Fact Checking
AI Fact-Checking: What is a Fact Checker?
AI content booming, but is it true? Discover AI fact-checking and how to ensure your content's credibility.
AI-generated content is becoming more and more prevalent. From news articles to academic papers, the speed at which AI content is developing makes it more challenging than ever to determine what’s human-produced and what’s written by machine. This creates its own set of obstacles, mainly when it comes to trust, credibility and originality.
That’s where fact-checkers come in. A fact checker can be a person or even a platform whose purpose is to validate and verify information while preserving the integrity of the details. But how do you go about checking AI-generated content? Let’s take a closer look:
Before you can effectively check AI content, you have to understand the basics of the technology behind it. When working in a digitally saturated environment, you need to approach AI fact checking with a comprehensive plan of action. At no other time has technology, ethics and information accuracy come together quite like this, which means knowing what you’re working with and moving forward from there.
The first step in being able to fact-check AI writing is to understand the models that are used to generate that writing. That manes familiarizing yourself with popular models like GPT (Generative Pre-trained Transformer) and BERT (Bidirectional Encoder Representations from Transformers). Different AI models have different strengths and weaknesses as well as limitations and potential biases based in the information that they are trained on and how they are programmed and developed.
For example, Originality.AI’s own fact checking tool was developed using a variety of models including GPT-2, 3 and 4 – not just the very latest ones, to provide it with a more comprehensive set of diagnostics for deeper, more accurate fact-checking. capabilities.
Even when you’re familiar with the different models, however, your understanding of them shouldn’t stop, because their development certainly won’t. Machine learning workshops, webinars and conferences can help you better understand the different methodologies and approaches that companies and platforms are taking with respect to AI and can help you stay informed about how they plan to move forward.
As AI-written content proliferates on the web, a surefire way to build immediate credibility with your readers is to clearly label which content is AI-generated and which is not. This lets consumers be in control of how they choose to consume the content (or if they do) as well as make their own judgements about its veracity.
It’s also a good idea to incorporate options that let readers give you feedback on AI-generated content. Having two-way, open communication fosters trust and allows you to build deeper, more resilient relationships with your target audience.
Beyond simply labeling AI-generated content, having a code of ethics that clearly spells out how AI content is handled in terms of transparency, accountability, fairness and responsible use will go a long way toward building credibility and trust. In addition, requiring regular audits of the content ensures that you’re aligned with the guidelines set forth by your company or organization and are ready to make adjustments as new developments emerge.
Outside of the tech sector, AI is still viewed with a bit of mystery, skepticism and uncertainty.. Companies, news organizations and publishers alike are treating it as everything from a marketing jackpot to a scary, nebulous, job-eating cloud. When people are better informed about the nuances of AI-generated content and know what to look for, it helps to demystify the role of AI and help present it as what it is, just another tool in a long list of developments in communication and the sharing of information.
Discerning the difference between AI-generated and human-written content is a big challenge. But when bias is thrown into the mix, it can change how information is perceived, skew results and create unintended consequences. With that in mind, fact-checking platforms and programs used to check AI writing must have a firm basis in understanding what bias is and how to confront it.
Just like with the overall development of AI, ways to identify and handle bias are changing and shifting rapidly. When it comes to human writing, bias from our life experiences has a tendency to color and shape our perception of the world around us. AI has no such life experience, however, the data that it is trained on can create a similar inherent effect. If the information that the AI is fed has its own data skewed or misrepresented, it can take those inherent biases and magnify them.
For this reason, it’s incredibly important to keep a “human in the loop” who is aware of conscious and unconscious biases and can eliminate them so that the resulting content is as factual and valid as possible.
It wouldn’t be nearly as difficult to spot and snuff out biases in AI if there were some all-encompassing, tell-tale signs that clearly point out what’s biased and what isn’t. Unfortunately, there are several different types of biases and it can be hard to root them out. These include:
Selection Bias: This type of bias happens when the data used to train the AI isn’t representative of the broader population. This can create models that are skewed toward particular perspectives or groupthink. For example, a financial lending AI that is only trained on data from urban customers might incorrectly assess the creditworthiness of rural customers because it doesn’t understand the challenges and financial behaviors of a rural population.
Confirmation Bias: This is when an AI (or the person training it) prefers information that confirms their existing beliefs or biases. This is a particularly dangerous form of bias when it comes to AI fact-checking since it leads people (and thus machines) to overlook evidence that points to the contrary.
Cultural Bias: When AI models are given data that primarily comes from one culture or another, they may miss important nuances from other cultures.
Gender and Racial Bias: This can cause AI to make decisions taking one’s gender or race into consideration which can lead to discriminatory outcomes.
The challenge of dealing with AI bias isn’t something that should be put squarely and only on the user’s shoulders. It should be a collaborative effort between users, developers, innovators, engineers and everyone involved at every step of the process. With that in mind, developers could, for example, make sure that the data used to train their AI is diverse and representative of the groups it is assisting.
Incorporating things like regular audits and feedback loops can help ensure that the AI platform is performing as expected and if it isn’t, give users a way to report biased outputs or decisions that can help improve the model over time.
But how exactly do we then carry this over to AI fact-checking?
If the fact-checking is done purely from a human standpoint, it helps to be introspective of one’s personal biases. Trainings and workshops can help in this regard to untangle any unconscious biases that might seep into the programming. In addition, just like academic works are peer-reviewed for accuracy, it can help to incorporate such a system for content verification too, helping to balance out biases and gain valuable and diverse perspectives.
Understanding all of these challenges and how they feel into the training, development and deployment of AI platforms and programs then leads us to the heart of the question: How do you go about fact-checking AI-generated content?
The first step is to use a trusted AI content verification tool, like Originality.AI’s own fact checking AI tool. Although there are free AI content checker programs available, by using a platform like Originality.AI, you benefit from the latest developments and technology that’s released concurrently with AI, always staying in-step with new shifts, discoveries and releases.
Beyond using a dedicated platform that understands how to tell apart AI-generated content from human content, there are several other steps you can take to fact check AI content, including:
If you’re concerned about AI influence in your fact-checking, always refer back to the original tool. If a piece of content references another study or report, check that source for authenticity. In addition, check the credentials and the background of the authors or contributors. Real authors will have a digital footprint, including other publications, a social media profile or belong to professional groups that lend to their credibility.
There are several different tell-tale signs of human writing in the piece you’re looking at. Although AI content is becoming more and more sophisticated, it often lacks the depth and nuance of human writing, to say nothing of the emotional clues. AI also doesn’t (yet) have the ability to remain more or less consistent in tone. If a piece sounds more like a patchwork of different sources, it could be a clue to AI generation.
It may seem like overkill, but depending on the level of accuracy needed for the AI fact check, it can be a good idea to verify information across many reliable sources. If the content or fact aligns across different platforms and checks out consistency, chances are, it’s true.
Just like bias can be inherent in human fact-checking, and erroneous information can cause AI to share misinformation or inaccuracies, the best possible outcome when it comes to check for AI content veracity is to develop your own set of data-driven fact-checking skills concurrently alongside the AI.
That means running the content through a trusted and reliable platform like Originality.AI’s fact checking tool, but also developing your own digital forensics skills and statistics literacy. Data can and often is misrepresented at times, and having a basic knowledge of how statistics work can help you spot inaccuracies. In addition, knowing how to use metadata, reverse image searches and other digital tools can help you verify digital content beyond what an AI can do.
In addition, being able to spot decidedly human writing traits including the use of tone and style, can go a long way in helping to verify AI generated content. Recognizing the biases inherent in both the training data and in our own naturally human experience also helps us to become better content writers, publishers and stewards of the information we share. Beyond that, it’s important to always cite your sources properly and give credit where credit is due while being transparent and ethical with the use of AI. Remember that AI is just a tool and shouldn’t be used to replace human creativity but rather add to it.
There are many more points to take into consideration beyond just knowing what a fact-checker is and how it works in the age of artificial intelligence. For example, fact checking for academics has its own set of rules, and the stakes are incredibly high when it comes to checking for not just plagiarism but AI content as well. Knowing how to check AI generated content and being able to fact-check that content is a must.
Another point worth its own discussion is Facebook. Social media giants play a vital role in how information is disseminated and shared and their algorithms often prioritize content engagement over content truth, which can lead to misinformation spreading like wildfire. Understanding Facebook’s role in truth (as well as other large social media platforms) is vital to fact-checking endeavors, especially in an era of breaking news, political elections, natural disasters and other timely events that need to be checked for authenticity and truthfulness.
All of these pieces of the greater whole help to answer the broader question of “What is a fact checker?” but are far too exhaustive to discuss in one very long post. We invite you to learn more about the fact-checking process and AI fact-checking by browsing our other articles on the topic. Try Originality.AI’s own fact-checker for yourself and check for plagiarism, AI detection, readability and more in one seamless, easy-to-use platform.