What would the world be like with machines taking over?
It is a slightly disconcerting thought to ponder on, at first. The truth is technology has been nothing but instrumental for several sectors in the present-day community, albeit a confusing riddle to figure out to some.
Businesses who’ve had their hand in digitally marketing their services and extending to the online community are witness to the harmonic teamwork between ai and content moderation.
It is no secret that filtering specific phrases, censoring curse words, and regulating the approval of various user-submitted content are all made faster and more accurate by the power of Artificial Intelligence. But is that all it really has to offer? And can AI fully replace humans? What role does human moderation have to play in light of the expanding capabilities of machine learning in keeping UGC in check?
User-generated content now gets a thorough check through the assistance of AI-powered processes and functions. One of the main hurdles in moderation that machine learning has helped solve is reducing the volume of user posts that needs to be reviewed daily. It has likewise offered a scalable approach for making content regulation more accurate.
To make an AI adept at reviewing different types of content, the first thing that must be done is to feed it with information relevant to the community, topics, or limitations that it is designed to moderate. By “educating” the automoderator with samples of texts or images of content or topics that are banned or allowed in a social media group, website, page, or digital forum.
The use of machine learning and advanced tools in content moderation differ based on different types of UGC.
Texts in the form of comments, reviews, replies on forums, blog posts, and even hashtags are the most common form of posts that people from across the world create and share online. Precision in checking text-based user posts involves meticulous natural language processing, or the capability of machines to comprehend the context behind written words of humans.
It is an essential component if you want AI to discern the intent behind various posts of the people who make up your digital community. The human language is quite complex and easily misunderstood without the proper references and resources. You have to consider diverse languages, dialects, slang terms, etymologies and cultural differences. As such, there needs to be the perfect blend of models for deep learning, machine learning, and computationally modeled human language or linguistics. These systems work together to simplify and detect any hidden meanings, incongruities, similarities and distinctions in worded content. Translating speech to text is also possible nowadays.
The libraries embedded in tools for natural language processing enable them to break down, segment, trim down, and analyze phrases, words, sentences down to their very core and simplest or base forms. For instance, AI identifies names of people and places like Biden or Chicago, or pinpoints the intended use of a particular word with multiple meanings. Additional applications of AI-fueled text moderation include detecting spam content through feeding the technology with phrases, terminologies, or words that are typically used in spammy UGCs. More complex uses of automoderation involve understanding the subjective undertones of what people post online.
Similar to how machine learning works in regulating text, AI reviews thousands of images through the assistance of a comprehensive and consistently updated internal library. Tech also covers functions that imitate the way the naked human eye sees and perceives visual content.
Some of the known methods in deconstructing digitally shared photos are rooting out and deducing the objects portrayed in the images. Paired with semantic segmentation, AI earns the skill of recognizing visually represented faces or items. Think facial recognition technology, or sorting through images with fully naked people, firearms, graphically violent scenarios, or gore. Additionally, text moderation is still relevant in reviewing images, since some pictures tend to contain words or phrases with inappropriate messages. Also, the tags or words used to describe what the photo is all about, give away the unacceptable nature of the image in question.
There are several other contributions of visual content moderation with ai in securing customers and businesses online. Format-wise, they reduce the time needed to check whether a forum member’s profile photo or banner coincides with the file types, size restrictions and resolutions supported on the website. Duplicated images are discovered in a jiffy, while incompatible images are rejected. In terms of quality, blurry or pixelated photos are at the top of an AI moderator’s blacklist of UGC to delete. Ecommerce sites are pretty strict when it comes to the accuracy of details that sellers indicate in their products. Thus, they use an automoderator to distinguish product photos with one or more products, along with the color and type of items that it showcases.
As for context, the background of an image also counts particularly when it raises suspicion of violating any of the guidelines in the community, website, or page. Say the main focus of a photo is a box, but if you stare at the background colors for a solid minute, you see a carefully concealed middle finger sign in a darker hue than the picture’s (bluff) main subject. Identity theft is prominent online, and artists are among the poor victims of such a despicable act. It is for this reason that text and image checking combined allow advanced tools to find images with watermarks or logos of existing brands and graphic artists.
Video moderation mainly concentrates on memorizing and familiarizing the sequencing of frames in a video. The process makes timestamps all the more important in websites and platforms that allow users to produce or share content in the form of a digital recording, a motion picture, or film. Figuring out the chronological order of each frame is the secret behind how AI gets to understand the subject of a video content while also scrutinizing its quality. Is the video crystal clear or is it blurry and pixelated? Is it saved in a format that is compatible with the platform or website’s system?
Speech recognition plays a role in monitoring user videos. Most of you may be familiar with YouTube’s strict rules against the mention or suggestive implication of swear words, racial slurs, discriminatory remarks, and sexually suggestive comments on their content creators’ videos. Before someone reaches out to YouTube to report a content creator for using foul words on their videos, it is possible for automoderators to be two steps ahead and block the violator in advance.
Content moderation using ai is by far an efficient technology that helps several brands at present. Sadly, that does not automatically mean that it is free from scrutiny and obstacles.
Even the best tools available have their own share of imperfections, and so breaking down these discrepancies and shedding light on them is of high importance.
Machine learning for content moderation is a milestone. However, reaping its benefits to the full extent is hampered when a business that attempts to employ AI moderation is not well-equipped with an updated configuration, design, or library of references on their digital community, page, or app. Imagine applying a state-of-the-art automoderator with advanced features such as face detection, real-time text-to-speech recognition, or instantly banning videos and user profiles with harmful details on an outdated platform or site. It would be impossible for an AI to perform smoothly within an obsolete environment.
Yes, technology is gradually learning how to assess the qualitative messages in a sentence, video, or image. On the other hand, successfully grasping a user’s intent in their posts is still lacking in accuracy and independence. Not all tools for AI moderation are fully developed to adapt to a multitude of human emotions and subtle hints of disturbing implications. It will take a little more time before AI can do this on its own and in real-time.
If the human language is a jigsaw puzzle for AI, then discerning subjective meanings and read-between-the-line type of posts are a different level of artificial brain-teaser. Here’s an example:
A user excitedly shares happy news to their fellow members and in the moment, drops the F-bomb along with a couple more swear words due to their ecstaticness. The scenario depicts there are some expressions that, at times, cross the border between elation and anger. On its own, the AI moderator will either flag or delete the said post. Meanwhile, a human moderator may hit the pause button on the automoderator and double-check that the user in fact, did not mean to curse their digital comrades.
Another example would be the excessive use of exclamation points and capitalized letters that AI may not detect as a vulgar or blatant display of negative emotions right off the bat. Suicidal hints may or may not be openly identified by robots. Let’s say a person regularly disseminates heart wrenching poems or quotes about saying goodbye for good. As long as there are no explicitly added texts or objects on any of the posts, the AI will probably not reassess it as a cry for help.
Definitely, ai is faster than human content moderators. It may not be as careful and meticulous, though.
Some faces look alike, thereby raising the possibility of wrongly tagging an individual’s profile photo as someone else’s. Also, have you ever tried using FaceApp? One of its features includes transforming your facial features into that of the opposite sex or make you look younger or older.
People can easily bypass our trusty modern content checkers by using digitally manipulated images. At the same time, if an automoderator’s resources are not consistently fed with brand-new information, then it will have a hard time matching the pace of new lingo, slang terms, and socially relevant trends that go hand-in-hand with the kind of content and community it is tasked to police.
Tricky internet users fill their posts with techniques to bypass content filters. They add special characters such as asterisks and periods in between the letters of prohibited words, or discreetly spell sexual innuendos using the first or last letters of each word in a seemingly harmless sentence. These simple manipulations are enough for an AI to give these UGCs the coveted green light.
The disadvantage to technology is it becomes powerless and inefficient when bugs, hackers, and power interruptions enter the picture. A heavily compromised moderation database puts a website and its users at risk.
Bugs are inevitable, and cause the software to either slow down or malfunction at the height of the massive incoming posts on the moderator’s end. Worse, hackers are potentially lurking around an app or site, waiting for the perfect timing to strike and render advanced moderation methods as practically useless.
Failing to shield your mod bots from these digital attacks will cause more severe damage to your brand and supporters in the future.
There will always be a tight comparison between machine learning and content moderation. On a positive note, the differences between humans and technology are also the key element that harmonizes the strong and weak points of each.
At the end of the day, people created and refined technology to improve life in general. For any advanced tool to continuously meet what it is expected to carry out without fail, humans must do their part to nurture what they have produced.
Now, how can humans and AI work in perfect union when regulating content online?
For starters, recent news revealed Zuckerberg’s new addition to his team’s moderation algorithm: Conflict control on various Facebook groups. To do this, the social media behemoth’s AI moderator will utilize several signals and cues from group conversations. These signals are programmed and patterned to indicate signs of an ongoing conflict among members. In response, the clash is immediately brought to the group moderator’s attention so that the appropriate course of action may be implemented ASAP.
It’s a bit too early to conclude its effectiveness, but the premise is already promising and an excellent representation of human and AI partnership.
Previously, human moderators frequently fell victim to experiencing burnout at work. Automoderators were yet to play a bigger role in monitoring what people share across websites and forums. Their responsibilities involved scanning for spams, sifting through thousands of profile images, and evaluating flagged posts. Overtime, speed and quality were both compromised.
With AI in the picture, reducing the volume of content that needs checking is already taken care of. In online dating sites with a large-scale market, newly registered profiles come by non-stop. Bots can detect the subjects on each profile photo and highlight any text on the description or username that violates the website’s guidelines. Humans will then follow-up on these discrepancies and approve or ban a member.
What if someone attempts to use a celebrity’s image as their account’s default photo? Again, if the AI’s stored data is not fully updated to recognize the famous person whose identity is being stolen and assumed by an internet troll, then the latter gets to roam the online community freely. Humans, on the other hand, can recognize famous individuals in a heartbeat and put a stop to unauthorized utilization of these images.
Worse, what if the automoderator mistakes a minor’s picture on a digital forum as an image of an adult? It will surely cause a scandal and put the teen’s safety in serious risk. It is crucial that humans intervene and ensure that young, underage individuals are well-protected across all digital platforms they can access.
Scammers and spammers are notorious for recycling their lines. Have you ever checked the comment section on an article shared on Facebook? Repeatedly shared paragraphs with introductions that go, “Mr. Whatsisname changed my life!” and “I never thought it would work, but…” are surefire signs of spam. Bots and non-robotic mods join forces by simultaneously detecting these comments, deleting it and if needed, banning the user for good.
For copyright infringement and scams, automoderators are programmed to determine repetitive patterns on fraudulent schemes as well as alert their flesh-and-blood counterparts of any duplicated and illegally reproduced content. Human moderators may conduct additional research, as in verify IP addresses of suspicious users and aid in locating their hideout.
Another cool way for AI and humans to team up is through shadow banning. When an individual is shadow banned on a digital group, their profile and content are not readily visible to other members. It is concealed from public viewing without them knowing. The phenomenon is effective for trolls who live for the drama. Once they find out that their (partially removed) comments are receiving zero reactions, they will soon grow tired and waste people’s time elsewhere.
Content is not the only thing that moderators can keep a keen eye on. Even user activities have no escape. Some apps and site owners hire both types of moderators to check the engagements of their registered members based on their accounts. Doing so helps prevent any abuse and misuse of the platform’s features. For instance, individuals with paid accounts may be covertly selling their profiles for extra profit. Otherwise, basic account users might try to imitate the actions of paid users to try and trick the system into giving them access to exclusive services.
In the heat of any internet misconduct, humans reach out to the violators and start dialogues to correct their errors. As a result, people will feel that there is a genuine human touch to the regulations imposed on their unacceptable content. Alternately, bots share the burden of monitoring a salad bowl of communities and member posts. The term 24/7 becomes all the more practical because once one of your non-AI mods ends their shift, AI will be there to pick things up right where the former stopped.
More importantly, if ever there are any technical difficulties encountered while applying advanced moderation algorithms, then a dependable network of experienced moderators can always take charge and fix the inaccuracy.
Is ai content moderation better than humans?
Well, it is—in some aspects, but it can never fully replace humans. That’s not to say that humans will survive without technology.
Chekkee can attest to that. Aside from employing highly skilled and carefully trained moderators, they are also adamant at exhausting the impressive feats of AI-powered content review! We combine tested-and-proven processes for checking images, videos, and texts with consistently refreshed machine learning tools. Consequently, we have earned a reputation for professionally regulating online communities, forums, websites, social media pages, and all types of UGC you can think of.
Send us an inquiry, and we’ll be happy to engage you in a more detailed discussion. If customized ai content moderation is what you need, then we’re the perfect team to assist you with that!