University of Kwazulu-Natal

Safeguarding Authenticity For Mitigating The Harms Of Generative Ai

Bonisiwe Shabane
-
safeguarding authenticity for mitigating the harms of generative ai

Chandigarh College of Engineering and Technology, Chandigarh, India As transformer-based AI, exemplified by using ChatGPT, continues to permeate various domains, worries regarding authenticity and explainability are at the upward thrust. It is crucial to enforce sturdy detection, verification, and explainability mechanisms to counteract the ability harms stemming from AI-generated inauthentic content and clinical discoveries. These dangers include the spread of disinformation, incorrect information, and the possibility of generating unreproducible studies consequences. Urgent action is needed to establish and uphold moral requirements, fostering believe and transparency in AI applications. By prioritizing these efforts, this paper will harness the transformative electricity of AI for the advancement of technological know-how and society while mitigating its bad repercussions.

Additionally, fostering collaboration among technologists, policymakers, and area professionals is important to expand comprehensive answers that stability innovation with obligation. This collaborative method will facilitate the introduction of effective rules and frameworks to safeguard facts authenticity inside the age of AI, selling a climate of agree with and accountability in the digital panorama. Keywords: Machine Learning, Generative AI The speedy advancement of generative synthetic intelligence (AI) technologies, exemplified via transformer-based fashions like ChatGPT, has revolutionized content material advent throughout diverse domain names, from textual content technology to picture synthesis and beyond. While those AI structures demonstrate awesome abilties in producing content material that mimics human-like creativity[1], they also enhance vast worries regarding authenticity and the capability for misuse. In current years, the proliferation of AI-generated content has delivered to the leading edge the urgent want to guard authenticity and mitigate the potential harms related to the dissemination of misleading or misleading information.

The capacity of AI algorithms to create extraordinarily convincing and seemingly genuine content poses profound demanding situations to the integrity of virtual information, threatening to exacerbate troubles which includes incorrect information, disinformation, and the... 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 A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity.© Copyright 2025 IEEE - All rights reserved.

Use of this web site signifies your agreement to the terms and conditions. WASHINGTON — While the integration of generative AI in health care holds significant potential to transform the practice of medicine and the health and well-being of patients, successful, ethical, and equitable implementation of generative... Collaboration among health care providers, patients, policymakers, ethicists, and researchers, along with a cross-sector commitment to maximizing the benefit of generative AI while minimizing the risks, is important for navigating the complexities. Artificial intelligence encompasses a broad range of technologies designed to perform complex tasks typically associated with human intelligence, such as reasoning, learning, and problem-solving. Generative AI is a type of artificial intelligence that focuses on creating new content by learning patterns from existing data, and it produces various types of content, including text, imagery, and audio. Large language models are a subset of generative AI that specializes in interpreting and generating human language to create text.

Users of generative AI guide the creation of text using prompts and post-processing actions to further refine it and, if needed, correct errors, omissions, and fabrications. Generative AI has demonstrated potential with reducing clinician burden and delays in care, as well as in supporting biomedical discovery in areas such as drug development, diagnostics, and clinical trial management, the publication says. It can also transform complex medical information into understandable formats to help patients and their support networks comprehend diagnoses and treatment plans, leading to better-informed and more engaged patients. The primary risks that use of generative AI in health care poses include data privacy and security concerns, bias, output limitations, algorithmic brittleness, and hallucinations — which are when a tool produces information that... To mitigate these risks, the publication says, it is important for stakeholders to work together to establish guidelines and regulations that protect patient data, ensure fairness and transparency, and evaluate the effectiveness and safety... “The path forward for integrating generative AI into health care should involve collaboration to ensure that its deployment and use are intentional, coordinated, and ethically sound,” said Thomas M.

Maddox, chair of the authoring group, professor of cardiology at Washington University School of Medicine, and executive director of the Healthcare Innovation Lab, a joint effort of the School of Medicine and BJC HealthCare. “As these technologies evolve, the policies and best practices that guide trustworthy and responsible use of generative AI tools in health care should as well.”

People Also Search

Chandigarh College Of Engineering And Technology, Chandigarh, India As Transformer-based

Chandigarh College of Engineering and Technology, Chandigarh, India As transformer-based AI, exemplified by using ChatGPT, continues to permeate various domains, worries regarding authenticity and explainability are at the upward thrust. It is crucial to enforce sturdy detection, verification, and explainability mechanisms to counteract the ability harms stemming from AI-generated inauthentic cont...

Additionally, Fostering Collaboration Among Technologists, Policymakers, And Area Professionals Is

Additionally, fostering collaboration among technologists, policymakers, and area professionals is important to expand comprehensive answers that stability innovation with obligation. This collaborative method will facilitate the introduction of effective rules and frameworks to safeguard facts authenticity inside the age of AI, selling a climate of agree with and accountability in the digital pan...

The Capacity Of AI Algorithms To Create Extraordinarily Convincing And

The capacity of AI algorithms to create extraordinarily convincing and seemingly genuine content poses profound demanding situations to the integrity of virtual information, threatening to exacerbate troubles which includes incorrect information, disinformation, and the... Corresponding author a.hamed@sanoscience.org Corresponding author xwu@zhejianglab.com This is an open access article under the...

We Recognize The Transformative Potential Of Generative AI, Exemplified By

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 in...

Use Of This Web Site Signifies Your Agreement To The

Use of this web site signifies your agreement to the terms and conditions. WASHINGTON — While the integration of generative AI in health care holds significant potential to transform the practice of medicine and the health and well-being of patients, successful, ethical, and equitable implementation of generative... Collaboration among health care providers, patients, policymakers, ethicists, and ...