Towards a Roadmap on Software Engineering for Responsible AI
- URL: http://arxiv.org/abs/2203.08594v1
- Date: Wed, 9 Mar 2022 07:01:32 GMT
- Title: Towards a Roadmap on Software Engineering for Responsible AI
- Authors: Qinghua Lu, Liming Zhu, Xiwei Xu, Jon Whittle, Zhenchang Xing
- Abstract summary: This paper aims to develop a roadmap on software engineering for responsible AI.
The roadmap focuses on (i) establishing multi-level governance for responsible AI systems, (ii) setting up the development processes incorporating process-oriented practices for responsible AI systems, and (iii) building responsible-AI-by-design into AI systems through system-level architectural style, patterns and techniques.
- Score: 17.46300715928443
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Although AI is transforming the world, there are serious concerns about its
ability to behave and make decisions responsibly. Many ethical regulations,
principles, and frameworks for responsible AI have been issued recently.
However, they are high level and difficult to put into practice. On the other
hand, most AI researchers focus on algorithmic solutions, while the responsible
AI challenges actually crosscut the entire engineering lifecycle and components
of AI systems. To close the gap in operationalizing responsible AI, this paper
aims to develop a roadmap on software engineering for responsible AI. The
roadmap focuses on (i) establishing multi-level governance for responsible AI
systems, (ii) setting up the development processes incorporating
process-oriented practices for responsible AI systems, and (iii) building
responsible-AI-by-design into AI systems through system-level architectural
style, patterns and techniques.
Related papers
- Imagining and building wise machines: The centrality of AI metacognition [78.76893632793497]
We argue that shortcomings stem from one overarching failure: AI systems lack wisdom.
While AI research has focused on task-level strategies, metacognition is underdeveloped in AI systems.
We propose that integrating metacognitive capabilities into AI systems is crucial for enhancing their robustness, explainability, cooperation, and safety.
arXiv Detail & Related papers (2024-11-04T18:10:10Z) - Combining AI Control Systems and Human Decision Support via Robustness and Criticality [53.10194953873209]
We extend a methodology for adversarial explanations (AE) to state-of-the-art reinforcement learning frameworks.
We show that the learned AI control system demonstrates robustness against adversarial tampering.
In a training / learning framework, this technology can improve both the AI's decisions and explanations through human interaction.
arXiv Detail & Related papers (2024-07-03T15:38:57Z) - Ten Hard Problems in Artificial Intelligence We Must Get Right [72.99597122935903]
We explore the AI2050 "hard problems" that block the promise of AI and cause AI risks.
For each problem, we outline the area, identify significant recent work, and suggest ways forward.
arXiv Detail & Related papers (2024-02-06T23:16:41Z) - Managing extreme AI risks amid rapid progress [171.05448842016125]
We describe risks that include large-scale social harms, malicious uses, and irreversible loss of human control over autonomous AI systems.
There is a lack of consensus about how exactly such risks arise, and how to manage them.
Present governance initiatives lack the mechanisms and institutions to prevent misuse and recklessness, and barely address autonomous systems.
arXiv Detail & Related papers (2023-10-26T17:59:06Z) - Responsible Design Patterns for Machine Learning Pipelines [10.184056098238765]
AI ethics involves applying ethical principles to the entire life cycle of AI systems.
This is essential to mitigate potential risks and harms associated with AI, such as biases.
To achieve this goal, responsible design patterns (RDPs) are critical for Machine Learning (ML) pipelines.
arXiv Detail & Related papers (2023-05-31T15:47:12Z) - Responsible AI Pattern Catalogue: A Collection of Best Practices for AI
Governance and Engineering [20.644494592443245]
We present a Responsible AI Pattern Catalogue based on the results of a Multivocal Literature Review (MLR)
Rather than staying at the principle or algorithm level, we focus on patterns that AI system stakeholders can undertake in practice to ensure that the developed AI systems are responsible throughout the entire governance and engineering lifecycle.
arXiv Detail & Related papers (2022-09-12T00:09:08Z) - Putting AI Ethics into Practice: The Hourglass Model of Organizational
AI Governance [0.0]
We present an AI governance framework, which targets organizations that develop and use AI systems.
The framework is designed to help organizations deploying AI systems translate ethical AI principles into practice.
arXiv Detail & Related papers (2022-06-01T08:55:27Z) - Responsible-AI-by-Design: a Pattern Collection for Designing Responsible
AI Systems [12.825892132103236]
Many ethical regulations, principles, and guidelines for responsible AI have been issued recently.
This paper identifies one missing element as the system-level guidance: how to design the architecture of responsible AI systems.
We present a summary of design patterns that can be embedded into the AI systems as product features to contribute to responsible-AI-by-design.
arXiv Detail & Related papers (2022-03-02T07:30:03Z) - Software Engineering for Responsible AI: An Empirical Study and
Operationalised Patterns [20.747681252352464]
We propose a template that enables AI ethics principles to be operationalised in the form of concrete patterns.
These patterns provide concrete, operationalised guidance that facilitate the development of responsible AI systems.
arXiv Detail & Related papers (2021-11-18T02:18:27Z) - 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) - Distributed and Democratized Learning: Philosophy and Research
Challenges [80.39805582015133]
We propose a novel design philosophy called democratized learning (Dem-AI)
Inspired by the societal groups of humans, the specialized groups of learning agents in the proposed Dem-AI system are self-organized in a hierarchical structure to collectively perform learning tasks more efficiently.
We present a reference design as a guideline to realize future Dem-AI systems, inspired by various interdisciplinary fields.
arXiv Detail & Related papers (2020-03-18T08:45:10Z)
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.