AI Content Moderation vs. Human Content Moderation: Which is Better?

How do platforms like Facebook, Instagram, Twitter, Google, YouTube, and other social media platforms manage to process the countless millions of posts, comments, videos, and photographs that are sent to their websites every day? It requires collaboration between automated and human content moderators.

User-generated content (UGC) is constantly changing the rules of the game for a company’s growth strategies, forcing companies to adopt content moderation to control all of the information posted about their brand.

In this article, we are going to discuss the difference between artificial intelligence (AI) content moderation and human content moderation. We will also talk about the benefits and drawbacks of both.

AUTOMATED MODERATION MEANING 

AI content moderation speeds up the process of assessing every content, which makes it crucial in a pool of UGC that is always expanding. By using automated moderation, it is also possible to block the IP addresses of users who have been flagged as abusive.

With the aid of algorithms, built-in knowledge bases, natural language processing, and computer vision AI content moderation is achievable. 

HUMAN CONTENT MODERATION MEANING

Human content moderation is the process of manually monitoring and screening user-generated information and removing content that is fraudulent, offensive, illegal, scam, or otherwise in violation of the rules. 

Automated tools are unable to judge full-scale datasets and make content-specific judgements, human content moderation is still essential in order to sort through this subjectivity. 

DIFFERENCE BETWEEN AI CONTENT MODERATION & HUMAN CONTENT MODERATION

Let’s get into more detail about how AI and human content moderation differ:

Cost-effectiveness

You need a lot of human moderators because there is a lot of data that has to be moderated in order to accomplish a large amount of work. More employees mean a larger staff, and a larger staff entails more payroll expenses.

However, the cost of AI content moderation varies depending on your organization’s requirements. The price of AI software depends on a wide range of variables, including your company’s preferences and the kind of AI necessary for particular tasks.

Accurate Content

When assessing texts, there are many subtle differences in the content that require a critical comprehension to decide whether it is allowed or not.

It is incredibly difficult to analyze human speech because it has so many different meanings. Automation technologies are only partially reliable at locating content across linguistic categories.

It goes without saying that AI content moderation can regulate content faster than a person could. It has the ability to complete repetitive moderation jobs in shorter amounts of time, making it the perfect tool for categorizing large amounts of content.

However, human moderators are better able to preserve the accuracy of content regulation when it pertains to subjective ideas.

Quality of Moderation

In addition to the daily increase in UGC, the excessively inappropriate topics that are frequently noted among it may put a strain on human workers and degrade the effectiveness of content control.

The logical answer in this case would be automated content moderation, which can handle a significant portion of the process. It protects human moderators from being exposed to and taking in the most offensive stuff. 

HOW AI CAN HELP IN CONTENT MODERATION? WHY DO WE NEED IT? 

Since it helps produce and sustain a product that is then sold to customers, AI content moderation is a significant accomplishment in the history of social media management. Automated content moderation is incorporated into business plans in this manner, helping to increase user engagement and define brand identity.

Here are the following advantages of AI content moderation:  

Content Filtering & Automation

Given the enormous amount of UGC, manually editing material becomes difficult and calls for scalable solutions. AI content moderation can automatically scan words, images, and videos for harmful material. Additionally, AI content moderation may categorize and filter content that is deemed improper in a certain situation and aid in preventing its posting, supporting human moderators in the content review process and assisting brands in maintaining the quality and safety of their content.

Reduced Exposure to Dangerous Content

Human moderators have to deal with content that is controversial. And people who think their decisions are biased often question their ability to help. Moderation is hard for people to do. The fact that they have to filter out so much bad stuff could have negative effects on their mental health.

AI can help human moderators by sorting out questionable content for human review. This saves content moderation teams the time and effort of going through all of the user-reported content. AI content moderation can also make humans more productive. This is by making it easier for them to manage online content faster, more accurately, and without making mistakes.

Speed & Scalability

By 2025, the World Economic Forum predicts that humans would produce 463 exabytes of data per day, which is equivalent to more than 200 million DVDs every day. Humans are unlikely to be able to keep up with the amount of UGC that is available. 

On the other side, AI content moderation can offer scalable data handling across several channels in real time. In terms of the sheer amount and volume of UGC it can scan and detect, AI content moderation can outperform humans. AI can scale on demand and quickly analyze massive volumes of data when it comes to content control.

Monitoring Real-Time Content

Moderating real-time UGC data is absolutely essential to maintaining the user experience safety of your platform. The AI-enabled solution aids automated content moderation systems by instantaneously identifying the type of content and flagging it before publication. One benefit of it is that it protects the platform from harmful and malicious information that may otherwise disturb users’ thoughts.

Pre-moderation Enhancement

AI content moderation has a significant impact on how pre-moderation works. Context-based moderation and content-based moderation are the two components of the procedure. AI modifies text using a variety of objects, including sentiment analysis, word matching, word embedding, natural language processing (NLP) classifiers, and others.

AI CONTENT MODERATION PROBLEMS

While it generally works out well, there are challenges in AI content moderation as well. Let’s talk about the AI content moderation problems. 

Bias in the Creator & Dataset 

Bias in automated technologies is one of the main issues with algorithmic decision-making in a variety of businesses. Automated technologies that make decisions, including in the context of content moderation, carry the potential of further stigmatizing and silencing already disproportionately subjected populations both online and offline. Therefore, when making decisions, the use of such automated methods should be kept to a minimum.

Importance of Context 

The machine learning technology doesn’t take into account the context of a post. Which is often what makes it illegal or against a content policy. Some data, like the sender and recipient of a message, could be used by a machine learning tool. But this could be very bad for your privacy. In essence, it is much harder to use technology to figure out other kinds of contexts.

Accuracy

The sort of information that a tool is trained to identify and eliminate greatly influences how accurately it can do so. Smaller sites may rely on commercially available automated techniques, however the accuracy of these systems in locating information across several platforms is limited.

Accountability & Transparency

The inherent lack of transparency surrounding algorithmic decision-making as a whole is one of the main issues with the use of automated solutions in the content moderation space. Because nothing is known about how these algorithms are constructed, what datasets they are trained on, how they find connections and make choices, and how trustworthy and accurate they are, they are frequently referred to as “black boxes”.

Algorithms don’t have “critical reflection,” unlike humans. In fact, researchers are unable to determine how the algorithm creates the associations it detects when using black box machine learning systems. Currently, several internet platforms only disclose a small portion of the methods they use to find and delete information using automated algorithms. 

Understanding Human Speech in Context

The limited capacity of technology to understand contextual changes in speech, visuals, and overall cultural standards presents another barrier to automated content moderation. It may be okay to use certain terms or slang expressions in one place while finding them offensive in another. For automated platforms, subtleties and variances in language and behavior could also be challenging to detect. The use of images in context can be challenging at times. 

However, because content moderation is fundamentally subjective and human speech is not objective, this technology is constrained in that it cannot recognize the complexities and contextual differences present in human speech.

Flexible & Dynamic Models are Essential

Flexible and dynamic models are needed because human communication patterns change quickly, and speakers who are barred by automated filters have an added motivation to figure out how to bypass the filter. Machine learning algorithms that are static and unable to effectively classify user communications will quickly become obsolete.

The Distinction of the Domain

NLP software performs best. Building tools that function in various contexts, languages, cultures, interest groups, and topic areas is challenging.

A Bias Introduced by an Annotation Could Affect Supervised Learning

Many people are typically involved in labeling a dataset for supervised learning, reviewing samples and selecting the appropriate label, or assessing an automatically applied label. Understanding the consistency of performance across various labelers of a dataset using intercoder reliability is crucial. Low intercoder reliability demonstrates that the humans deciding whether the information is categorized as “hate speech” or “spam” disagree.

CONCLUSION

It gets tougher for businesses to keep up with the need to check the information before it goes live One efficient answer to this expanding problem is AI content moderation.

However, the widespread usage of AI is concerning. Because it has been discovered that many of these automatic systems are erroneous. This is partially due to the lack of diversity in the training samples used to train algorithmic models.

Currently, rather than serving to replace humans, AI content moderation still performs best as a tool. AI will not be able to completely replace people, especially in this field.

The best results can be achieved by businesses combining these two. Both approaches can create an operational framework that enables companies to get the greatest moderating outcomes in the online world.

Magellan Solutions Can Provide a Quality Service for Your Content Moderation Needs

Make a difference for your company and achieve the outcomes you’ve always desired.

If you are looking for a content moderator in the Philippines, Magellan Solutions will undoubtedly assist you with your business goals and objectives at a significantly reduced cost without sacrificing quality.

With 18 years of expertise in the field, we take great pride in offering only the best call center services. On top of that, we are an ISO-certified and HIPAA-compliant outsourcing company. We provide a great variety of top-notch outsourced business solutions. 

Contact us now and let us assist you for your business needs. 

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