Uncovering Ai Art Cons Ethical Emotional Legal Risks

Bonisiwe Shabane

-Nov 18, 2025, 8:11 PM

uncovering ai art cons ethical emotional legal risks

AI-generated art is revolutionizing the creative industry, prompting a mix of excitement, curiosity, and concern. While it offers unprecedented speed, accessibility, and scalability, it also raises important ethical, emotional, and legal questions. In this post, we unpack the true costs and complexities of AI art and explore how humans and machines can—and should—coexist in the creative process. AI art is visual content created with the help of artificial intelligence, typically using tools like generative adversarial networks (GANs), diffusion models, or other machine learning algorithms. These systems are trained on thousands of images to learn patterns, colors, and compositions, allowing them to produce art that mimics a wide range of human styles. Unlike traditional digital tools, AI doesn’t require you to know how to draw, paint, or compose.

You can input a text prompt like “a cat surfing on Jupiter in watercolor” and receive an image that fits the description in seconds. This accessibility has made AI art popular in marketing, content creation, game design, and even fine art spaces. While AI can produce impressive visuals, it lacks true understanding. It does not feel, reflect, or interpret the world through a lived human experience. AI art is often derivative, relying on remixing or recontextualizing existing styles and data. There are also practical limitations.

AI can struggle with hands, text, facial expressions, or complex perspectives. It can unintentionally produce distorted or nonsensical elements. Additionally, some outputs feel overly polished or repetitive, lacking the unpredictability that characterizes human creativity. As you explore AI-generated art, you’ll find that ethical and legal battles center on ownership, originality, and moral rights. Questions arise about whether the AI, creator, or user holds rights, and how existing laws apply to such works. These debates challenge traditional ideas of creativity and authenticity.

If you want to understand how regulations are evolving and how industry standards are shaping the future, there’s more to uncover on these complex issues. The rise of AI-generated art is transforming how you think about creativity and artistic expression. It challenges traditional notions by producing complex, unique pieces without human hands directly involved. As an observer or creator, you might find yourself questioning what truly defines art—human emotion or algorithmic precision. AI tools can generate images, music, and poetry at unprecedented speeds, expanding your creative possibilities. This shift democratizes art, allowing more people to participate regardless of technical skill.

However, it also sparks debates about originality and authenticity. You may feel excitement about new forms of expression, but also concern about the dilution of human craftsmanship. Additionally, the vetting process, such as the Vetted – Mother Baby Kids standards, underscores the importance of safety and authenticity in creative outputs. Ultimately, AI art reshapes how you perceive talent and creativity in an evolving digital landscape. You might wonder who owns the rights to AI-generated art—does it belong to the creator, the programmer, or the AI itself? Fair use becomes complicated when AI tools remix existing works, raising questions about proper attribution and limits.

As these issues grow, understanding copyright ownership and fair use is more important than ever. Additionally, the use of home furnishings like heated mattress pads demonstrates how technology can improve comfort and safety, raising similar questions about ownership and innovation rights in the digital realm. As AI-generated art becomes more prevalent, questions about who owns the copyright grow increasingly complex. You might wonder if the creator of the AI, the user who prompted it, or the AI itself holds the rights. Currently, laws often see the human behind the process as the copyright holder, but this isn’t always clear-cut. If an artist trains an AI model with copyrighted works, disputes can arise over whether the output infringes on existing rights.

Ownership also depends on the level of human input; minimal contributions may challenge the attribution of rights. As AI tools evolve, legal systems will need to clarify whether rights belong to developers, users, or a new category altogether. This ambiguity complicates licensing, royalties, and the future of creative ownership. Additionally, the role of ethical hacking in testing AI security measures highlights the importance of safeguarding intellectual property from malicious exploitation. In the rapidly evolving landscape of AI-generated art, fair use emerges as a critical legal doctrine that can sometimes justify the use of copyrighted works without permission. As an artist or developer, you need to understand that fair use considers factors like purpose, nature, amount, and effect on the market.

If your AI art transforms original works considerably or serves a different audience, you might argue fair use applies. However, courts often scrutinize whether your work competes with or undermines the original creator’s rights. You should carefully evaluate how much copyrighted material you incorporate and whether your use impacts the original’s value. While fair use provides some flexibility, it doesn’t guarantee immunity from legal challenges, so proceed cautiously. Additionally, leveraging AI content clustering strategies can help organize and produce content that aligns more closely with fair use principles by focusing on transformative and non-competitive elements. As Artificial Intelligence (AI) continues to grow daily, more exciting (and somewhat controversial) technology emerges every other day.

As we see the advancements in AI, we see more and more people becoming skeptical of it. This paper explores the complications and confusion around the ethics of generative AI art. We delve deep into the ethical side of AI, specifically generative art. We step back from the excitement and observe the impossible conundrums that this impressive technology produces. Covering environmental consequences, celebrity representation, intellectual property, deep fakes, and artist displacement. Our research found that generative AI art is responsible for increased carbon emissions, spreading misinformation, copyright infringement, unlawful depiction, and job displacement.

In light of this, we propose multiple possible solutions for these problems. We address each situation’s history, cause, and consequences and offer different viewpoints. At the root of it all, though, the central theme is that generative AI Art needs to be correctly legislated and regulated. Many artists are not so thrilled at the idea that a machine might replace them one day (Verdegem, 2021; Coeckelbergh, 2017). This anxiety is obvious in the animation and digital art communities, where AI-generated content is rapidly gaining traction (Caporusso et al., 2023; Lovato et al., 2024). A notable example is the high amount of backlash faced by the tech channel CorridorDigital (King, [n.

d.]), where animation was made using machine learning software that greatly simplified the process (Appleford, [n. d.]). While some praised the technological advancement, many artists criticized it as threatening their craft, creativity, and job security. Many people in the art field or planning to go into it are very concerned about the future, as it holds anything. Concern for the future doesn’t end here, as some artists are also in an uproar over the procurement of art for training data without consent—especially in cases like Corridor Digital111https://www.pugetsystems.com/featured/case-study-with-corridor-digital/?srsltid=AfmBOoq_MCR-RaOTh1MoWcillHT_gQojU_GDNNjjysGJY5-XVV9xvV6l and ChatGPT. This lack of transparency and respect for creative ownership raises serious questions about consent, attribution, and artistic integrity in the age of AI.

These concerns are not isolated incidents—they reflect a broader unease in the creative industries about the unchecked rise of generative AI (Amankwah-Amoah et al., 2024). One of the most pressing issues involves the unauthorized use of artists’ work to train large-scale machine learning models (Verdegem, 2021). Many generative systems, such as Stable Diffusion 222https://stablediffusionweb.com/ or Midjourney 333https://en.wikipedia.org/wiki/Midjourney, scrape images from the internet—including professional portfolios, illustrations, and concept art—without the consent or even awareness of the original creators. This practice has sparked a wave of frustration and activism among artists, many of whom argue that their labor, style, and creative identity are being commodified without recognition or compensation (Kompatsiaris, 2015). But copyright infringement is just the beginning. As generative AI systems become more accessible and sophisticated, ethical dilemmas continue to multiply: Who owns AI-generated art?

(Chesterman, 2023) Can a machine imitate the emotional intent of a human creator? (Demmer et al., 2023) What happens when deepfakes and AI-generated images are used for deception or defamation in art, journalism, or activism? These aren’t the only concerns that have arisen, and probably won’t be the last ethical dilemmas. The rise of AI-generated art has introduced a wave of ethical dilemmas that extend far beyond copyright questions. From unauthorized data scraping to stylistic mimicry, the creative industry is grappling with preserving artistic integrity in the face of rapid technological change. As these practices gain public visibility and legal scrutiny, calls for regulation are growing louder.

Yet crafting legislation around AI art remains challenging, given the novelty and complexity of the issues at hand. The legal landscape remains fragmented and inconsistent across regions with generative tools evolving faster than policy can adapt. You have full access to this open access article AI systems and tools are being implemented at an increasingly rapid rate in society for a variety of purposes such as decision-making, managing job applications, and socializing. These new technologies have a lot of promise but may also introduce new risks by threatening human moral and relational values, as well as values connected to flourishing. Mainstream approaches to risk assessment do not pay sufficient attention to these values.

The study of emotions as they are connected to human values can therefore play an important role in risk management. We will contribute to this discussion by introducing the concept of human needs, or what we consider to be the sources of values that constitute emotions. This brings a new perspective to the debate around AI and risk. By combining insights from Martha Nussbaum and Soran Reader, we argue that while emotions are crucial for highlighting what values are activated in a particular situation, the sources of an important part of human... This provides for what we call the ‘needs-values-emotions nexus’. We argue that this framework can add to the discussion about the ethical risks of AI in two fundamental ways.

First, highlighting the crucial role of needs helps to explain why AI systems cannot develop, feel, nor reason according to human values. On the most basic level, AI systems lack a constitutive part of these values, i.e., they lack needs. The deployment of AI, for example to replace human decision-making, may therefore threaten human values. We discuss this by zooming in on a recent example, the so-called Dutch tax benefit scandal. Second, this paper argues that we need emotions to concretize and deliberate on what values are at risk when developing and using AI technology. Further building on the ‘needs-values-emotions nexus’ developed in this paper, we argue why art is a preeminent medium to elicit emotions and ethical reflection on the risks of AI.

Discussing a concrete example, we illustrate how contemporary artists can contribute to ethical risk-assessments by focusing on the societal impact of AI. Avoid common mistakes on your manuscript. AI systems, such as large language models, are rapidly advancing and are already used in decision-making and to increase efficiency in large societal institutions and organisations.Footnote 1 They are also often expected to outperform... These technologies are being deployed in organizations and institutions at an astonishing speed for numerous purposes and operations ranging from making decisions on loan applications, conducting job interviews, producing explanations in management, and for... 8–12). Hence, there are many optimistic expectations about these technologies, which have led to their quick adoption and rapid development.

Uncovering Ai Art Cons Ethical Emotional Legal Risks

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