Generative Ai Use Cases Future Trends Ethical Challenges

Bonisiwe Shabane

-Dec 28, 2025, 8:47 AM

generative ai use cases future trends ethical challenges

Al-kfairy, M.; Mustafa, D.; Kshetri, N.; Insiew, M.; Alfandi, O. Ethical Challenges and Solutions of Generative AI: An Interdisciplinary Perspective. Informatics 2024, 11, 58. https://doi.org/10.3390/informatics11030058 Al-kfairy M, Mustafa D, Kshetri N, Insiew M, Alfandi O. Ethical Challenges and Solutions of Generative AI: An Interdisciplinary Perspective.

Informatics. 2024; 11(3):58. https://doi.org/10.3390/informatics11030058 Al-kfairy, Mousa, Dheya Mustafa, Nir Kshetri, Mazen Insiew, and Omar Alfandi. 2024. "Ethical Challenges and Solutions of Generative AI: An Interdisciplinary Perspective" Informatics 11, no.

3: 58. https://doi.org/10.3390/informatics11030058 Al-kfairy, M., Mustafa, D., Kshetri, N., Insiew, M., & Alfandi, O. (2024). Ethical Challenges and Solutions of Generative AI: An Interdisciplinary Perspective. Informatics, 11(3), 58.

https://doi.org/10.3390/informatics11030058 You have full access to this open access article The advent of generative artificial intelligence and the widespread adoption of it in society engendered intensive debates about its ethical implications and risks. These risks often differ from those associated with traditional discriminative machine learning. To synthesize the recent discourse and map its normative concepts, we conducted a scoping review on the ethics of generative artificial intelligence, including especially large language models and text-to-image models. Our analysis provides a taxonomy of 378 normative issues in 19 topic areas and ranks them according to their prevalence in the literature.

The study offers a comprehensive overview for scholars, practitioners, or policymakers, condensing the ethical debates surrounding fairness, safety, harmful content, hallucinations, privacy, interaction risks, security, alignment, societal impacts, and others. We discuss the results, evaluate imbalances in the literature, and explore unsubstantiated risk scenarios. Avoid common mistakes on your manuscript. With the rapid progress of artificial intelligence (AI) technologies, the ethical reflection thereof is constantly facing new challenges. From the advent of deep learning for powerful computer vision applications (LeCun et al., 2015), to the achievement of superhuman-level performance in complex games with reinforcement learning (RL) algorithms (Silver et al., 2017), and... Alongside this technological progress, the field of AI ethics has evolved.

Initially, it was primarily a reactive discipline, erecting normative principles for entrenched AI technologies (Floridi et al., 2018; Hagendorff, 2020). However, it became increasingly proactive with the prospect of harms through misaligned artificial general intelligence (AGI) systems. During its evolution, AI ethics underwent a practical turn to explicate how to put principles into practice (Mittelstadt, 2019; Morley et al., 2019); it diversified into alternatives for the principle-based approach, for instance by... Both domains have a normative grounding and are devoted to preventing harm or even existential risks stemming from generative AI systems. On the technical side of things, variational autoencoders (Kingma & Welling, 2013), flow-based generative models (Papamakarios et al., 2021; Rezende & Mohamed, 2015), or generative adversarial networks (Goodfellow et al., 2014) were early successful... Later, the transformer architecture (Vaswani et al., 2017) as well as diffusion models (Ho et al., 2020) boosted the performance of text and image generation models and made them adaptable to a wide range...

However, due to the lack of user-friendly graphical user interfaces, dialog optimization, and output quality, generative models were underrecognized in the wider public. This changed with the advent of models like ChatGPT, Gemini, Stable Diffusion, or Midjourney, which are accessible through natural language prompts and easy-to-use browser interfaces (OpenAI, 2022; Gemini Team et al., 2023; Rombach et... The next phase will see a rise in multi-modal models, which are similarly user-friendly and combine the processing and generation of text, images, and audio along with other modalities, such as tool use (Mialon... In sum, we define the term “generative AI” as comprising large, foundation, or frontier models, capable of transforming text to text, text to image, image to text, text to code, text to audio, text... 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.

Like other forms of AI, generative AI can affect ethical issues and risks pertaining to data privacy, security, energy usage, political impact and workforces. GenAI technology can also potentially introduce a series of new business risks, such as misinformation and hallucinations, plagiarism, copyright infringements and harmful content. Lack of transparency and the potential for worker displacement are additional issues that enterprises might need to address. "Many of the risks posed by generative AI ... are enhanced and more concerning than those [associated with other types of AI]," said Tad Roselund, managing director and senior partner at consultancy BCG. Those risks require a comprehensive approach, including a clearly defined strategy, good governance and a commitment to responsible AI.

Corporate cultures that use GenAI should consider the following 11 issues: Generative AI systems can create content automatically based on text prompts by humans. "These systems can generate enormous productivity improvements, but they can also be used for harm, either intentional or unintentional," explained Bret Greenstein, partner and generative AI leader at professional services consultancy PwC. An AI-generated email sent on behalf of the company, for example, could inadvertently contain offensive language or issue harmful guidance to employees. GenAI should be used to augment but not replace humans or processes, Greenstein advised, to ensure content meets the company's ethical expectations and supports its brand values. Popular generative AI tools are trained on massive image and text databases from multiple sources, including the internet.

When these tools create images or generate lines of code, the data's source could be unknown, which might be problematic for a bank handling financial transactions or a pharmaceutical company relying on a formula... Reputational and financial risks could also be massive if one company's product is based on another company's intellectual property. "Companies must look to validate outputs from the models," Roselund advised, "until legal precedents provide clarity around IP and copyright challenges." Why Responsible AI Development Is the Key to Innovation and Trust Since September 2022, online searches for the term 'generative AI' have increased by 7900%, according to Google Trends. It is impossible to escape the impact of this technology.

As it permeates every field from content creation to healthcare diagnostics and self-driving cars, organizations have invested billions in generative AI (GenAI) search of increased revenues, improved efficiency, and competitive advantage. However, without ethical oversight, regulation, and proper governance, businesses risk losing their customers' trust. In the last few months alone, media publications have found dozens of high-profile examples of ethical failures, biased outcomes, and regulatory gaps, undermining public confidence in GenAI systems. To use GenAI responsibly, organizations should proactively address ethical issues, understand their compliance obligations, and establish robust governance mechanisms. Between the development, training, testing, and deployment of GenAI models, there is ample room for ethical missteps. These can lead to biased, discriminatory, or privacy-violating outcomes.

Generative AI ethics matters because it ensures the technology’s rapid innovation benefits society responsibly and equitably. As generative AI models advance, their capability to create content autonomously raises critical questions about fairness, transparency, and accountability. Without ethical frameworks, these powerful tools risk amplifying biases, violating privacy, and generating harmful or misleading outputs that can disrupt social trust. Embedding ethics at the core of generative AI development and deployment establishes a foundation of trustworthiness. Responsible adoption helps balance innovation with protecting user rights and societal well-being. It also guides creators and users in navigating complex moral considerations, making ethical governance essential for sustainable AI progress.

Generative AI development has evolved from pure technological innovation to embracing ethical responsibility as a core principle. Developers and stakeholders now recognize that innovation must be accompanied by accountability to ensure AI systems serve society positively. Early integration of ethical considerations has become essential, shaping design choices, data usage, and deployment strategies to prevent harm. This shift reflects a maturing mindset where advancing AI capabilities goes hand in hand with safeguarding fairness, privacy, and transparency throughout the development lifecycle. Ethical considerations are fundamental to AI adoption as they directly impact trust-building, risk management, and societal acceptance. Embedding ethics early in the AI development lifecycle helps organizations address potential biases, ensure fairness, and mitigate privacy concerns, which in turn fosters public confidence.

This early integration of ethical principles enables responsible innovation by aligning AI applications with societal values and legal standards. Consequently, companies adopting generative AI technologies can minimize reputational and operational risks, enhancing stakeholder trust and facilitating smoother adoption across diverse sectors. Core ethical challenges in generative AI include bias, copyright infringement, data privacy concerns, and misinformation risks. Artificial intelligence (AI) has rapidly evolved over the past few decades, transforming the way we live and work. From virtual assistants that help us manage our schedules to sophisticated algorithms that drive decision-making in industries like finance and healthcare, AI has become an integral part of our daily lives. As this technology has advanced, a new frontier has emerged: generative AI.

Generative AI refers to machine learning models designed to create new content based on patterns from their training data. This technology harnesses models like Generative Adversarial Networks (GANs), transformers, and Variational Autoencoders (VAEs) to produce diverse outputs—text, images, sounds, and videos. What makes generative AI so revolutionary is its capability to not only mimic existing data patterns but also produce content that can resemble creativity or expertise. This is why we now see AI-generated artworks, realistic images, lifelike voices, and even computer-generated influencers. Today’s generative models, like GPT-4 for text and Midjourney or DALL-E for images, are versatile and capable of adapting to a range of inputs, making them valuable in various industries. Beyond content generation, they’re transforming fields like healthcare, education, entertainment, and finance.

This blog post will dive into the most impactful use cases of generative AI, showing how this technology is reshaping workflows and unlocking new potential across sectors. Content creation is one of the most prominent applications of generative AI, where its ability to quickly generate human-like text and visuals is invaluable. Generative AI’s ability to analyze data and generate personalized content is transforming customer experiences across sectors, from retail to banking.

Generative Ai Use Cases Future Trends Ethical Challenges

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Al-kfairy, M.; Mustafa, D.; Kshetri, N.; Insiew, M.; Alfandi, O.

Informatics. 2024; 11(3):58. Https://doi.org/10.3390/informatics11030058 Al-kfairy, Mousa, Dheya Mustafa, Nir Kshetri,

3: 58. Https://doi.org/10.3390/informatics11030058 Al-kfairy, M., Mustafa, D., Kshetri, N., Insiew,

Https://doi.org/10.3390/informatics11030058 You Have Full Access To This Open Access Article

The Study Offers A Comprehensive Overview For Scholars, Practitioners, Or