Hey there! With over 1.7 billion websites in existence today, the amount of information shared online is mind-boggling. The web has connected people across the globe, giving everyone a voice. However, it has also enabled the spread of illegal and dangerous materials like child abuse images, terrorist propaganda, dangerous conspiracies and more. Detecting and removing this harmful content quickly is crucial, but no human team could ever monitor more than a fraction of the data being produced. This is where artificial intelligence comes in!
In this post, we‘ll explore how AI and machine learning allow us to automatically detect illegal online content on a massive scale and make the web a little bit safer. I‘ll walk you through real world examples of AI in action, the techniques used to identify banned materials, best practices for implementation, limitations of the technology and where things might be headed next. Let‘s dive in!
The Sheer Scale of the Problem
To really grasp why AI is so critical for finding illegal content online, it helps to understand the huge size of the issue. According to the Internet Watch Foundation, they received reports of over 105,000 web pages containing child sexual abuse imagery in 2020 alone. That‘s almost 300 flagged pages per day!
And consider that a report by the Counter Extremism Project revealed over 4,000 ISIS-linked pages and groups actively promoting extremist propaganda across mainstream social media. Or the fact that YouTube alone has over 500 hours of fresh video content uploaded every single minute.
Manually reviewing all this would be impossible. With so many new websites, platforms and communications channels opening up, the problem only grows. No human team could ever keep up with the firehose of data.
AI, on the other hand, can analyze millions of images, videos and text documents in seconds. It allows us to find and remove illegal materials at the tremendous scale required in the digital era. This is why AI is now on the frontlines keeping the web a bit safer!
How AI Is Able to Flag Banned Content
AI leverages two main techniques to pinpoint illegal or dangerous materials online:
Image and Video Analysis
Many forms of prohibited content have distinct visual patterns. Child sexual abuse media, for instance, depicts very specific types of criminal photos and videos. Terrorist propaganda frequently uses recognizable symbols, flags and imagery.
By training machine learning algorithms on many examples of this material, the AI systems can become extremely adept at recognizing the subtle indicators. They analyze the visual features of content, like shapes, textures, objects, faces and more. Over time, the algorithms learn to identify banned materials with high reliability.
For example, Facebook reported in 2020 that their AI systems were able to proactively detect 99.3% of the child nudity and sexual exploitation content that was eventually removed from their platforms. Their algorithms now even exceed human reviewers in finding these needles in the haystack.
Platforms like YouTube, Facebook and Twitter use this visual analysis technology to automatically flag or remove prohibited images and videos during the upload process. This allows them to take down illicit content much faster than if they needed to rely on human reports.
Natural Language Processing
In addition to analyzing visual content, AI systems can also scan text using natural language processing techniques. This allows them to "read" and comprehend the meaning of online posts, comments, chat messages and other text.
By training on many examples of prohibited language – like threats, racial slurs, dangerous conspiracies or terrorism instructions – the algorithms learn to recognize similar texts. This means AI can identify warning signs in communications across virtually any website, forum or app.
For instance, Twitter employs AI algorithms to detect hate speech and abusive language on their platform. According to the Center for Countering Digital Hate, over 90% of racist, sexist and other toxic tweets removed from Twitter are flagged by automated machine learning tools, not human reports.
So combining image and text analysis allows AI to detect illegal materials across multiple modes of online communication. This provides a much more comprehensive approach compared to humans manually reviewing content.
Best Practices for Implementation
There are a few key best practices that can help ensure AI systems are effective at identifying banned content:
-
Large, labelled datasets – Algorithms need many examples of illegal content to learn the common patterns. Platforms should compile training datasets with detailed labels to improve detection.
-
Human content moderators – While AI flags content, human reviewers make the final calls on takedowns. Their feedback further improves the AI‘s accuracy.
-
Adaptability – Algorithms need regular retraining on new data to keep pace as illegal content evolves. Models can become outdated rapidly.
-
Multiple content types – Training AI on text, images, audio and video provides a more holistic view compared to a single content type.
-
Transparency – Clear user communication about AI takedowns and options to appeal builds trust. Lack of transparency erodes confidence in automated decisions.
No algorithm is perfect. But following practices like these allows platforms to deploy AI that dramatically scales up the search for prohibited materials online.
AI Successes in the Fight Against Harmful Content
Both government agencies and private companies have already utilized AI to make progress detecting illegal and dangerous content:
-
Microsoft – Microsoft‘s AI systems helped identify and report over 600,000 items of child exploitation material between March and November 2020. Their algorithms achieved an accuracy rate of over 94% in flagging illicit photos and videos.
-
UK Home Office – The British Home Office funded an AI tool called Cerberus that can detect extremist propaganda with over 90% accuracy. During testing, the system successfully identified ISIS and right-wing terrorist content.
-
Facebook – On its platforms, Facebook estimates its AI proactively flags over 80% of the hate speech content that is ultimately removed before users even report it. The algorithms also detect nearly all child sexual exploitation material.
-
YouTube – By analyzing metadata like titles, descriptions and subtitles, YouTube‘s AI has reduced violent extremism watch time by an average of 83%. Machine learning drives most content policy enforcement on the platform.
These examples demonstrate that while not perfect, AI detection has already proven its worth by finding needles in the massive haystacks of daily internet data. Continued progress will only increase its impact on safety.
How AI Image and Video Analysis Works
Now that we‘ve looked at some real world examples, let‘s get a bit more technical. How exactly does AI analyze images and video to identify potentially illegal content?
The algorithms rely on computer vision techniques that allow the software to "see" and comprehend visual data. Here are some of the key steps:
-
The image or video file is broken down into pixel data that the algorithm can interpret.
-
Built-in convolutional neural networks (CNNs) analyze the pixel information, identify patterns and extract useful features about shapes, textures, objects, faces etc.
-
These visual feature outputs are compared against the patterns learned from training data to look for matches.
-
Based on the training, the AI estimates a probability that the content matches known illegal material.
-
If the probability passes a defined threshold (e.g. 90% certainty), the content is flagged for removal.
So the algorithms aren‘t necessarily looking at the full context and meaning of the image like a human. But by recognizing learned visual patterns through deep neural networks, the AI becomes extremely adept at flagging concerning content for additional review.
The same core approach also applies to natural language processing for analyzing text. The algorithms extract text features, compare them to learned patterns and estimate a match probability to known prohibited language.
Over time, with the right training, AI‘s ability to interpret both visual and textual information can surpass most human reviewers. This enables the analysis of internet data at enormous scale.
Limitations and Challenges of AI Detection
While AI has made huge strides, there are still some key limitations and challenges to automating content moderation:
-
Context comprehension – Unlike humans, algorithms struggle to factor in cultural context and satire that changes meanings. More false positives result.
-
Adversarial content – Those posting illegal content are evolving new ways to evade AI detection like subtle image edits. Continual retraining is needed.
-
Encryption – End-to-end encryption prevents external analysis, creating blindspots. Methods are needed to scan pre-encryption.
-
International coordination – Global issues like terrorism require collaborative data sharing and coordinated responses across regions and platforms. Achieving this remains difficult politically.
-
Livestream monitoring – Streaming adds new challenges like real-time analysis and takedowns. Progress has been made but more work is required.
There are no perfect solutions yet. However, the technology continues advancing rapidly and researchers are exploring ways to address these limitations.
For instance, Google recently proposed using multi-task models that analyze content while also considering signals like user accounts to better incorporate context. And platforms are coordinating more with other sites to adapt to adversaries and cross-post violations. So progress is being made incrementally.
What‘s Next for AI Detection Technology?
While AI content moderation has already had an enormous impact, there are still many opportunities to improve and refine detection technology:
-
Cross-platform coordinated detection – Most detection today works within closed platforms. New techniques aim to identify banned users and materials across multiple sites.
-
Proactive threat discovery – Thus far AI has reacted to issues. More proactive discovery of emerging threats before they spread widely remains challenging but impactful.
-
Integrating human knowledge – Better integrating human moderators‘ knowledge into the AI models could improve comprehension of nuances that algorithms miss.
-
Multimedia analysis – Combining different data types like text, images, audio and video together could enable more holistic detection across platforms and users.
-
Sharing data resources – A collaborative approach where different stakeholders share data training resources could accelerate AI development. But data privacy concerns exist.
-
Decentralized and transparency focused models – New models built around principles of decentralization and algorithmic transparency could address bias concerns and gain community trust.
Better datasets, more advanced neural networks, faster processing and new techniques will enable AI to take on more content types across a wider array of platforms. The scale of the issue means solutions must continue scaling up.
Case Study: Implementing AI Moderation on a Social Platform
Let‘s look at a case study to get more concrete about how a hypothetical social media platform might implement AI assisted content moderation:
The Problem
- Fast growing social media site with hundreds of hours of new video uploads daily
- Manual review team struggling to keep up, prohibited content often online for days
- Risks becoming a haven for dangerous conspiracy theories and hate if unchecked
The Approach
- Build a classified dataset of 10K images containing banned symbols and 10K text snippets with hate speech
- Train a deep neural network AI system for visual analysis and natural language processing on this dataset
- Conduct testing, refine model until accuracy for both images and text exceeds 90% on new examples
- Integrate API for pre-screening every new upload against the AI system
The Impact
- AI system automatically flags 30% of uploads for human review of potentially dangerous content
- Human moderation team productivity increases 5x now that obvious violations are filtered out
- Average policy violation online time drops from 5 days to under 1 hour due to automation
- The UpNext social platform brand improves as dangerous content is removed faster
By combining smart AI system design with adequate human moderator oversight, even smaller sites can leverage automation to better handle policy violations at scale.
The Path Forward
The amount of knowledge and communication online will only continue exploding in the years ahead. This will surely present new challenges in keeping illegal and dangerous content under control. However, AI has already demonstrated immense capability to scale up detection and takedowns to help meet this goal.
Advancements in computer vision, natural language processing and machine learning will enable algorithms to flag prohibited materials with increasing speed and nuance. While human review remains essential, AI assistance allows this crucial oversight to handle far more data than ever before.
With vigilance and care, we can work together to create an internet that balances the freedom of open information with the security of removing clearly harmful content. The solutions explored here are an important part of empowering better online communities.
So in summary, artificial intelligence provides invaluable assistance in cleaning up the web and making it a little bit safer for everyone. The progress thus far has been extremely promising, but there is still plenty of potential to unlock in the years ahead. It‘s an exciting time seeing technology leverage massive data analysis for social good! Let me know if you have any other thoughts or questions.
