University of Kwazulu-Natal

Can Ai Save Truth Exploring Ethical Pathways For Fact Checking In The

Bonisiwe Shabane
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can ai save truth exploring ethical pathways for fact checking in the

At the 2025 Milton Wolf Seminar, panel discussions tackled one of the urgent questions of the digital age: How can truth be verified in a world where its boundaries are increasingly blurred? Against a backdrop of widespread disinformation, increasing polarisation and declining public trust, the very notion of truth itself has become contested. In such an environment, fact-checking faces a dual challenge: not only is it harder to agree on what qualifies as truth, but disinformation now spreads with unprecedented speed and scale, outpacing traditional methods of... Amid these challenges, large language models (LLMs) have emerged as both a source of the problem and, paradoxically, a potential part of the solution. LLMs are advanced generative artificial intelligence (AI) systems trained on large amounts of internet data to generate human-like output. On the one hand, LLMs can produce convincing falsehoods rapidly and at scale, exacerbating the spread of disinformation.

On the other hand, its advanced capabilities might also be harnessed to detect, counter and even stop disinformation. This raises a critical question: Could generative AI, despite its risks, become an ally in the fight for truth? The 2025 Milton Wolf Seminar placed a strong emphasis on the dangers posed by AI, such as how it can produce disinformation and mislead individuals. This blog post explores an alternative angle. Instead of viewing AI only through the lens of risk, it asks whether this technology might also serve as part of the solution. In this blog post, I will first discuss the evolution of fact-checking and how it adapted to the changing information ecosystem.

Next, I will examine the challenges fact-checkers face today, especially the scale and speed of disinformation. I will then turn to LLMs, to consider whether this technology can help support the fact-checking process. Finally, I will reflect on how LLMs might strengthen the ongoing fight for truth. Disinformation itself is not new, but social media has profoundly transformed how quickly and widely it spreads. Platforms such as Facebook and X (formerly Twitter) have redefined how citizens consume information. While democratising access, they have also created unregulated and minimally controlled spaces where disinformation can rapidly proliferate (Wittenberg & Berinsky, 2020).

Unlike traditional journalism, where media professionals served as gatekeepers and information had to pass through institutional filters before reaching the public, social media platforms allow anyone to publish and share content instantly, without any... This has given rise to retroactive gatekeeping: a form of fact-checking that involves verifying the accuracy of claims after they have already begun circulating online (Singer, 2023). In the age of misinformation, AI-powered fact-checking tools offer a glimmer of hope for restoring truth and accuracy to public discourse. These tools can process vast amounts of information at unprecedented speeds, potentially identifying and flagging false or misleading claims faster than any human team. However, the development and deployment of these technologies raise crucial ethical considerations, particularly concerning bias and transparency. Ensuring these tools are used responsibly and ethically is paramount to their success and widespread acceptance.

One primary concern revolves around the potential for bias in AI-powered fact-checking systems. These systems are trained on large datasets, which can reflect existing societal biases. If the training data contains skewed information or underrepresents certain perspectives, the resulting AI model can perpetuate and even amplify these biases. This can lead to inaccurate fact-checking, potentially unfairly targeting specific groups or viewpoints. For instance, an AI trained predominantly on data from Western sources might misclassify information rooted in different cultural contexts as false or misleading. Furthermore, the algorithms themselves can introduce bias through their design and the choices made by their developers.

Addressing this challenge requires careful curation and auditing of training datasets, as well as continuous monitoring and evaluation of the AI’s outputs to identify and mitigate potential biases. Researchers are actively exploring techniques like adversarial training and explainable AI (XAI) to make these systems more robust and less susceptible to bias. Building diverse and inclusive teams of developers is also crucial to ensure a broader range of perspectives are considered during the design and development process. Transparency is another critical ethical consideration for AI-powered fact-checking. Users need to understand how these systems arrive at their conclusions to trust their judgments. A "black box" approach, where the internal workings of the AI remain opaque, undermines public trust and can fuel suspicion.

This lack of transparency can also hinder accountability. If an AI system makes an error, it’s difficult to identify the source of the problem and rectify it without understanding the system’s logic. Therefore, developers should strive to create explainable AI models that provide insights into their decision-making processes. This could involve revealing the sources used for verification, the specific criteria used to assess the veracity of a claim, and the confidence level of the AI’s assessment. Furthermore, independent audits and peer reviews of these systems are essential for ensuring their accuracy and reliability. Open-sourcing the code, where feasible, allows for broader scrutiny and can help identify potential vulnerabilities or biases more quickly.

By prioritizing transparency, developers can build trust in AI-powered fact-checking tools and pave the way for their wider adoption as valuable resources in the fight against misinformation. The line between truth and misinformation has become distressingly blurred. The age of connectivity has brought with it both unprecedented access to knowledge and an unparalleled ability to spread falsehoods. Against this complex backdrop, Artificial Intelligence (AI) emerges as both a tool of hope and a subject of scrutiny. Truth-checking systems, built on AI technologies, present a promising solution to the challenge of managing credibility and misinformation. However, they also raise profound ethical questions about bias, accountability, and the potential misuse of AI.

Misinformation, disinformation, and so-called "fake news" have become widespread phenomena. From the undermining of public health campaigns to the manipulation of political processes, the consequences of false information are dire. Social media platforms and the web's boundless reach exacerbate this problem, creating echo chambers where inaccuracies are not only disseminated but also reinforced. In response, the need for truth-checking and credibility assessment systems has never been more urgent. The question is no longer whether we can verify facts, but how we can do so effectively in an environment where information travels at lightning speed. AI-powered truth-checking systems rely heavily on Natural Language Processing (NLP), a branch of AI that enables machines to understand, interpret, and generate human language.

NLP algorithms can swiftly analyze vast volumes of textual content, identifying claims, comparing them against verified data sources, and assigning a credibility score. Tools like OpenAI's GPT models and Google's BERT have been instrumental in advancing these capabilities. For instance, when an unverified claim appears in an online news article or social media post, AI systems can cross-reference it with a database of trustworthy information sources, such as scientific journals or official... This process, which would be laborious for humans, can be achieved in seconds through AI. The Rise of AI Fact-Checking: How Machines Are Helping Us Separate Truth from Fiction In an age where misinformation spreads faster than wildfire, the need for accurate, reliable fact-checking has never been greater.

From social media rumors to manipulated news headlines, false claims can sway public opinion, damage reputations, and even endanger lives. Enter artificial intelligence—a tool that’s quietly revolutionizing how we verify information. But how exactly does AI fact-checking work, and can we trust machines to distinguish truth from lies? Let’s dive in. At its core, AI fact-checking relies on algorithms trained to analyze vast amounts of data, identify patterns, and cross-reference claims against trusted sources. Here’s a simplified breakdown of the process:

1. Claim Detection: AI scans text, audio, or video content to identify statements that need verification. For example, if a viral tweet claims, “Eating chocolate cures COVID-19,” the system flags it as a potential claim to investigate. 2. Source Analysis: The AI checks the credibility of the source. Is it a peer-reviewed study, a government website, or an obscure blog?

Context matters. 3. Cross-Referencing: Using databases like academic journals, official reports, and fact-checking archives (e.g., Snopes or PolitiFact), the algorithm compares the claim against established facts. 4. Contextual Understanding: Advanced natural language processing (NLP) helps AI grasp nuances like sarcasm, hyperbole, or cultural references that might trip up simpler systems. 5.

Confidence Scoring: The AI assigns a score indicating how likely a claim is to be true, false, or somewhere in between. Think of it as a supercharged librarian who can read millions of books in seconds and spot inconsistencies with eerie precision. arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community?

Learn more about arXivLabs. Highlights the arms race between truth-enforcing and truth-bending AI systems. Promotes student involvement in shaping ethical verification tools. In today's era, where information is abundant yet often unreliable, the struggle for truth has become a defining hallmark of our times. The advent of Artificial Intelligence (AI) has introduced unprecedented capabilities for both verifying and falsifying information. As we navigate this new frontier, the battle between AI fact-checkers and AI-powered misinformation has escalated into a veritable arms race.

This article explores the implications, challenges, and opportunities associated with this dynamic, highlighting the pivotal role of students and emerging thought leaders in shaping ethical verification tools within this technological milieu. In 2016, Facebook initiated its fact-checking program to combat rampant misinformation. Partnering with independent fact-checkers, the platform utilizes algorithms that assess data credibility. However, the efficacy of this initiative is continually challenged by the surge of AI-fueled disinformation campaigns—exemplifying the ceaseless tug-of-war between verification and deception. Analogous to the development of chemical warfare during World War I, the emergence of AI tools for misinformation is reminiscent of the dual-edged sword of human innovation—where advancements in one domain, fraught with ethical... Just as nations raced to develop chemical agents and antidotes, the tech world engages in a concurrent struggle for information integrity amidst evolving deception tactics.

Many assume that deploying AI will inherently lead to accurate fact-checking. However, bias in training data, algorithmic opacity, and an inherent lack of context can lead AI systems to misidentify truths and perpetuate inaccuracies. The advent of artificial intelligence in the domain of fact-checking represents a significant shift in how information veracity is assessed and disseminated. At its core, AI-driven fact-checking involves deploying algorithms and machine learning models to analyze vast quantities of data → text, images, video, and audio → to identify patterns indicative of misinformation or disinformation. This automation offers the promise of speed and scale, addressing the overwhelming volume of content generated in the digital age, far exceeding human capacity for manual verification. However, the integration of AI into this critical function introduces a complex array of ethical considerations that warrant careful examination.

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