A collection of peer-reviewed research articles and conference proceedings from PEER project.
Deliverables of the PEER project, public deliverables can be downloaded.
D2.2 - AI co-creation representations The aim of task 2.2 is to develop representations of AI systems so that parts of the AI system become design materials in workshops with end-users. D4.1 - Measurement scales and metrics for trustworthy AI T4.1 focused on identifying reliable measurement scales for evaluating trustworthy AI, forming the foundation of the AIA index. This deliverable (D4.1) defines key concepts of trust and acceptance and compiles measurement tools to support the AIA index development. D6.3 - Dissemination & Communication plan The dissemination and communication plan is the main guideline for the execution of the D&C activities. It gives an overview of the whole D&C activities foreseen throughout the project. D2.3 - Low-fidelity simulation method workshop format This deliverable describes the work done in T2.3 “Low-fidelity simulation methods for AI systems”. The aim of this task is to define, describe, and summarise the process of simulation development, testing, and evaluation. In doing so, it focuses first on the need for using simulation methods when developing AI systems. D3.1 - MVP of the PEER collaborative AI assistant for lab evaluation D3.1 report presents a comprehensive literature review in progress as well as ongoing stakeholder discussions across multiple use cases. The report points out key deficiencies in current AI solutions that the literature review has identified, including inflexible search and planning methods, lack of transparency in recommender systems, limited interactivity in explainable AI (XAI), and rigid conversation management techniques. D3.2 - First planning algorithms, XAI and user modelling, and human-AI conversation loop for use-cases after lab evaluation report D3.2 report presents a comprehensive view of our ongoing research as well as its application across our four use cases. The report points out our progress on AI explainability, user modelling, and AI-user interaction in general, as well as on research topics specifically motivated by our use cases, such as anomaly detection, multi-objective routing, or LLM-mediated optimization. D5.3 - Report for Integration and Technical Validation (1st Version) This deliverable provides a 1st version of the demonstrator (MVP) of the conversation of human and AI showing a basic set of functions illustrating the principles of the conversation flow. Use cases will provide a partial data and messages integration for a limited communication flow, which demonstrates the abilities of the technology and facilitates further refinement in versions 2 and 3. D5.4 - Report for Integration and Technical Validation (2nd Version) This deliverable provides a 2nd version of the demonstrator (MVP) of the conversation between human and AI showing a basic set of functions illustrating the principles of the conversation flow. Use cases will provide a partial data and messages integration for a limited communication flow, which demonstrates the abilities of the technology and facilitates further refinement in version 3. D5.6 - First report on the evaluation process Deliverable D5.6 Report on the Evaluation Process presents the methodology, the results and perspectives on the evaluation of the MVP (Minimal Viable Product) for each use-case. This evaluation will be performed 3 times iteratively to improve the prototypes, from partially simulated (TRL 3, Year 2) to functional in real conditions (TRL 5, Year 4) prototypes. D6.4 - Report on dissemination & communication activities – M24 The following document is the “Report on dissemination & Communication activities M24” conducted and organised by ALPHA, leader of Task 6.3 “Dissemination and Communication” for the PEER project. In the following text an overview of all communication and dissemination actions carried out to date is available. D6.6 - 1st report on Scientific Dissemination The present deliverable includes a description of the scientific dissemination strategies and results achieved so far by the PEER consortium partners for external and internal dissemination activities. It is closely linked to D6.3. Dissemination and Communication(D&C) plan which outlines dissemination strategies at the project level.This area includes the collection of the PEER past newsletters.
Issue 7 Issue 6 Issue 5 Issue 4 Issue 3 Issue 2 Issue 1In this section you will find our liaisons and partners.
TANGO
TANGO is a €7M EU-funded Horizon Europe project that aims to develop the theoretical foundations and the computational framework for synergistic human-machine decision making. The 4-year project will pave the way for the next generation of human-centric AI systems. The goal is to fully develop the enormous potential that AI holds in terms of positive impact on individuals, society and economy by establishing a symbiosis between humans and machines: people should feel they can trust the systems they interact with, in terms of reliability of their predictions and decisions, capacity of the systems to understand their needs, and guarantees that they are genuinely aiming at supporting them rather than some undisclosed third party. TANGO focuses on developing the theoretical foundations and the computational framework for synergistic human-machine decision making, framework that will be tested on four real-life case studies.
HumAIne
HumAIne researches, develops, validates, and promotes a novel Operating System for Human-AI collaboration. This system facilitates advanced decision-making applications in dynamic, unstructured environments across various industrial sectors. By leveraging cutting-edge technologies such as Active Learning (AL), Neuro-Symbolic Learning (NL), Swarm Learning (SL), and eXplainable AI (XAI), all integrated into the HumAIne OS platform, HumAIne empowers AI solution integrators to create collaboration systems that surpass the capabilities of AI and humans working independently.
AI4REALNET
HumAIne researches, develops, validates, and promotes a novel Operating System for Human-AI collaboration. This system facilitates advanced decision-making applications in dynamic, unstructured environments across various industrial sectors. By leveraging cutting-edge technologies such as Active Learning (AL), Neuro-Symbolic Learning (NL), Swarm Learning (SL), and eXplainable AI (XAI), all integrated into the HumAIne OS platform, HumAIne empowers AI solution integrators to create collaboration systems that surpass the capabilities of AI and humans working independently.
THEMIS 5.0
THEMIS 5.0 is making AI decisions easier to understand. We are accelerating the shift towards more trusted AI-enabled services by helping people unpack the 'black box' that are AI algorithms to better understand what data is used, and how decisions are reached, so they can influence improvements. Using a risk-based approach, our AI trustworthiness framework and intelligent coach will create an ecosystem through which AI-driven hybrid decision making occurs in accordance with the needs and moral values of specific users. Our co-creation, development and testing processes are taking place across three use case domains - healthcare, port logistics and journalism.
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