AI Safety Subproblems for Software Engineering Researchers
- URL: http://arxiv.org/abs/2304.14597v3
- Date: Thu, 31 Aug 2023 04:26:52 GMT
- Title: AI Safety Subproblems for Software Engineering Researchers
- Authors: David Gros, Prem Devanbu, Zhou Yu
- Abstract summary: We briefly summarize long-term AI Safety, and the challenge of avoiding harms from AI as systems meet or exceed human capabilities.
We make conjectures about how software might change with rising capabilities, and categorize "subproblems" which fit into traditional SE areas.
- Score: 20.606264558332498
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: In this 4-page manuscript we discuss the problem of long-term AI Safety from
a Software Engineering (SE) research viewpoint. We briefly summarize long-term
AI Safety, and the challenge of avoiding harms from AI as systems meet or
exceed human capabilities, including software engineering capabilities (and
approach AGI / "HLMI"). We perform a quantified literature review suggesting
that AI Safety discussions are not common at SE venues. We make conjectures
about how software might change with rising capabilities, and categorize
"subproblems" which fit into traditional SE areas, proposing how work on
similar problems might improve the future of AI and SE.
Related papers
- Safetywashing: Do AI Safety Benchmarks Actually Measure Safety Progress? [59.96471873997733]
We propose an empirical foundation for developing more meaningful safety metrics and define AI safety in a machine learning research context.
We aim to provide a more rigorous framework for AI safety research, advancing the science of safety evaluations and clarifying the path towards measurable progress.
arXiv Detail & Related papers (2024-07-31T17:59:24Z) - Using AI Assistants in Software Development: A Qualitative Study on Security Practices and Concerns [23.867795468379743]
Recent research has demonstrated that AI-generated code can contain security issues.
How software professionals balance AI assistant usage and security remains unclear.
This paper investigates how software professionals use AI assistants in secure software development.
arXiv Detail & Related papers (2024-05-10T10:13:19Z) - AI in Software Engineering: A Survey on Project Management Applications [3.156791351998142]
Machine Learning (ML) employs algorithms that undergo training on data sets, enabling them to carry out specific tasks autonomously.
AI holds immense potential in the field of software engineering, particularly in project management and planning.
arXiv Detail & Related papers (2023-07-27T23:02:24Z) - AI for IT Operations (AIOps) on Cloud Platforms: Reviews, Opportunities
and Challenges [60.56413461109281]
Artificial Intelligence for IT operations (AIOps) aims to combine the power of AI with the big data generated by IT Operations processes.
We discuss in depth the key types of data emitted by IT Operations activities, the scale and challenges in analyzing them, and where they can be helpful.
We categorize the key AIOps tasks as - incident detection, failure prediction, root cause analysis and automated actions.
arXiv Detail & Related papers (2023-04-10T15:38:12Z) - 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) - Proceedings of the Artificial Intelligence for Cyber Security (AICS)
Workshop at AAAI 2022 [55.573187938617636]
The workshop will focus on the application of AI to problems in cyber security.
Cyber systems generate large volumes of data, utilizing this effectively is beyond human capabilities.
arXiv Detail & Related papers (2022-02-28T18:27:41Z) - The application of artificial intelligence in software engineering: a
review challenging conventional wisdom [0.9651131604396904]
This survey chapter is a review of the most commonplace methods of AI applied to software engineering.
The review covers methods between years 1975-2017, for the requirements phase, 46 major AI-driven methods are found.
The purpose of this chapter is to answer the following questions: is there sufficient intelligence in the SE lifecycle?
arXiv Detail & Related papers (2021-08-03T15:59:59Z) - Trustworthy AI: A Computational Perspective [54.80482955088197]
We focus on six of the most crucial dimensions in achieving trustworthy AI: (i) Safety & Robustness, (ii) Non-discrimination & Fairness, (iii) Explainability, (iv) Privacy, (v) Accountability & Auditability, and (vi) Environmental Well-Being.
For each dimension, we review the recent related technologies according to a taxonomy and summarize their applications in real-world systems.
arXiv Detail & Related papers (2021-07-12T14:21:46Z) - Inspect, Understand, Overcome: A Survey of Practical Methods for AI
Safety [54.478842696269304]
The use of deep neural networks (DNNs) in safety-critical applications is challenging due to numerous model-inherent shortcomings.
In recent years, a zoo of state-of-the-art techniques aiming to address these safety concerns has emerged.
Our paper addresses both machine learning experts and safety engineers.
arXiv Detail & Related papers (2021-04-29T09:54:54Z) - Explainable AI for Software Engineering [12.552048647904591]
We first highlight the need for explainable AI in software engineering.
Then, we summarize three successful case studies on how explainable AI techniques can be used to address the aforementioned challenges.
arXiv Detail & Related papers (2020-12-03T00:42:29Z) - On Safety Assessment of Artificial Intelligence [0.0]
We show that many models of artificial intelligence, in particular machine learning, are statistical models.
Part of the budget of dangerous random failures for the relevant safety integrity level needs to be used for the probabilistic faulty behavior of the AI system.
We propose a research challenge that may be decisive for the use of AI in safety related systems.
arXiv Detail & Related papers (2020-02-29T14:05:28Z)
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.