Genai Vs Human Fact Checkers Accurate Ratings Flawed Rationales

Bonisiwe Shabane
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genai vs human fact checkers accurate ratings flawed rationales

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Last week we were thrilled to host Dr. Yuehong Cassandra Tai, assistant research professor and assistant director at C-SoDA, for a talk on, "GenAI vs. Human Fact-Checkers: Accurate Ratings, Flawed Rationales." Dr. Tai presented new research evaluating how well generative AI models assess the credibility of political content, specifically, content in posts shared by subnational U.S. politicians on Facebook. While some GenAI models can come close to (though never match) the performance of human fact-checkers in identifying low-credibility content, they often rely on surface-level features—like tone or source cues—rather than true reasoning about...

This work is forthcoming at WebSci 2025, and is available on ArXiv: https://lnkd.in/eKBb6VyE #GenAI #FactChecking It was a pleasure to share my research with the CSoDA community. I enjoyed the discussions with students and social scientists. The CSoDA Weekly Speaker Series offers an excellent platform for exchanging ideas on how computational approaches are advancing our understanding of both social science and the humanities. A timely warning for social scientists to use AI smartly but cautiously! Employers and policymakers alike are aware of the potentially earth-shattering ramifications of AI for the European labor market.

At our #POLITICOCompetitiveSummit, Ben Munster spoke to two labor experts on the issue. Here are his takeaways 👇 Follow along: https://ow.ly/kpZP50X5Gu2 Full room at the Bias and Fairness session (1/3 sessions), I had the pleasure of chairing on Tuesday at ECMLPKDD It was great to see fairness included as part of the main conference program... and combine papers on fairness and discrimination in AI systems with the broader topic of bias (not always directly linked to fairness, but touching model robustness more broadly). The session featured: - A paper on class imbalance learning introducing a new loss function, Jensen–Tsallis Divergence, presented by David Luengo García - A paper on overconfident predictions and how to overcome this issue,... Paper preprints are available online: https://lnkd.in/dveGWa-9 in case you want to learn more #AI #bias #fairness #ECMLPKDD25

Generative AI (GenAI) has revolutionized content creation, offering unprecedented speed and scale. From crafting blog posts to generating marketing copy, its potential seems limitless. However, beneath the surface of impressive text generation lies a critical vulnerability: the accuracy and reliability of the information it produces. While GenAI excels at mimicking human-like writing styles and optimizing for SEO, its dependence on vast datasets without inherent fact-checking mechanisms makes it prone to errors, biases, and even outright fabrication. This article delves into the challenges of accuracy in GenAI content, highlighting the indispensable role of human oversight and robust fact-checking processes. GenAI models are trained on massive datasets scraped from the internet.

This allows them to learn patterns, grammar, and stylistic nuances, enabling them to generate seemingly original content on a wide range of topics. The ability to quickly produce drafts, brainstorm ideas, and translate languages is undoubtedly attractive. However, the very nature of this training data introduces inherent risks. If the data contains inaccuracies, biases, or outdated information, the GenAI model will likely perpetuate these flaws in its output. One of the most significant challenges is the phenomenon of “hallucination,” where GenAI confidently presents false or misleading information as fact. This isn’t deliberate deception; rather, it stems from the model’s attempt to create coherent and plausible narratives based on the patterns it has learned.

The model might fill in gaps in its knowledge with fabricated details, present outdated information as current, or even invent sources and citations. This can be particularly problematic in fields requiring high accuracy, such as journalism, scientific writing, and legal documentation. When comparing GenAI-generated content to human-written content, several key differences emerge, particularly regarding accuracy and overall quality. Human writers, especially those with expertise in a specific field, bring critical thinking, contextual understanding, and the ability to verify information through reliable sources. They can assess the credibility of sources, identify biases, and synthesize information to create accurate and nuanced content. GenAI, on the other hand, lacks this inherent ability.

It relies on statistical probabilities rather than genuine understanding, making it susceptible to errors. A human writer is trained to research and back up claims, GenAI is trained to statistically predict the next word in a sequence based on its training data. A human always trumps this when it comes to accuracy. Abstract:Despite recent advances in understanding the capabilities and limits of generative artificial intelligence (GenAI) models, we are just beginning to understand their capacity to assess and reason about the veracity of content. We evaluate multiple GenAI models across tasks that involve the rating of, and perceived reasoning about, the credibility of information. The information in our experiments comes from content that subnational U.S.

politicians post to Facebook. We find that GPT-4o, one of the most used AI models in consumer applications, outperforms other models, but all models exhibit only moderate agreement with human coders. Importantly, even when GenAI models accurately identify low-credibility content, their reasoning relies heavily on linguistic features and ``hard'' criteria, such as the level of detail, source reliability, and language formality, rather than an understanding... We also assess the effectiveness of summarized versus full content inputs, finding that summarized content holds promise for improving efficiency without sacrificing accuracy. While GenAI has the potential to support human fact-checkers in scaling misinformation detection, our results caution against relying solely on these models. by jsendak | Feb 24, 2025 | AI | 0 comments

arXiv:2502.14943v1 Announce Type: new Abstract: Despite recent advances in understanding the capabilities and limits of generative artificial intelligence (GenAI) models, we are just beginning to understand their capacity to assess and reason about the... We evaluate multiple GenAI models across tasks that involve the rating of, and perceived reasoning about, the credibility of information. The information in our experiments comes from content that subnational U.S. politicians post to Facebook. We find that GPT-4o, one of the most used AI models in consumer applications, outperforms other models, but all models exhibit only moderate agreement with human coders. Importantly, even when GenAI models accurately identify low-credibility content, their reasoning relies heavily on linguistic features and “hard” criteria, such as the level of detail, source reliability, and language formality, rather than an understanding...

We also assess the effectiveness of summarized versus full content inputs, finding that summarized content holds promise for improving efficiency without sacrificing accuracy. While GenAI has the potential to support human fact-checkers in scaling misinformation detection, our results caution against relying solely on these models. Generative artificial intelligence (GenAI) models have made significant advancements in recent years, but the extent to which they can accurately assess and reason about the veracity of content is still being explored. In this study, we evaluate multiple GenAI models’ ability to rate and reason about the credibility of information, using content posted by subnational U.S. politicians on Facebook as our dataset. One of the most widely used GenAI models, GPT-4o, emerged as the top performer in our evaluation, outperforming other models.

However, it is important to note that even the best-performing models exhibited only moderate agreement with human coders. This suggests that although GenAI models have made strides in this area, there is still progress to be made in accurately assessing the veracity of content. Furthermore, our findings reveal that GenAI models heavily rely on linguistic features and “hard” criteria such as level of detail, source reliability, and language formality when determining the credibility of content. While these criteria can be indicative of low-credibility content, they do not necessarily capture the underlying veracity of the information. This highlights the need for GenAI models to develop a deeper understanding of veracity, beyond surface-level indicators. 1-�����Di����؝��S7�v^��5���_w��f���m޼�N�}�~yD�?�Q�&���]aN���"�^s�Q�٬�m� +���qff&�/��d���_�� ڡ��ƻsWX+%��,��k�p2�ï��*��o���z���5�I$���,4��k��j:s��ւ6�J2��-9-��~��M*��F�{�� U�X�`U���*"��QH��H���ڞ�<�ǥR�� �b�5��h��V�|�@jw��d�•xpT}�:t����#�|&�^�(a�;��{ �<�ߖ�bHF9]R�6�A�����E�|��=��V�Y�XM����L�� ��i��_�26����T�}�ZႽɜ'b2��a"�^2��9��|R�R�~ � CFlY���^ _i�c�ڗ�� Ce=�VCe��Ŷ�Ø�j�1#� ��Ԇ_�?6ٞ-S�a�0l�?�,RZ��bDYf��g׈��~�q�?>����=�ʏչW���U��遱ͤp* ���4� �~�����3�Lz��}�ݐ��Z�v��Mm�Иvp����J9 ���0V+,S�#�� �{�����_7]D�^-y�ۜ]&���kB� j�}0�X��{��:��L��2K(,��O8��U)��]�TC�\zal �‘;U��U��W�/� ^���'NpU+�V![���)�bǑܢ~v��������\�mJ�iilUN��N��>9 ��g�o5\k7 ��!6���!����m��*�A�=����_C2���t�V�P�T����r���\�����=bO�$b����tI(̎�ZRVE�!:������v���*,#p� 9� -��4�����L�-X/��� � �X���6Ce�E38�W�7K�/O���B��u���L���>�Xe��E�j�}f <�=zA�9K� Ƚ��0�¡td �0qR[����y��\(�����8 �f��%�^�,�I>�z��V��⻍�ͺ� :����[m���J�(�:� ��Ƙx��'�~�����S�#�pgJ�<���T�T�`�bɕ�ƅ��''�/�{v��fO|��}���������X��@�E��wF�x�tJ}{��]��X�)��ޤ4h�q�ܢX?�IUB9\q5 -ȕѤ��E��[�<�~ƍ��&O�/ ��� S\�O_���7�bgRqy����%1��L;ĕ[�u�]�Zn�@7}����ϸ���gI�p...

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