Ethics of Software Programming with Generative AI: Is Programming without Generative AI always radical?

• URL: http://arxiv.org/abs/2408.10554v2
• Date: Thu, 31 Oct 2024 08:55:48 GMT
• Title: Ethics of Software Programming with Generative AI: Is Programming without Generative AI always radical?
• Authors: Marcellin Atemkeng, Sisipho Hamlomo, Brian Welman, Nicole Oyetunji, Pouya Ataei, Jean Louis K. E Fendji,
• Abstract summary: 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.
• Score: 0.32985979395737786
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper provides a comprehensive analysis of Generative AI (GenAI) potential to revolutionise software coding through increased efficiency and reduced time span for writing code. It acknowledges the transformative power of GenAI in software code generation, while also cautioning against the inherent risks of bias and errors if left unchecked. Emphasising the irreplaceable value of traditional programming, 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 to ensure GenAI serves the greater good and does not compromise on accountability in writing software code. It suggests a balanced approach, combining human oversight with AI's capabilities, to mitigate risks and enhance reliability. The paper concludes by proposing guidelines for GenAI utilisation in coding, which will empower developers to navigate its complexities and employ it responsibly. This approach addresses current ethical concerns and sets a foundation for the judicious use of GenAI in the future, ensuring its benefits are harnessed effectively while maintaining moral integrity.

Related papers

• Engineering Trustworthy AI: A Developer Guide for Empirical Risk Minimization [53.80919781981027]
  Key requirements for trustworthy AI can be translated into design choices for the components of empirical risk minimization. We hope to provide actionable guidance for building AI systems that meet emerging standards for trustworthiness of AI.
  arXiv  Detail & Related papers  (2024-10-25T07:53:32Z)
• Balancing Innovation and Ethics in AI-Driven Software Development [0.0]
  This paper critically examines the ethical implications of integrating AI tools like GitHub Copilot and ChatGPT into the software development process. It explores issues such as code ownership, bias, accountability, privacy, and the potential impact on the job market.
  arXiv  Detail & Related papers  (2024-08-10T14:11:22Z)
• 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)
• Legal Aspects for Software Developers Interested in Generative AI Applications [5.772982243103395]
  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.
  arXiv  Detail & Related papers  (2024-04-25T14:17:34Z)
• Genetic Auto-prompt Learning for Pre-trained Code Intelligence Language Models [54.58108387797138]
  We investigate the effectiveness of prompt learning in code intelligence tasks. Existing automatic prompt design methods are very limited to code intelligence tasks. We propose Genetic Auto Prompt (GenAP) which utilizes an elaborate genetic algorithm to automatically design prompts.
  arXiv  Detail & Related papers  (2024-03-20T13:37:00Z)
• Review of Generative AI Methods in Cybersecurity [0.6990493129893112]
  This paper provides a comprehensive overview of the current state-of-the-art deployments of Generative AI (GenAI) It covers assaults, jailbreaking, and applications of prompt injection and reverse psychology. It also provides the various applications of GenAI in cybercrimes, such as automated hacking, phishing emails, social engineering, reverse cryptography, creating attack payloads, and creating malware.
  arXiv  Detail & Related papers  (2024-03-13T17:05:05Z)
• 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)
• Innovating Computer Programming Pedagogy: The AI-Lab Framework for Generative AI Adoption [0.0]
  We introduce "AI-Lab," a framework for guiding students in effectively leveraging GenAI within core programming courses. By identifying and rectifying GenAI's errors, students enrich their learning process. For educators, AI-Lab provides mechanisms to explore students' perceptions of GenAI's role in their learning experience.
  arXiv  Detail & Related papers  (2023-08-23T17:20:37Z)
• Generation Probabilities Are Not Enough: Uncertainty Highlighting in AI Code Completions [54.55334589363247]
  We study whether conveying information about uncertainty enables programmers to more quickly and accurately produce code. We find that highlighting tokens with the highest predicted likelihood of being edited leads to faster task completion and more targeted edits.
  arXiv  Detail & Related papers  (2023-02-14T18:43:34Z)
• 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)
• Time for AI (Ethics) Maturity Model Is Now [15.870654219935972]
  This paper argues that AI software is still software and needs to be approached from the software development perspective. We wish to discuss whether the focus should be on AI ethics or, more broadly, the quality of an AI system.
  arXiv  Detail & Related papers  (2021-01-29T17:37:44Z)

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.