Prote\c{c}\~ao intelectual de obras produzidas por sistemas baseados em
intelig\^encia artificial: uma vis\~ao tecnicista sobre o tema
- URL: http://arxiv.org/abs/2206.03215v1
- Date: Wed, 11 May 2022 12:07:47 GMT
- Title: Prote\c{c}\~ao intelectual de obras produzidas por sistemas baseados em
intelig\^encia artificial: uma vis\~ao tecnicista sobre o tema
- Authors: F\'abio Manoel Fran\c{c}a Lobato
- Abstract summary: The pervasiveness of Artificial Intelligence (AI) is unquestionable in our society. Even in the arts, AI is present.
This essay aims to contribute with a technicist view on the discussion of copyright applicability from works produced by AI.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: The pervasiveness of Artificial Intelligence (AI) is unquestionable in our
society. Even in the arts, AI is present. A notorious case is the song "Hey
Ya!" of the OutKast group, successful in the 2000s. At this time, the music
industry began to make decisions based on data to strategize based on
predictions of listeners' habits. This case is just one of the countless
examples of AI applications in the arts. The advent of deep learning made it
possible to build systems capable of accurately recognizing artistic style in
paintings. Content generation is also possible; for example, Deepart customizes
images from two \textit{inputs}: 1) an image to be customized; 2) a style of
painting. The generation of songs according to specific styles from AI-based
systems is also possible. Such possibilities raise questions about the
intellectual property of such works. On this occasion, who owns the copyright
of a work produced from a system based on Artificial Intelligence? To the
creator of the AI? The company/corporation that subsidized the development of
this system? Or AI itself as a creator? This essay aims to contribute with a
technicist view on the discussion of copyright applicability from works
produced by AI.
Related papers
- Laypeople's Egocentric Perceptions of Copyright for AI-Generated Art [3.072340427031969]
This research investigates perceptions of AI-generated art concerning factors associated with copyright protection.
We find that participants are most likely to attribute authorship and copyright over AI-generated images to the users who prompted the system to generate the image.
Our results suggest that people judge their own AI-generated art more favorably with respect to some factors (creativity and effort) but not others (skills)
arXiv Detail & Related papers (2024-07-15T08:53:43Z) - AI Art is Theft: Labour, Extraction, and Exploitation, Or, On the Dangers of Stochastic Pollocks [0.0]
generative artificial intelligence has been controversial as a tool for creating artwork.
The artistic community has launched a protest movement, which argues that AI image generation is a kind of theft.
This paper analyzes, substantiates, and critiques these arguments, concluding that AI image generators involve an unethical kind of labour theft.
arXiv Detail & Related papers (2024-01-10T20:20:55Z) - AIxArtist: A First-Person Tale of Interacting with Artificial
Intelligence to Escape Creative Block [20.96181205379132]
The future of the arts and artificial intelligence (AI) is promising as technology advances.
This workshop pictorial puts forward first-person research that shares interactions between an HCI researcher and AI.
The paper explores two questions: How can AI support artists' creativity, and what does it mean to be explainable in this context.
arXiv Detail & Related papers (2023-08-22T13:15:29Z) - AI Audit: A Card Game to Reflect on Everyday AI Systems [21.75299649772085]
An essential element of K-12 AI literacy is educating learners about the ethical and societal implications of AI systems.
There is little work in using game-based learning methods in AI literacy.
We developed a competitive card game for middle and high school students called "AI Audit"
arXiv Detail & Related papers (2023-05-29T06:41:47Z) - Designing Participatory AI: Creative Professionals' Worries and
Expectations about Generative AI [8.379286663107845]
Generative AI, i.e., the group of technologies that automatically generate visual or written content based on text prompts, has undergone a leap in complexity and become widely available within just a few years.
This paper presents the results of a qualitative survey investigating how creative professionals think about generative AI.
arXiv Detail & Related papers (2023-03-15T20:57:03Z) - Seamful XAI: Operationalizing Seamful Design in Explainable AI [59.89011292395202]
Mistakes in AI systems are inevitable, arising from both technical limitations and sociotechnical gaps.
We propose that seamful design can foster AI explainability by revealing sociotechnical and infrastructural mismatches.
We explore this process with 43 AI practitioners and real end-users.
arXiv Detail & Related papers (2022-11-12T21:54:05Z) - Cybertrust: From Explainable to Actionable and Interpretable AI (AI2) [58.981120701284816]
Actionable and Interpretable AI (AI2) will incorporate explicit quantifications and visualizations of user confidence in AI recommendations.
It will allow examining and testing of AI system predictions to establish a basis for trust in the systems' decision making.
arXiv Detail & Related papers (2022-01-26T18:53:09Z) - A User-Centred Framework for Explainable Artificial Intelligence in
Human-Robot Interaction [70.11080854486953]
We propose a user-centred framework for XAI that focuses on its social-interactive aspect.
The framework aims to provide a structure for interactive XAI solutions thought for non-expert users.
arXiv Detail & Related papers (2021-09-27T09:56:23Z) - The MineRL BASALT Competition on Learning from Human Feedback [58.17897225617566]
The MineRL BASALT competition aims to spur forward research on this important class of techniques.
We design a suite of four tasks in Minecraft for which we expect it will be hard to write down hardcoded reward functions.
We provide a dataset of human demonstrations on each of the four tasks, as well as an imitation learning baseline.
arXiv Detail & Related papers (2021-07-05T12:18:17Z) - The Threat of Offensive AI to Organizations [52.011307264694665]
This survey explores the threat of offensive AI on organizations.
First, we discuss how AI changes the adversary's methods, strategies, goals, and overall attack model.
Then, through a literature review, we identify 33 offensive AI capabilities which adversaries can use to enhance their attacks.
arXiv Detail & Related papers (2021-06-30T01:03:28Z) - Building Bridges: Generative Artworks to Explore AI Ethics [56.058588908294446]
In recent years, there has been an increased emphasis on understanding and mitigating adverse impacts of artificial intelligence (AI) technologies on society.
A significant challenge in the design of ethical AI systems is that there are multiple stakeholders in the AI pipeline, each with their own set of constraints and interests.
This position paper outlines some potential ways in which generative artworks can play this role by serving as accessible and powerful educational tools.
arXiv Detail & Related papers (2021-06-25T22:31:55Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.