A perspective on Fact-Checking, Technology and AI

In an era of fake news and misinformation, AI isn’t just a tool – it’s essential. Read on to find out how artificial intelligence is altering the way we verify facts, lectures, claims, and more.

Do you remember the days when we could settle a debate just by pulling up Google? Those were much simpler times. Nowadays, the world needs extensive fact-checking, beyond quick searches and surface-level verification.  

How AI can help verify the Truth

You may have been in a situation where you hear someone make a claim – “Pluto is still a planet,” or “Humans only use 10% of their brain.” Perhaps you’ll find a definitive answer through a Reddit thread, a government website, or perhaps nothing conclusive.  

But picture this – the claim comes from a live presidential debate or a popular health influencer with millions of viewers. Now that is a different league altogether – one that manual verification alone will not suffice. 

Enter Artificial Intelligence (AI), with its scale and speed, AI can step in and do what humans cannot: spot falsehoods, work around the clock, and go through numerous sources in real time. 

Why does AI Fact-Checking Matter? 

There’s a saying that misinformation (false information) spreads at the speed of light, but the truth will follow eventually. The challenge is that with the immense volume of content available online, fact-checkers are currently drowning in the workload.

According to Duke Reporters Lab, 443 fact-checking projects are active in approximately 116 countries in more than 70 languages. Unfortunately, Meta has recently announced the closure of third-party services in the U.S., causing rising concerns among the community.  

Artificial intelligence can be a powerful ally that can help us flag inconsistencies behind the scenes.

How does AI Fact-Checking Work? 

Below are some ways that AI can be used in modern society for automated fact-checking (AFC). 

From study: https://direct.mit.edu/tacl/article/doi/10.1162/tacl_a_00454/109469/A-Survey-on-Automated-Fact-Checking 

Step 1: Speech to Text 

Whether it be a university professor giving a lecture or even your favourite drama series, the system will listen to the audio and then transcribe it into words in real time. 

Step 2: Claim Extraction 

Next, the system will identify factual statements from the text – for example, “Ice cream was invented in 1976” or “The Amazon is the world’s longest river”. These statements will be separated for further analysis. 

Step 3: Cross-Referencing 

The claims are compared against trusted databases such as research journals, historical records, news archives, and more. Cross-referencing checks whether the claim can hold up to what it stated or was misleading in any way. 

Step 4: Fact Report

The system produces a report, showing which claims are supported, may be questionable, or completely disputed. Essentially, a confidence score of the statements will be given, all while the event is ongoing. 

The next stage involves the human-in-the-loop (HITL) approach. A team of expert fact-checkers will review the report and conduct advanced checks based on unverified or false claims, as these areas require knowledge of cultural nuance and context. 

Technology behind AI systems  

Advances in technology have helped AI become powerful – it’s not magic, it’s science. Here are some of the technologies powering artificial intelligence systems:

• Machine Learning: Trains algorithms to learn from data and make decisions without being programmed to – 3 main categories, such as supervised and unsupervised learning, along with reinforcement learning. 
• Deep Learning: Uses neural networks, which works similar to the human brain. Neural networks are composed of multiple layers, and is used to discover patterns within large datasets without needing feature extraction. 
• Natural Language Processing (NLP): This is another core component, and allows AI to understand and interpret human language. NLP powers chatbots to voice assistants, and text-based chats, allowing for interactive communication. 

• Computer Vision: As the name suggests, computer vision allows systems to see the world by analyzing visual information – enables tasks like facial and image recognition. 

• Robotics: Related to the design and manufacturing of machines that can replicate human actions. Robots can carry out physical tasks with or without human guidance and widely used in industries from manufacturing to healthcare. 

Benefits of AI in Fact-Checking

It isn’t about catching lies; it’s to make the truth more accessible. When used responsibly, these systems can help to empower decision-making and reshape the consumption of public information. A few such advantages are listed:

• Speed and Scalability: The speed at which machine learning systems can analyze information is unbeatable – hundreds to thousands per second. This scale is almost impossible for mere human effort. 

• Cognitive Offloading: Offloading repetitive tasks to intelligent systems allows teams to focus on more complex investigations, leading to increased productivity. Task delegation is not a new concept; utilising these tools can speed up the process immensely.     

• Media Monitoring: Unlike traditional practices, artificial intelligence can monitor media simultaneously, from podcasts to live streams. It acts as an early warning scheme, catching misinformation before it gains traction. 
• Public Literacy: Embedding real-time fact-checking into national news channels or even online content platforms can help train the general audience to question claims or seek out credible sources. Improving public literacy is a step towards a more resilient generation, especially with rising misinformation. 

A study on Artificial Intelligence and Disinformation by Katarina Kertysova noted:

“AI-based moderation risks ‘overinclusion’—where truthful content is wrongly flagged. Worse, poorly trained models may replicate human biases, posing ethical issues around censorship and fairness. This is why hybrid AI-human systems are currently the safest path forward.” 

Limitations in AI Fact-Checking 

Although artificial intelligence systems bring power and speed, they are prone to hallucinations due to the data fed into the training system. Intelligent systems cannot distinguish the intricacies between biased and non-biased data, language, and context – it’s far from ideal. 

Here are some of the pressing challenges with AI fact-checking: 

• False positives: Though millions of claims can be processed quickly, AI could flag legitimate content incorrectly due to a lack of context, particularly in topics like sarcastic jokes, cultural idioms, double meanings, metaphors, etc This leads to a rise of false positive claims.  
• Bias Replication: Algorithms that are trained on skewed data can end up amplifying social or political biases – for instance, gender bias figures in recruitment. This can result in accidental censorship of certain content and potentially discriminate against underrepresented minorities. 

• Linguistic Gaps: A lot of current programs are trained heavily on English datasets, which leaves a huge knowledge gap in other languages and/or dialects. The lack of good-quality training sets limits the effectiveness in diverse situations, as disinformation can take on different forms. 

The Road Ahead 

We’re increasingly moving towards a future where automated fact-checking becomes seamlessly integrated into our consumption habits. Here is what’s next: 

• Structured Data: Systems will rely more on curated claim databases – essentially massive libraries of previously fact-checked statements. When new content is analyzed, AI can match it to a known claim and provide the verdict immediately. 

• Real-Time Integration: Picture watching a live news event, and you can see claims verified on-screen as they’re said. Plugins or widgets could soon be embedded into browsers, news sites, or even YouTube moderation tools to provide instant fact-checking on the spot. 
• Human-AI Collaboration: The full extent of AFC is still out of reach; however, it can get us most of the way by pinpointing likely issues, while human teams review high-risk claims. This hybrid model will continue to be the future standard. 
• Global Standardization: To enable functions across countries and the various languages, there must be shared taxonomies as the source/evidence. Certain initiatives like ClaimReview and MediaReview are building the groundwork for machine-readable, multilingual ecosystems. 

Final Thoughts 

In today’s generation, where information is at hand, disinformation and fake news proliferate faster than you can imagine. For humans, Artificial intelligence is merely a tool for us to level the playing field. In the end, we have to use our sound judgment to determine what’s real and what’s fake. 

The future of fact-checking isn’t passive – it’s proactive, real-time, and AI-powered. For the organizations, media, and governments that embrace it will be the ones leading the next wave of trust in digital content.