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Every company and their grandmom are now offering AI-powered solutions. With so many options, does it matter who you partner with for AI moderation?
When we started building our AI for moderation in 2008, machine learning was hardly applied to content moderation. Since then, others have understood the value automation brings in keeping marketplace users safe.
Every time we go to a tradeshow or conference, we see new companies with AI offers and we understand that as the market gets more saturated, it can be hard to decide which vendor to bet on.
To help you navigate the AI jungle, we wanted to highlight some specific areas where our AI is unique in the market.
It’s actionable
Many AI models work based on a sliding scale, and the output you get is a probability score. The score gives you a picture of how likely the content piece will be, whatever the algorithm is looking for. So if a content piece receives a high probability score from a model looking to detect unwanted content, there’s a good chance that the content piece falls into that category.
However, a scoring system is often arbitrary. When should you reject an item as a scam? When is the probability score 100%? 99% or maybe 85% is good enough?
Our AI doesn’t operate this way. We want to give our clients clear answers so they can apply immediately. As such, we do not send back an abstract score. Instead, our algorithm provides a concrete answer.
We operate with 3 different but clear answers that are easy to apply a moderation action to. The 3 values we expose are OK, NOK (not okay), and uncertain.
Let’s use the unwanted content model as an example. Our algorithms will look at the content and determine whether it’s unwanted. If it is, it will return “NOK,” and you should reject the content piece, if it isn’t, you will get “OK” back and accept it. If the model isn’t sure, it will send back “Uncertain”, this doesn’t happen often, but if it does, you should send the content for manual review.
That’s how simple it is. There’s no grey zone, only clear actionable answers to each content piece you run through the model.
A holistic AI approach
We believe that the value of AI is often mistakenly judged on the accuracy of the models alone. The reality is that it’s more complex than that. To explain why we need to get a bit technical and quickly outline a bit of AI terminology. (If you are interested, you can read more about the basic concepts of AI moderation here)
When evaluating an AI, there are multiple KPIs you can look at. Accuracy is just one of them. We look at a wide array of metrics to determine if our AI is performing to our standards. We can’t cover all in this article, but here are some of the most important ones.
Precision
Is a number that describes how often the model’s predictions were actually correct. If there are 100 content pieces and the machine determines 10 of them to be unwanted content, but only 8 of them are unwanted content, then the model has a precision of 80%.
Recall
Recall shows the number of unwanted pieces the algorithm correctly identifies. If we go back to our example with 100 content pieces. The AI correctly identified 8 unwanted content pieces out of the 100, but, there were 16. In this case, the recall of the model is 50% as it only found half of the unwanted content cases present.
Accuracy
Describes the number of decisions the model gets correct. Suppose we have 100 content pieces, and 16 are unwanted. In that case, the model’s accuracy will be negatively impacted by the unwanted content it fails to identify and by any good content, it wrongly identifies as bad.
This means that if a model out of 100 content pieces correctly identified 8 unwanted content when there were 16 present, and it wrongly identified 2 good content pieces as unwanted content, the model would have an accuracy of 90%.
Automation rate
Automation rate is a way to measure exactly how much of the total content volume is being handled by AI. If you have 100,000 content pieces per day, and 80,000 of them are dealt with by the models, then you have an automation level of 80%
When judging how well AI works, we believe it needs to be based on how it performs in relation to all these 4 metrics, as that will give you a truer picture of how well the AI is dealing with the content challenges.
You can never simultaneously have perfect accuracy, precision, recall, and automation. Our AI is unique in calibrating to meet your business objectives and find the right balance between these indicators.
Supervised and continuous learning
Machine learning models can be taught in different ways; how they are taught greatly impacts how well they perform.
Our AI is trained on structured and labeled data of high quality. This means that the data sets we train our models on have been reviewed manually by expert content moderators who have taken a yes or no decision on every piece of content.
We also update the models regularly, ensuring they are updated and adhere to new rules and global changes or events that could impact moderation decisions.
A calibrated solution
One of the benefits of designing our AI with an eye on multiple metrics is that we can tailor-make a solution to ensure the perfect fit for your business.
We can pull multiple levers to adjust the output, allowing us to tweak accuracy and automation, ensuring that everything is calibrated as your business requires.
Our solution’s accuracy and degree of automation are elastic, making our AI setup much more flexible than other available options.
Adaptive AI solution
One of the few drawbacks of Machine Learning is that it’s rigid and static. To change the model, you need to retrain it with a quality dataset. This makes it hard for most AI setups to deal with sudden policy changes.
We‘ve solved this problem by deeply integrating it into our content moderation tool Implio. Implio has a powerful filter feature that adds flexibility to the solution, so you can quickly adapt to change.
For example. when a new iPhone comes out, the AI models will not pick up the new scams until it has been trained on a new dataset, including them, but you can add filters in Implio until there’s time to update machine learning. The same is true for other temporary events like the Olympic Games or global disasters, except that these are over so quickly that it’s likely impossible to update the models. Instead, you can add Implio filters that ensure high accuracy even during times with special moderation demands.
In addition, we have a team dedicated to studying moderation trends and best practices. All our AI customers benefit from their knowledge and our 16 years of experience to support and guide them.
ML Tailored to content moderation
Most of the AI solutions on the market were created to solve a general problem in multiple industries. This means that AI works okay for most companies, but it’s never a perfect fit.
We took the other route and dedicated our efforts to creating an AI that’s perfect for content moderation.
When we develop our AI, we do it based on 20+ years of experience helping companies of all sizes keep their users safe and high-quality in their user-generated content. That has made our stack uniquely tailored to content moderation, ensuring unparalleled results in our field.
We also have a team of experts supporting our AI developers with insights, internal learnings from moderating global sites of all sizes, and research into industry trends and the challenges faced by online marketplaces and classifieds in particular.
Our research team feeds their insights to Besedo as a whole, ensuring a high level of expertise at every level of our organization, from moderation agents to managers and developers. This ensures that our experience and expertise are infused into all our services and products.
Get an AI solution that fits your needs
There is no question about it; AI will play a huge role in marketplace growth over the next few years. However, to truly benefit from machine learning, make sure you get models that will work well for you.
We often talk to marketplace owners who have become slightly disillusioned after testing AI solutions that weren’t properly calibrated for their businesses. They have wasted time implementing a solution that didn’t properly solve their issue, and now they are wary of AI as a whole.
That’s a shame. When applied correctly, AI is a great money saver and provides other benefits like fast time to site and user privacy protection.
To avoid spending money on the wrong AI, chat with our solution designers, who will give you a good idea of which setup would work for you and the results you can expect. Together you can tailor a solution that fits your exact needs.
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