University of Kwazulu-Natal

Fact Checking In The Age Of Ai Reducing Biases With Non Human

Bonisiwe Shabane
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fact checking in the age of ai reducing biases with non human

As social media has grown into many people’s primary news source, so has its potential for misinformation. Facebook and X have both launched fact-checking tools to combat so-called fake news. Yet many users dismiss the fact-check itself as false, especially when it challenges their preexisting views. Could an AI fact-checker seem more objective and help change minds? Science communicators who strive to share information may find that fact-checking does little to sway opinion on politicized topics. Partisan views on issues such as climate change and COVID-19 can distort readers’ perceptions and make them more likely to accept false information and reject any fact checks.

Won-Ki Moon, University of Florida College of Journalism and Communications Advertising assistant professor and University of Texas at Austin Professor Lee Ann Kahlor examined partisan bias in how people process false information about science... They examined whether one’s political affiliation affected their perception of fact-checking and compared AI-generated fact checkers to traditional human ones. Could AI, which is ostensibly less biased, help viewers challenge their pre-existing beliefs and consider that the false information might actually be false? Fact-checks often fail because they trigger cognitive dissonance, a phenomenon in which an individual feels uncomfortable when faced with information that challenges deeply held beliefs. As certain science topics have become politically loaded, one’s partisan identity feeds into how they perceive these issues. They are more likely to accept information that supports their preferred political party.

Corresponding author a.hamed@sanoscience.org Corresponding author xwu@zhejianglab.com This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). As the influence of transformer-based approaches in general and generative artificial intelligence (AI) in particular continues to expand across various domains, concerns regarding authenticity and explainability are on the rise. Here, we share our perspective on the necessity of implementing effective detection, verification, and explainability mechanisms to counteract the potential harms arising from the proliferation of AI-generated inauthentic content and science. We recognize the transformative potential of generative AI, exemplified by ChatGPT, in the scientific landscape.

However, we also emphasize the urgency of addressing associated challenges, particularly in light of the risks posed by disinformation, misinformation, and unreproducible science. This perspective serves as a response to the call for concerted efforts to safeguard the authenticity of information in the age of AI. By prioritizing detection, fact-checking, and explainability policies, we aim to foster a climate of trust, uphold ethical standards, and harness the full potential of AI for the betterment of science and society. Subject areas: Biocomputational method, Bioinformatics, Biological sciences, Computational bioinformatics, Natural sciences, Neural networks, Artificial intelligence, Artificial intelligence applications 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. Fact-checking in the age of AI: Reducing biases with non-human information sources The Imperative of Fact-Checking in the Age of AI: Protecting Credibility in Communications In today’s rapidly evolving digital landscape, the proliferation of artificial intelligence (AI) has brought about a dramatic shift in content creation and dissemination.

While AI offers remarkable capabilities in summarizing vast amounts of information and generating text, it also presents unprecedented challenges in maintaining accuracy and combating misinformation. For communications and public relations professionals, the stakes have never been higher. A single inaccurate fact, whether propagated by AI or human error, can quickly escalate into a reputational crisis, eroding public trust and undermining carefully crafted narratives. This article explores the critical importance of rigorous fact-checking in the age of AI and provides practical strategies for safeguarding credibility. One of the most significant pitfalls of AI-generated content is its potential for "hallucinations," instances where the AI confidently presents fabricated information as fact. From attributing inventions to the wrong historical figures to generating plausible yet entirely false statistics, AI can easily mislead those who rely on it without critical evaluation.

This is compounded by the speed and volume at which AI can produce content, making manual verification a daunting task. Furthermore, the sophisticated nature of some AI-generated text can make it difficult to distinguish from human-written content, increasing the risk of misinformation slipping through the cracks. For communications professionals, this presents a significant challenge, as the pressure to produce timely and engaging content can sometimes overshadow the need for meticulous accuracy. The first line of defense against misinformation is to critically evaluate any study or research cited. The phrase "a recent study shows…" should never be taken at face value. It’s essential to delve into the methodology of the study, scrutinizing the sample size, the data collection methods, and, crucially, the funding source.

Studies funded by organizations with vested interests in the outcome can be susceptible to bias, either consciously or unconsciously. Always seek out the original research paper rather than relying on summaries, press releases, or media coverage, as these can oversimplify, misinterpret, or selectively present the findings. By understanding the nuances of the research, communications professionals can avoid propagating misleading or inaccurate information. Statistical data, often used to lend weight and credibility to arguments, can be easily manipulated or misinterpreted. "Zombie statistics," debunked figures that continue to circulate, are a common pitfall. The ubiquity of online information makes it easy for outdated or inaccurate statistics to persist, especially when they serve a particular narrative.

Therefore, before citing any statistic, it’s crucial to verify its accuracy using reputable fact-checking resources, searching for the statistic alongside terms like "debunked" or "fact-check." If the statistic cannot be traced back to a... This meticulous approach ensures that the information presented is not only accurate but also demonstrably reliable, strengthening the credibility of the communication.

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