Spotify adds'Verified' badges to distinguish human artists from AI Spotify is introducing a'Verified' badge to help users identify when artists on its platform are human, not AI-generated. The world's most-used music streaming service said the'Verified by Spotify' text and green checkmark icon would appear next to artist names when they meet defined standards demonstrating authenticity. This could include having linked social accounts on their artist profile, consistent listener activity or other signals of a real artist behind the profile, the company said, such as merchandise or concert dates. In its blog post, Spotify said more than 99% of the artists listeners actively search for will be verified, representing hundreds of thousands of artists. It said the process would prioritise acts with important contributions to music culture and history, rather than content farms, with the platform rolling out verification and badges over the coming weeks.

We developed a robot arm that collaboratively paints with a human artist. The robot has an awareness of the artist's heartbeat through the EmotiBit sensor, which provides the arousal levels of the painter . Given the heartbeat detected, the robot decides to increase proximity to the artist's workspace or retract. If a higher heartbeat is detected, which is associated with increased arousal in human artists, the robot will move away from that area of the canvas. If the artist's heart rate is detected as neutral, indicating the human artist's baseline state, the robot will continue its painting actions across the entire canvas. We also demonstrate and propose alternative robot-artist interactions using natural language and physical touch. This work combines the biometrics of a human artist to inform fluent artistic interactions.

Psychedelic rock band The Velvet Sundown has over a million monthly listeners on Spotify and earns thousands of dollars every month. However, the catch is that it's not a traditional band at all. It's mostly made by artificial intelligence. Their Spotify bio confirms that the group is a synthetic music project, guided by human creative direction but composed, voiced, and visualized using AI. This is a sign of where music may be headed.

Current legal frameworks consider AI-generated works eligible for copyright protection when they meet originality requirements and involve substantial human intellectual input. However, systematic legal standards and reliable evaluation methods for AI art copyrights are lacking. Through comprehensive analysis of legal precedents, we establish three essential criteria for determining distinctive artistic style: stylistic consistency, creative uniqueness, and expressive accuracy. To address these challenges, we introduce ArtBulb, an interpretable and quantifiable framework for AI art copyright judgment that combines a novel style description-based multimodal clustering method with multimodal large language models (MLLMs). We also present AICD, the first benchmark dataset for AI art copyright annotated by artists and legal experts. Experimental results demonstrate that ArtBulb outperforms existing models in both quantitative and qualitative evaluations. Our work aims to bridge the gap between the legal and technological communities and bring greater attention to the societal issue of AI art copyrights.

In early 2025, Augmented Intelligence - Christie's first AI art auction - drew criticism for showcasing a controversial genre. Amid wider legal uncertainty, artists voiced concerns over data mining practices, notably with respect to copyright. The backlash could be viewed as a microcosm of AI's contested position in the creative economy. Touching on the auction's presentation, reception, and results, this paper explores how, among social dissonance, machine learning finds its place in the artworld. Foregrounding responsible innovation, the paper provides a balanced perspective that champions creators' rights and brings nuance to this polarised debate. With a focus on exhibition design, it centres framing, which refers to the way a piece is presented to influence consumer perception. Context plays a central role in shaping our understanding of how good, valuable, and even ethical an artwork is. In this regard, Augmented Intelligence situates AI art within a surprisingly traditional framework, leveraging hallmarks of "high art" to establish the genre's cultural credibility. Generative AI has a clear economic dimension, converging questions of artistic merit with those of monetary worth. Scholarship on ways of seeing, or framing, could substantively inform the interpretation and evaluation of creative outputs, including assessments of their aesthetic and commercial value.

Thousands of artists are urging the auction house Christie's to cancel a sale of art created with artificial intelligence, claiming the technology behind the works is committing "mass theft". The Augmented Intelligence auction has been described by Christie's as the first AI-dedicated sale by a major auctioneer and features 20 lots with prices ranging from 10,000 to 250,000 for works by artists including Refik Andanol and the late AI art pioneer Harold Cohen. A lettter calling for the auction to be scrapped has received 3,000 signatures, including from Karla Ortiz and Kelly McKernan, who are suing AI companies over claims that the firms' image generation tools have used their work without permission. These models, and the companies behind them, exploit human artists, using their work without permission or payment to build commercial AI products that compete with them." Calling on Christie's to cancel the auction, which starts on 20 February, it adds: "Your support of these models, and the people who use them, rewards and further incentivizes AI companies' mass theft of human artists' work." The British composer Ed Newton-Rex, a key figure in the campaign by creative professionals for protection of their work and a signatory to the letter, said at least nine of the works appearing in the auction appeared to have used models trained on artists' work. However, other pieces in the auction do not appear to have used such models. A spokesperson for Christie's said that "in most cases" the AI used to create art in the auction had been trained on the artists' "own inputs". "The artists represented in this sale all have strong, existing multidisciplinary art practices, some recognised in leading museum collections.

A group of more than 200 high-profile musicians have signed an open letter calling for protections against the predatory use of artificial intelligence that mimics human artists' likenesses, voices and sound. The signatories span musical genres and eras, ranging from A-list stars such as Billie Eilish, J Balvin and Nicki Minaj to Rock and Roll Hall of Famers like Stevie Wonder and REM. The estates of Frank Sinatra and Bob Marley are also signatories. The letter, which was issued by the Artist Rights Alliance advocacy group, makes the broad demand that technology companies pledge not to develop AI tools that undermine or replace human songwriters and artists. "This assault on human creativity must be stopped. We must protect against the predatory use of AI to steal professional artists' voices and likenesses, violate creators' rights, and destroy the music ecosystem," the letter states.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Kelly McKernan's acrylic and watercolor paintings are bold and vibrant, often featuring feminine figures rendered in bright greens, blues, pinks and purples. The style, in the artist's words, is "surreal, ethereal … dealing with discomfort in the human journey." The word "human" has a special resonance for McKernan these days.

Each fall, I begin my course on the intersection of music and artificial intelligence by asking my students if they're concerned about AI's role in composing or producing music. So far, the question has always elicited a resounding "yes." Their fears can be summed up in a sentence: AI will create a world where music is plentiful, but musicians get cast aside. In the upcoming semester, I'm anticipating a discussion about Paul McCartney, who in June 2023 announced that he and a team of audio engineers had used machine learning to uncover a "lost" vocal track of John Lennon by separating the instruments from a demo recording. But resurrecting the voices of long-dead artists is just the tip of the iceberg in terms of what's possible – and what's already being done. In an interview, McCartney admitted that AI represents a "scary" but "exciting" future for music.

Modern diffusion models have set the state-of-the-art in AI image generation. Their success is due, in part, to training on Internet-scale data which often includes copyrighted work. This prompts questions about the extent to which these models learn from, imitate, or copy the work of human artists. This work suggests that tying copyright liability to the capabilities of the model may be useful given the evolving ecosystem of generative models. Specifically, much of the legal analysis of copyright and generative systems focuses on the use of protected data for training. As a result, the connections between data, training, and the system are often obscured. In our approach, we consider simple image classification techniques to measure a model's ability to imitate specific artists. Specifically, we use Contrastive Language-Image Pretrained (CLIP) encoders to classify images in a zero-shot fashion. Our process first prompts a model to imitate a specific artist. Then, we test whether CLIP can be used to reclassify the artist (or the artist's work) from the imitation. If these tests match the imitation back to the original artist, this suggests the model can imitate that artist's expression. Our approach is simple and quantitative. Furthermore, it uses standard techniques and does not require additional training. We demonstrate our approach with an audit of Stable Diffusion's capacity to imitate 70 professional digital artists with copyrighted work online. When Stable Diffusion is prompted to imitate an artist from this set, we find that the artist can be identified from the imitation with an average accuracy of 81.0%. Finally, we also show that a sample of the artist's work can be matched to these imitation images with a high degree of statistical reliability. Overall, these results suggest that Stable Diffusion is broadly successful at imitating individual human artists.