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Found 36 papers, 3 papers with code
Resolving Ethics Trade-offs in Implementing Responsible AI
While the operationalisation of high-level AI ethics principles into practical AI/ML systems has made progress, there is still a theory-practice gap in managing tensions between the underlying AI ethics aspects.
Navigating Privacy and Copyright Challenges Across the Data Lifecycle of Generative AI
The advent of Generative AI has marked a significant milestone in artificial intelligence, demonstrating remarkable capabilities in generating realistic images, texts, and data patterns.
Towards Responsible Generative AI: A Reference Architecture for Designing Foundation Model based Agents
Foundation models, such as large language models (LLMs), have been widely recognised as transformative AI technologies due to their capabilities to understand and generate content, including plans with reasoning capabilities.
Towards Self-Interpretable Graph-Level Anomaly Detection
In this paper, we investigate a new challenging problem, explainable GLAD, where the learning objective is to predict the abnormality of each graph sample with corresponding explanations, i. e., the vital subgraph that leads to the predictions.
FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare
This work describes the FUTURE-AI guideline as the first international consensus framework for guiding the development and deployment of trustworthy AI tools in healthcare.
Decentralised Governance-Driven Architecture for Designing Foundation Model based Systems: Exploring the Role of Blockchain in Responsible AI
Foundation models including large language models (LLMs) are increasingly attracting interest worldwide for their distinguished capabilities and potential to perform a wide variety of tasks.
Test-takers have a say: understanding the implications of the use of AI in language tests
To the best of our knowledge, this is the first empirical study aimed at identifying the implications of AI adoption in language tests from a test-taker perspective.
Enhancing Virtual Assistant Intelligence: Precise Area Targeting for Instance-level User Intents beyond Metadata
Although their capabilities of processing user intents have been developed rapidly, virtual assistants in most platforms are only capable of handling pre-defined high-level tasks supported by extra manual efforts of developers.
Distributed Trust Through the Lens of Software Architecture
Distributed trust is a nebulous concept that has evolved from different perspectives in recent years.
A Taxonomy of Foundation Model based Systems through the Lens of Software Architecture
The recent release of large language model (LLM) based chatbots, such as ChatGPT, has attracted huge interest in foundation models.
Implementing Responsible AI: Tensions and Trade-Offs Between Ethics Aspects
Many sets of ethics principles for responsible AI have been proposed to allay concerns about misuse and abuse of AI/ML systems.
Towards Responsible AI in the Era of Generative AI: A Reference Architecture for Designing Foundation Model based Systems
The release of ChatGPT has drawn huge interests on foundations models.
Blockchain-Empowered Trustworthy Data Sharing: Fundamentals, Applications, and Challenges
Second, we focus on the convergence of blockchain and data sharing to give a clear picture of this landscape and propose a reference architecture for blockchain-based data sharing.
Developing Responsible Chatbots for Financial Services: A Pattern-Oriented Responsible AI Engineering Approach
In this article, we first summarise the major challenges in operationalising responsible AI at scale and introduce how we use the Responsible AI Pattern Catalogue to address those challenges.
Federated Learning on Non-IID Graphs via Structural Knowledge Sharing
Inspired by this, we propose FedStar, an FGL framework that extracts and shares the common underlying structure information for inter-graph federated learning tasks.
Responsible AI Pattern Catalogue: A Collection of Best Practices for AI Governance and Engineering
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.
Decision Models for Selecting Federated Learning Architecture Patterns
Federated machine learning is growing fast in academia and industries as a solution to solve data hungriness and privacy issues in machine learning.
Towards a Roadmap on Software Engineering for Responsible AI
Although AI is transforming the world, there are serious concerns about its ability to behave and make decisions responsibly.
Responsible-AI-by-Design: a Pattern Collection for Designing Responsible AI Systems
In the meantime much effort has been put into responsible AI from the algorithm perspective, but they are limited to a small subset of ethical principles amenable to mathematical analysis.
Resource Management and Security Scheme of ICPSs and IoT Based on VNE Algorithm
Based on the above two problems faced by ICPSs, we propose a virtual network embedded (VNE) algorithm with computing, storage resources and security constraints to ensure the rationality and security of resource allocation in ICPSs.
Feature-context driven Federated Meta-Learning for Rare Disease Prediction
Additionally, a dynamic-weight based fusion strategy is proposed to further improve the accuracy of federated learning, which dynamically selects clients based on the accuracy of each local model.
M-FasterSeg: An Efficient Semantic Segmentation Network Based on Neural Architecture Search
In the search process, combine the self-attention network structure module to adjust the searched neural network structure, and then combine the semantic segmentation network searched by different branches to form a fast semantic segmentation network structure, and input the picture into the network structure to get the final forecast result.
AI Ethics Principles in Practice: Perspectives of Designers and Developers
As consensus across the various published AI ethics principles is approached, a gap remains between high-level principles and practical techniques that can be readily adopted to design and develop responsible AI systems.
CO-PILOT: COllaborative Planning and reInforcement Learning On sub-Task curriculum
Next, a bottom-up traversal of the tree trains the RL agent from easier sub-tasks with denser rewards on bottom layers to harder ones on top layers and collects its cost on each sub-task train the planner in the next episode.
Software Engineering for Responsible AI: An Empirical Study and Operationalised Patterns
These patterns provide concrete, operationalised guidance that facilitate the development of responsible AI systems.
Blockchain-based Trustworthy Federated Learning Architecture
To enhance the accountability and fairness of federated learning systems, we present a blockchain-based trustworthy federated learning architecture.
FLRA: A Reference Architecture for Federated Learning Systems
The proposed FLRA reference architecture is based on an extensive review of existing patterns of federated learning systems found in the literature and existing industrial implementation.
AI and Ethics -- Operationalising Responsible AI
In the last few years, AI continues demonstrating its positive impact on society while sometimes with ethically questionable consequences.
FedProto: Federated Prototype Learning across Heterogeneous Clients
Heterogeneity across clients in federated learning (FL) usually hinders the optimization convergence and generalization performance when the aggregation of clients' knowledge occurs in the gradient space.
A Survey on Federated Learning and its Applications for Accelerating Industrial Internet of Things
Federated learning (FL) brings collaborative intelligence into industries without centralized training data to accelerate the process of Industry 4. 0 on the edge computing level.
Architectural Patterns for the Design of Federated Learning Systems
Therefore, in this paper, we present a collection of architectural patterns to deal with the design challenges of federated learning systems.
Cooperative Heterogeneous Deep Reinforcement Learning
Numerous deep reinforcement learning agents have been proposed, and each of them has its strengths and flaws.
Dynamic Fusion based Federated Learning for COVID-19 Detection
To improve communication efficiency and model performance, in this paper, we propose a novel dynamic fusion-based federated learning approach for medical diagnostic image analysis to detect COVID-19 infections.
Blockchain-based Federated Learning for Failure Detection in Industrial IoT
Therefore, in this paper, we present a platform architecture of blockchain-based federated learning systems for failure detection in IIoT.
A Systematic Literature Review on Federated Machine Learning: From A Software Engineering Perspective
Federated learning is an emerging machine learning paradigm where clients train models locally and formulate a global model based on the local model updates.
