Legal Aspects for Software Developers Interested in Generative AI Applications
- URL: http://arxiv.org/abs/2404.16630v1
- Date: Thu, 25 Apr 2024 14:17:34 GMT
- Title: Legal Aspects for Software Developers Interested in Generative AI Applications
- Authors: Steffen Herbold, Brian Valerius, Anamaria Mojica-Hanke, Isabella Lex, Joel Mittel,
- Abstract summary: Generative Artificial Intelligence (GenAI) has led to new technologies capable of generating high-quality code, natural language, and images.
The next step is to integrate GenAI technology into products, a task typically conducted by software developers.
This article sheds light on the current state of two such risks: data protection and copyright.
- Score: 5.772982243103395
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Recent successes in Generative Artificial Intelligence (GenAI) have led to new technologies capable of generating high-quality code, natural language, and images. The next step is to integrate GenAI technology into products, a task typically conducted by software developers. Such product development always comes with a certain risk of liability. Within this article, we want to shed light on the current state of two such risks: data protection and copyright. Both aspects are crucial for GenAI. This technology deals with data for both model training and generated output. We summarize key aspects regarding our current knowledge that every software developer involved in product development using GenAI should be aware of to avoid critical mistakes that may expose them to liability claims.
Related papers
- Computational Safety for Generative AI: A Signal Processing Perspective [65.268245109828]
computational safety is a mathematical framework that enables the quantitative assessment, formulation, and study of safety challenges in GenAI.
We show how sensitivity analysis and loss landscape analysis can be used to detect malicious prompts with jailbreak attempts.
We discuss key open research challenges, opportunities, and the essential role of signal processing in computational AI safety.
arXiv Detail & Related papers (2025-02-18T02:26:50Z) - "So what if I used GenAI?" -- Implications of Using Cloud-based GenAI in Software Engineering Research [0.0]
This paper sheds light on the various research aspects in which GenAI is used, thus raising awareness of its legal implications to novice and budding researchers.
We summarize key aspects regarding our current knowledge that every software researcher involved in using GenAI should be aware of to avoid critical mistakes that may expose them to liability claims.
arXiv Detail & Related papers (2024-12-10T06:18:15Z) - SoK: Watermarking for AI-Generated Content [112.9218881276487]
Watermarking schemes embed hidden signals within AI-generated content to enable reliable detection.
Watermarks can play a crucial role in enhancing AI safety and trustworthiness by combating misinformation and deception.
This work aims to guide researchers in advancing watermarking methods and applications, and support policymakers in addressing the broader implications of GenAI.
arXiv Detail & Related papers (2024-11-27T16:22:33Z) - Ethics of Software Programming with Generative AI: Is Programming without Generative AI always radical? [0.32985979395737786]
The paper acknowledges the transformative power of GenAI in software code generation.
It posits that GenAI is not a replacement but a complementary tool for writing software code.
Ethical considerations are paramount with the paper advocating for stringent ethical guidelines.
arXiv Detail & Related papers (2024-08-20T05:35:39Z) - Risks and Opportunities of Open-Source Generative AI [64.86989162783648]
Applications of Generative AI (Gen AI) are expected to revolutionize a number of different areas, ranging from science & medicine to education.
The potential for these seismic changes has triggered a lively debate about the potential risks of the technology, and resulted in calls for tighter regulation.
This regulation is likely to put at risk the budding field of open-source generative AI.
arXiv Detail & Related papers (2024-05-14T13:37:36Z) - A Survey on Responsible Generative AI: What to Generate and What Not [15.903523057779651]
This paper investigates the practical responsible requirements of both textual and visual generative models.
We outline five key considerations: generating truthful content, avoiding toxic content, refusing harmful instruction, leaking no training data-related content, and ensuring generated content identifiable.
arXiv Detail & Related papers (2024-04-08T17:53:21Z) - Generative Artificial Intelligence for Software Engineering -- A
Research Agenda [8.685607624226037]
We conducted a literature review and focus groups for a duration of five months to develop a research agenda on GenAI for Software Engineering.
Our results show that it is possible to explore the adoption of GenAI in partial automation and support decision-making in all software development activities.
Common considerations when implementing GenAI include industry-level assessment, dependability and accuracy, data accessibility, transparency, and sustainability aspects associated with the technology.
arXiv Detail & Related papers (2023-10-28T09:14:39Z) - Identifying and Mitigating the Security Risks of Generative AI [179.2384121957896]
This paper reports the findings of a workshop held at Google on the dual-use dilemma posed by GenAI.
GenAI can be used just as well by attackers to generate new attacks and increase the velocity and efficacy of existing attacks.
We discuss short-term and long-term goals for the community on this topic.
arXiv Detail & Related papers (2023-08-28T18:51:09Z) - A Comprehensive Survey of AI-Generated Content (AIGC): A History of
Generative AI from GAN to ChatGPT [63.58711128819828]
ChatGPT and other Generative AI (GAI) techniques belong to the category of Artificial Intelligence Generated Content (AIGC)
The goal of AIGC is to make the content creation process more efficient and accessible, allowing for the production of high-quality content at a faster pace.
arXiv Detail & Related papers (2023-03-07T20:36:13Z) - Investigating Explainability of Generative AI for Code through
Scenario-based Design [44.44517254181818]
generative AI (GenAI) technologies are maturing and being applied to application domains such as software engineering.
We conduct 9 workshops with 43 software engineers in which real examples from state-of-the-art generative AI models were used to elicit users' explainability needs.
Our work explores explainability needs for GenAI for code and demonstrates how human-centered approaches can drive the technical development of XAI in novel domains.
arXiv Detail & Related papers (2022-02-10T08:52:39Z)
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.