Ethical Legal And Social Issues In Ai

Bonisiwe Shabane

-Dec 4, 2025, 9:21 PM

The authors have declared that no competing interests exist. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. This paper aims to (1) summarize the descriptions of ethical, legal, and social issues (ELSI) in major reporting guidelines, identify trends and tendencies, and (2) highlight issues for future researchers and guideline creators. Medical care is a fundamental aspect of human life, closely linked to their health and well-being. Advances in the research and development (R&D) of medical artificial intelligence (AI) are anticipated to yield numerous benefits. However, concerns regarding incorrect AI design and usage, causing various ethical and human-rights issues for both individuals and society, do arise [1–4].

Policies for research efforts in developing AI have been proposed; however, no consensus on approach selection has been attained [4]. Therefore, this study focuses on “reporting guidelines,” which provide specific result-publication instructions for researchers. The guidelines provide a basis for AI deployment and implementation through the increased transparency and evaluability of processes and data management during R&D [5]. In recent years, AI design and usage have been closely related to ELSI [4]. ELSI is the examination of ethical, legal, and social issues raised by the deployment of new knowledge. This perspective focuses on the impact of new scientific methods and knowledge on the current and future generations.

While questioning the role of researchers in ELSI, the position of reporting guidelines requires changes. Therefore, we review recent reporting guidelines for medical AI research and adopt guidelines with a “checklist” of specific initiatives as the selection criteria. First, six reports registered in the Enhancing the Quality and Transparency of Health Research (EQUATOR) network were selected. The EQUATOR network is an international initiative seeking improvements to the credibility and value of published health-research literature by promoting transparent and accurate reports and disseminating robust reporting guidelines [6]. Furthermore, two other reports from major literature databases (Web of Science and PubMed) were identified and added for a total of eight reporting guidelines [7–14]. While organizing these guidelines from an ELSI perspective, the 11 items of the “Considerations for AI developers,” which append the WHO guidance “Ethics and governance of artificial intelligence for health,” were used [Table 1].

While the uses of AI tools can seem unlimited, it’s critical that their expertise does not go unquestioned; AI tools are only as reliable as the data they’re trained on — and the people... Issues related to privacy, biases, and transparency remain paramount for building AI systems that are both ethical and accurate. As corporations continue to embed AI into their day-to-day processes, establishing frameworks ensuring AI applications are within legal and ethical bounds is increasingly important. Understanding the ethical implications of AI is critical for leaders: First, AI ethical literacy gives leaders an understanding of the potential issues AI could cause, allowing them to protect their companies from lawsuits and reputational damage. Second, understanding AI ethics helps leaders build a holistic picture of the coming AI-Age, and the concomitant risks and opportunities.

Years in this theme: All years 2025 2024 2023 2022 Download Issue Citations: END BibTex RIS Articles published in 2023-ethical-legal-and-social-issues-in-ai in this theme: 11 (scroll down to load remaining articles) Developing Ethics and Equity Principles, Terms, and Engagement Tools to Advance Health Equity and Researcher Diversity in AI and Machine Learning: Modified Delphi Approach Rachele Hendricks-Sturrup, Malaika Simmons, Shilo Anders, Kammarauche Aneni, Ellen Wright Clayton, Joseph Coco, Benjamin Collins, Elizabeth Heitman, Sajid Hussain, Karuna Joshi, Josh Lemieux, Laurie Lovett Novak, Daniel J Rubin, Anil Shanker, Talitha Washington, Gabriella... Artificial intelligence, once confined to science fiction and the imaginations of dreamers, has rapidly become a force shaping our everyday reality.

AI powers the apps we use, the recommendations we see, the cars we drive (or will drive), and increasingly, the decisions that govern our lives. But as AI technologies grow more capable, they also raise profound ethical questions—questions that touch on human rights, fairness, freedom, and even the future of our species. The power of AI to transform society is immense, but without careful ethical consideration, that transformation could lead us into a dystopia as easily as it could a utopia. Let’s dive deeply into ten of the most urgent and fascinating ethical issues surrounding artificial intelligence today. Imagine a job applicant applying online, only to be rejected not by a human, but by an AI system trained on historical data. If past hiring practices favored certain demographics, the AI will “learn” to do the same, perpetuating bias under the illusion of objectivity.

Bias in AI is not just an accident; it is a mirror of our societal prejudices. Machine learning systems, which are trained on data from the real world, absorb the inequalities and injustices present in that data. From facial recognition systems that misidentify people of color more often than white individuals, to sentencing algorithms that recommend harsher penalties for minorities, the examples are chilling and numerous. Although there is justified excitement about the rapid development of artificial intelligence, these tools raise important legal and ethical concerns and present a range of potential impacts on society more generally. Governments, companies, and individuals are starting to consider their ethical obligations when it comes to the use and implementation of AI systems. For instance UNESCO has created a human rights approach to AI, and the Australian government has created Australia’s AI Ethics Principles.

There are several important considerations related to copyright and AI, including: Content creators and owners have become increasingly concerned that LLMs have been trained on copyrighted works without permission. There is ongoing litigation about whether AI companies breached copyright. In late 2023, The New York Times sued OpenAI and Microsoft, claiming “unlawful copying and use of The Times’s uniquely valuable works.” The Complex World of Style, Copyright and Generative AI blog discusses some... Some AI tools automatically incorporate any content you upload into their underlying data. In addition to the obvious privacy concerns, you should think carefully before uploading content that is owned or licensed by someone else.

UQ’s AI tool, Microsoft Copilot Chat, does not use supplied information in this way and is generally a safer alternative. Be careful not to upload licensed or copyright-protected materials into AI tools. Refer to restrictions on the use of online collections to help you use Gen AI ethically and legally. In recent years, the field of Artificial Intelligence (AI) has witnessed remarkable advancements, catalyzing a transformative wave across various sectors, including healthcare and finance. As AI takes on increasingly pivotal roles in decision-making processes, it unfurls a myriad of ethical and societal implications that demand thorough scrutiny. There are growing ethical quandaries stemming from AI's expanding involvement in critical decision-making.

Foremost among these concerns is the specter of bias and equity. AI systems often draw from historical data that may harbor inherent biases, resulting in discriminatory outcomes in domains like hiring, lending, and the criminal justice system. Another pivotal challenge is algorithmic transparency. AI algorithms, often perceived as inscrutable "black boxes," render the decision-making process opaque, raising questions about accountability and the capacity to rectify erroneous decisions. The issue of bias and discrimination permeates diverse sectors, including medicine. The ramifications of bias in AI algorithms on medical diagnoses and treatment recommendations come to the forefront.

Should AI systems be trained on biased data, they could perpetuate healthcare disparities, potentially leading to unequal access to high-quality medical care. Moreover, the life-altering consequences of AI errors in medical contexts, emphasizing the critical need to combat bias and ensure responsible AI application in healthcare settings. Overview addressing the ethics, use, acquisition, and development of AI and key affected practices areas Reviewed by Jessica Brand, Sr. Specialist Legal Editor, Thomson Reuters | Originally published March 1, 2024 Many legal software tools are incorporating artificial intelligence (AI) to enhance their performance.

Generative AI (GenAI) is a type of AI that is trained on existing content to create new images, audio, video, computer code and, most important for lawyers, written texts. Examples of GenAI for consumer and general -use include popular AI tools such as ChatGPT, Copilot, Gemini, Midjourney, and DALL-E. Large language models (LLMs) are a subset of GenAI that usually focus on text. You may see either or both of these terms in discussions of lawyers’ ethical responsibilities when using AI tools.

Ethical Legal And Social Issues In Ai

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