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  • Writer's pictureSigurður Ragnarsson

AI Content Moderation: Use, Types, & Integration With Hash Matching



 

Videntifier technology is built on hash matching, i.e., digital fingerprinting/local descriptor hashes. However, when it comes to content moderation, we don’t turn a blind eye to identification solutions that are taking the world by storm. Content moderators do the hard and often psychologically taxing work of keeping the digital space safe, so while we truly believe in hash matching’s capabilities, it would be insensitive of us to ignore the fact that there are highly effective, non-hash-based solutions to content moderation out there today. 


If you are still reading, you probably know what we’re alluding to: AI content moderation. So, for the sake of building a safer, more secure digital space, let’s dive into AI content moderation and see what it can do. Then we’ll pair it with hash matching to get a sense of what dual solutions might afford the content moderators in the near future. 


What is AI content moderation?


Though AI is seemingly in its infancy, the use cases for artificial intelligence are growing. Individuals can use ChatGPT, DALLE, and countless other programs for personal use. On an enterprise scale, online service providers and social media platforms use AI to cull massive amounts of user-generated content violating the law or community guidelines.


AI content moderation uses artificial intelligence (AI) and machine learning algorithms to scan, filter, and control user-generated content across various online platforms.

AI moderates the content shared on platforms to ensure compliance with community guidelines, terms of service, and laws. As a result, AI content moderation can create a safer and more conducive online environment.


How it works


Artificial intelligence (AI) content moderation runs by examining data trends and producing algorithms, which conform to the guidelines and restrictions of an online service platform. These algorithms enable AI to forecast, decide, and flag or remove content deemed to be harmful. For instance, your online service should be able to correctly identify whether a recently uploaded photo contains objects prohibited by law or community guidelines, such as specific body parts or other explicit material.  After that, AI will either flag and delete the content or pass it on to a moderator so they can manually judge whether the image is unsafe.


Use cases for AI content moderation


User safety and trust: By swiftly removing inappropriate or harmful content, AI content moderation aims to create a safer online environment, fostering trust among users and maintaining the platform's integrity.

Scalability and efficiency: AI-powered content moderation systems can handle large volumes of content faster than human moderators, ensuring quicker responses to potentially harmful content.

Cost-effectiveness: While there's an initial investment in developing and training AI models, over time, AI can be more cost-effective than a large team of human moderators, especially for platforms with massive amounts of content.


AI content moderation types & features


Automated filtering


AI algorithms automatically analyze and flag content that may violate platform policies. These systems can identify various types of content, including hate speech, spam, explicit images or videos, bullying, misinformation, and inappropriate content. They use advanced techniques to identify inappropriate images or videos, including nudity, graphic violence, and other forms of harmful or illegal content.




Natural Language Processing (NLP)


AI content moderation relies heavily on Natural Language Processing (NLP), which allows automated systems to screen text-based content for spam, fraud, toxicity, hate speech, and offensive language. AI models equipped with NLP capabilities understand context, sentiment, and nuances in language, aiding in more accurate content moderation decisions. This capability helps distinguish between harmless discussions and potentially harmful content, contributing to user safety and trust by swiftly removing inappropriate or harmful content.


These systems are also multilingual and adaptive, adapting to feedback and learning continuously. Finding a balance between reducing false positives and false negatives is the difficult part, though. Human monitoring is still necessary to supplement NLP-based moderation and reduce potential biases in content moderation decisions.



Human-in-the-loop approach 


While AI plays a significant role, many platforms utilize a hybrid approach that combines AI technology with human moderators. Human moderators handle complex cases, review flagged content, and fine-tune AI algorithms to enhance accuracy and reduce biases. This approach acknowledges the strengths and limitations of both AI algorithms and human moderators, aiming to leverage the advantages of each to achieve more accurate and contextually sensitive content moderation.


Human moderators possess contextual understanding, cultural awareness, and nuanced judgment that AI systems may lack. They can assess content based on context, intent, and nuances that algorithms might struggle to comprehend accurately. Human moderators provide feedback on the accuracy of AI-generated flags. This feedback loop helps refine and improve the AI algorithms over time. Human input allows for the adjustment of algorithmic models, reducing false positives and negatives and enhancing the overall effectiveness of content moderation.


Where AI content moderation falls short


AI content moderation, while increasingly sophisticated, has several notable shortcomings that should be taken into consideration by moderators and platforms interested in hiring moderation services. 


  1. Inability to understand nuanced contexts: AI often struggles to grasp the nuances of human communication, such as sarcasm, humor, or cultural references. This can lead to the misinterpretation of content, either by flagging benign material as problematic or overlooking genuinely harmful content.

  2. Bias and discrimination: AI models can inherit biases present in their training data. This could result in unfair moderation practices, where certain groups or viewpoints are disproportionately censored or targeted.

  3. Over-reliance on automation: Excessive reliance on AI for content moderation can lead to a lack of human judgment in complex situations. AI is not yet capable of understanding the full complexity of human interactions and ethics.

  4. Scalability and adaptability issues: While AI can process vast amounts of data quickly, it may not effectively adapt to new forms of harmful content or evolving social norms. This lag can create windows of vulnerability where harmful content remains unmoderated.

  5. Privacy concerns: Content moderation AI often requires access to large datasets, which raises concerns about user privacy and data security. The collection and analysis of user data can be intrusive and potentially exploited.

  6. Error rates and false positives/negatives: No AI system is perfect, and content moderation systems are prone to errors, including both false positives (flagging harmless content as harmful) and false negatives (failing to detect actual harmful content).


Rectifying AI's pitfalls with hash matching


While these shortcomings may seem extensive, the good news is that they can be supplemented with other highly capable moderation technologies/solutions. For example, by dovetailing AI with hash matching technology, moderators can drastically reduce their overreliance on AI automation, and, in many cases, completely eliminates the pitfalls AI is prone to. 


Hash matching and AI combination


To quickly define hash matching, every piece of content is assigned its own digital fingerprint (also known as a hash). This fingerprint or hash is then tested against all incoming content, with the matched content either flagged and removed or sent to the moderator/AI workflow for further analysis.



Videntifier required reading:

Hash Matching Will Save Content Moderation, Faster Than AI Currently Can On Its Own


Explore how highly advanced yet lightweight hash matching technologies can come together with AI to provide a fully comprehensive solution.





Hash matching currently exists as a fully automated solution for identifying known content. To provide a quick definition, every piece of content is assigned its own digital fingerprint (also known as a hash). Systems can then effectively identify and react to threats or flagged content based on previously published content stored in hash databases by integrating AI-driven analysis with hash matching. Platforms often use shared databases (like those maintained by National Center for Missing & Exploited Children) for hash matching, enabling a collaborative approach to blocking known harmful content. This allows for speedier and more accurate detection and mitigation of inappropriate content.


By properly utilizing a collaborative AI-hash matching system, content moderation may be able to improve labor standards while also simplifying the moderation procedure. Hash matching, being a non-intrusive method, respects user privacy while AI provides a nuanced understanding of content, ensuring that moderation aligns with freedom of expression and privacy concerns.


Here are a few examples of how hash matching can supplement AI


Reducing misinterpretation: Unlike AI that relies on context and linguistic nuances, hash matching works by identifying exact duplicates of known harmful content. This method can be more precise in targeting specific content without misinterpreting the context, thereby reducing false positives and negatives.


Mitigating bias: Since hash matching is based on exact digital 'fingerprints' of content, it's less prone to biases that can be introduced through the subjective interpretation of AI models. It treats all content based on its hash value, irrespective of the subject matter or origin.


Enhancing Scalability and adaptability: Hash databases can be continually updated with new hashes of harmful content, making the system more adaptable to new threats. This method can scale efficiently as it's less resource-intensive than real-time AI analysis for known content.


In short, the hash matching side of the process is able to identify previously detected content and remove it without exposing it to the moderator, drastically cutting down reliance on AI and providing moderators with more time/resources to carefully sift through content that needs human attention. 


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