Pages Best Practices

5 Examples of Facebook Brand Pages to learn from 

As marketers constantly look for better ways to use social media to engage with customers and generate positive word-of-mouth and sentiment, a well-designed, thought-out Facebook page is a key platform to target consumers. Facebook brand pages can offer businesses, both large and small, an excellent opportunity to create a presence and a brand experience, but only if this experience is well planned and executed.

In this post I’ll look at some examples of companies that I think have created a great experience on Facebook that fits their brand well. I hope these “best practices” give some ideas of improvements that could work for your Facebook presence.  These examples all show these key ingredients of a great Facebook brand page including:

A clear objective and raison d’être for the page

A good, professional design which is consistent with all other marketing communications

An impactful welcome page with a clear call-to-action

Integration of other social media outposts, such as Twitter, YouTube and a company blog

Rich media, especially good quality photos and videos

A newsletter sign-up form to capture more detailed information about fans

A set of house rules or posting guidelines to keep fans ‘on message’

Fun and interesting! People come to Facebook to chill out and to connect with their friends

So, here are my 5 top Facebook brand pages from well known to less well known that contain many of the ingredients above and my thoughts on what we can learn from them:

1. Coca-Cola

Likes: 36,294,451

 What makes this page great?

With well over 36 million likes, Coca-Cola is one of the most followed consumer brands on Facebook. How have they done this? By providing a rich mix of innovative, fun and immersive promotions and features that are exclusive to their Facebook followers.

Coca-Cola keeps their campaigns simple and use a blend of photos and video to encourage fans to join in and participate. The page is fun and there’s plenty to do and see.

Key takeaways:

Be creative and encourage participation

Facebook’s EdgeRank algorithm favours photos and videos more highly than regular status updates, meaning they’ll stand a better chance of finding their way into followers’ News Feeds

Post both original and user-generated rich media to grab people’s attention and spark discussions

Use fun, simple and intuitive competitions to keep fans engaged

2. ASOS

Likes: 1,409,490

What makes this page great?

The online fashion retailer was one of the first companies to move into f-commerce and create a fully integrated Facebook store that is easily accessible from within the page.

However, it isn’t just the Facebook store that makes the ASOS Facebook page great – it’s the brand’s focus on creating a memorable customer experience for its fans.

ASOS regularly posts pictures of new fashion ranges to generate discussion, announces deals and promotions and have also created an Instagram feature showcasing behind-the-scenes pictures from magazines and photoshoots.

Key takeaways:

Follow the golden rule of effective Facebook marketing: create a customer experience that keeps users ‘in-system’

Create an environment that gives your followers everything they need within the Facebook page

Focus on engaging fans on your page by posting regular updates and answering fans’ questions on the wall

Be different and stand out from your competitors. Integrate features, competitions and social outposts that others haven’t (e.g. Instagram)

3. Manchester United

Likes: 20,637,665

 What makes this page great?

The Manchester United Facebook page gives fans an immersive brand experience by offering behind-the-scenes videos, exclusive interviews and timely, considered posts that range from breaking news to birthday wishes for the players.

The page also encourages fans to interact, not only through its use of (good quality) photos and videos but also through polls, contests and questions. Every fan responds differently so Manchester United covers all options.

Key takeaways:

Offer your Facebook followers an exclusive, behind-the-scenes experience they won’t find elsewhere

Post regularly but not so much that it annoys and frustrates your fans

 4. Cisco

 Likes: 233,091

What makes this page great?

Facebook pages are not just for B2C brands. On the contrary, brands like Cisco have leveraged the popularity and appeal of Facebook to create a very well-designed, informative and immersive Facebook experience tailored to their customer’s and stakeholder’s needs.

What is also cool is a tab that allows followers to find a Cisco Facebook page specific to their country using the Cisco Facebook Global Map.

Key takeaways:

Don’t create a Facebook page because it’s the ‘cool thing to do’. Make sure your Facebook page has a clear raison d’être, preferably based on customer/ stakeholder research

Integrate other key social channels into the page to provide other social options for fans and increase visibility for those outposts

Create custom tabs that provide easily digestible information about your company or product

5. EasyLunchboxes

Likes: 15,945

What makes this page great?

Just as B2B brands can make an impact using Facebook, small businesses can, too.

EasyLunchboxes has embraced many of the key ingredients that make up a great Facebook page, including a clear, impactful ‘Welcome’ page, lots of colourful, interesting photos and the integration of their blog, Twitter and YouTube channels.

Key takeaways:

Make sure you design the page to a high standard and in a way that is consistent with the company website and all other marketing communications

Stay on topic and post updates that are consistent with the product and what the brand stands for

Add a newsletter sign-up form with a clear call-to-action to capture more detailed information of your followers and fans

Creating The Perfect Facebook Experience

Last week I was talking to my Dad about a company he has been invested in for quite some time. He asked me what I thought about the way they were using Facebook to market their medical device. Yep, a medical device. I said to him, “what do you need a fan page for?” He simply replied, “well, everyone seems to have them so we thought we should, too.”

And that’s when I proceeded to thrust my forehead into my hand. Just one of thousands of cases where people create a fan page because of Facebook’s buzz. It’s as if people think that if they create a page on there, people will not only find it on their own, but they would love to cloud up their newsfeed with your corporate propaganda. Who wouldn’t?

Now I’m not saying that he shouldn’t have one, I’m just saying that unless you plan on doing something more than throwing it up there and regurgitating your RSS feed – there really isn’t a point in investing time in it. At that point your fan page is doing nothing for you or your fans.

Which brings me to what should be the simplest conclusion in marketing: if you’re going to enter a market, you need a strategy. Facebook, Twitter, anything really, all need their own custom marketing strategy. Without one, you’ll be doing nothing but spinning your wheels and all of that time you spent doing so could have been used to sell more of your product/service.

How do we go about creating a marketing strategy for Facebook? The first step is easy: research. You need to get an understanding of whether or not your audience is even interested in seeing you on Facebook. Remember, they are on there to socialize with family and friends. Take a look at what your competitors are doing, or even your industry associations. Any company that is related to your industry is a case study you can study to determine what type of market exists for you on Facebook.

And the big question you want to answer is: what is their unique value proposition? You’re going to need to answer this one, too. You need to convince your potential fans that if they don’t “like” your page, then they’ll be missing out on unique content they can’t find anywhere else. You’re fan page is much more than an RSS feed dump, its a resource for them.

How can you go about making your fan page a resource? Unfortunately, there isn’t a general option that any company can use. It’s going to depend on what your product/service is. The first thing that comes to mind is Facebook-only coupons and discounts. Let’s say your a local restaurant who is looking to do more than the traditional coupon strategy. You could post Facebook-only recipes, announce daily specials and giveaways to only your Facebook fans, and even take polls from your fans for what the daily special/desert should be.

What are you doing to create the perfect Facebook experience for your fans?

How Will Facebook Make Money?

What will Facebook do?

Low-Hanging Fruit: Facebook’s Business Today

And that’s it.

Stay the Course?

According to Harvard Business School professor Ben Edelman, Facebook could stick with its current money-making model, but start charging for things that it does for free now. “Facebook may be using the old crack dealer model–the first puff is free,” Edelman says. After app developers and partner Websites get ‘hooked,’ Facebook could start charging for access to its user data, he says.

Edelman believes that many of Facebook’s current business partners would be happy to pay. “If American Airlines can afford to spend $100,000 to somebody to design their page, I imagine that they could afford to pay $10,000 a year to Facebook itself,” Edelman says.

Sitting on a Gold Mine?

A Facebook profile may contain your age, sex and location information. It might also know that you are an avid runner who attended a wine-tasting party last Tuesday night. It might even know for example, that you recently visited the Websites of chúng tôi and chúng tôi A marketer such as Coca-Cola or Saturn or Nike, could compare this combination of demographic and preference data, and determine your similarity to people who have already bought their products.

But there’s more. Facebook also collects and makes “public” the list of people who are your Facebook friends. If a marketer is looking to reach people who have a good chance of going out and buying their product, they might naturally want to focus on people whose friends have already purchased that product, explains Tom Phillips of Media6Degrees.

That sort of “social marketing” is a much more fine-grained and effective way of targeting potential customers than relying on the traditional demographics approach (for example, “42-year-old men in Montana often buy Ford trucks”). The tastes and buying habits of your circle of friends, in other words, are much better predictors of what you are likely to buy, than are your age, sex, and location data.

But some Web marketers, including Media6Degrees, are steering clear of Facebook user data, fearing that using it could sweep them into the center of the current privacy firestorm alongside Facebook itself. Though Media6Degree’s success depends entirely on the breadth and depth of its database of (social) people, Phillips says that his company has no interest in adding Facebook’s data to its current collection. Why? Because Facebook’s massive database is full of PII, and the stigma associated with such data is so great right now that Media6Degrees might face a privacy backlash of its own if it added some of that content to its database.

Anonymous Social Marketing?

Eric Wheeler, CEO of 33Across, another Web marketing firm, says that his company routinely licenses user data from blog sites, social media sites, and app developers, but doesn’t collect the actual IP addresses, names, or e-mail addresses from those sites. “There is no PII; We don’t need it, so none of that ever touches our system,” Wheeler says. Instead, 33Across uses cookies. A company like Sprint (a 33Across client) might provide 33Across with a list of its current customers, and then ask 33Across to track the online social interactions that those customers have with others. If 33Across determines that a particular contact might be likely to buy a Sprint product, the firm drops a cookie into the contact’s browser so that a relevant Sprint ad can be served up there in the future.

Watching and Waiting

At the moment, Facebook is closely watching the privacy backlash and the debate that has been raging since the company’s last privacy settings overhaul in late April. The public outcry has prompted talk in Washington, D.C., about the need for a social networking privacy bill, as well as a Federal Trade Commission investigation into the data management practices of Facebook and Google.

If Facebook users suddenly began receiving direct-marketing come-ons based on personal information harvested from their Facebook profiles, the result could be a user revolt, followed by class-action lawsuits, followed by government intervention.

Hospital Patients Say A Facebook

Two new lawsuits allege that Meta, Facebook’s parent company, and a number of US hospitals violated medical privacy law HIPAA, according to The Verge. These lawsuits follow a report from The Markup published this June documenting how the Meta Pixel, an ad analytics tracking tool installed on many websites, potentially shared identifying patient data in a way that violated HIPAA. Both lawsuits were filed in the Northern District of California and argued that the use of the Meta Pixel on hospital websites allowed sensitive health information to be sent to Facebook. The lawyers for the plaintiffs are trying to get them classified as class action suits and demanding jury trials. 

But let’s step back a bit and answer some key questions: What is the Meta Pixel, how does it work, and why are hospitals’ installing it on their websites? And since they are, is that likely to be a HIPAA violation? 

The Meta Pixel is automatically triggered when someone visits a website with it installed. If they’re logged into Facebook (and not using a browser that protects against third-party tracking), it sends information about who they are and what they do on the site to Facebook. (Even if they’re not logged in, Facebook has other ways of attempting to glean information about visitors through the Meta Pixel). What information is sent to Facebook is controlled by the website operator, and this is where the HIPAA troubles start. 

As part of The Markup’s Pixel Hunt investigation into Facebook ad tracking, it tested the websites of Newsweek’s top 100 US hospitals for 2023. It found the Meta Pixel installed on 33 of them, and all of them sent sensitive data to Facebook, including identifying information such as a visitor’s IP address, and when they attempted to schedule an appointment. 

[Related: How data brokers threaten your privacy]

For seven hospitals, the situation was even worse. The Meta Pixel wasn’t just installed on the public facing web pages, but also on the password-protected patient portals. For five of those websites, it documented real patient data—provided by volunteers who signed up to help the Pixel Hunt investigation using Mozilla’s ad-tracker tracking Rally plugin—being sent to Facebook. Some of that information included “the names of patients’ medications, descriptions of their allergic reactions, and details about their upcoming doctor’s appointments.”

It’s important to note that Facebook itself is not subject to HIPAA as it is not a healthcare provider. Still, there is cause for legitimate scrutiny of how Meta handles sensitive data. Following a report in The Wall Street Journal and a New York Department of Financial Services investigation in 2023, Meta said it was introducing a tool to automatically filter out sensitive medical data sent by websites through the Meta Pixel. However, according to previous reporting by The Markup and leaked Facebook internal documents, it is unlikely that the tool is 100 percent effective at filtering out sensitive medical data. 

Medical providers, on the other hand, are bound by HIPAA. They are not supposed to share data with third-parties without express consent from the patient in question. From The Markup’s reporting, it seems unlikely that any of the hospitals obtained that. 

While the majority of hospitals documented by The Markup’s investigation removed the Meta Pixel from their patient portals after they were contacted (and some also removed it from their public websites), their past actions set the stage for these two lawsuits. 

We won’t know whether either case will become a class action or even proceed for a while yet, but it’s another bad story for Meta—which really can’t seem to catch a break. 

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