TAILOR: Foundations of Trustworthy AI – Integrating Reasoning, Learning and Optimization

Maximising opportunities and minimising risks associated with artificial intelligence (AI) requires a focus on human-centred trustworthy AI. This can be achieved by collaborations between research excellence centres with a technical focus on combining expertise in theareas of learning, optimisation and reasoning. Currently, this work is carried out by an isolated scientific community where research groups are working individually or in smaller networks. The EU-funded TAILOR project aims to bring these groups together in a single scientific network on the Foundations of Trustworthy AI, thereby reducing the fragmentation and increasing the joint AI research capacity of Europe, helping it to take the lead and advance the state-of-the-art in trustworthy AI. The four main instruments are a strategic roadmap, a basic research programme to address grand challenges, a connectivity fund for active dissemination, and network collaboration activities.

Info

(field of science)
- natural sciences
  - computer and information sciences
    - artificial intelligence
(funding body) EU Horizon 2020 Framework Programme (H2020)
- (programme) H2020-EU.2.1.1 – INDUSTRIAL LEADERSHIP - Leadership in enabling and industrial technologies - Information and Communication Technologies (ICT)
- (topic) ICT-48-2020 – Towards a vibrant European network of AI excellence centres
- (call for proposal) H2020-ICT-2018-20 – sub call H2020-ICT-2019-3
- (funding scheme) RIA – Research and Innovation action
(project acronym) TAILOR
- (project full title) Foundations of Trustworthy AI – Integrating Reasoning, Learning and Optimization
(grant agreement id) 952215
- (overall budget) € 12000000
- (EU overall contribution) € 12000000
- (EU contribution / UniBo) € 279653,75
(DOI) 10.3030/952215
- (CORDIS page) CORDIS
- (official site) https://tailor-network.eu
(start-end dates) 01/09/2020 – 31/08/2024

UniBo Unit

3 Publications / TAILOR

Evaluation Metrics for Symbolic Knowledge Extracted from Machine Learning Black Boxes: A Discussion Paper (paper in proceedings, 2022) — Federico Sabbatini, Roberta Calegari
Clustering-Based Approaches for Symbolic Knowledge Extraction (paper in proceedings, 2022) — Federico Sabbatini, Roberta Calegari
Symbolic Knowledge Extraction from Opaque Machine Learning Predictors: GridREx & PEDRO (paper in proceedings, 2022) — Federico Sabbatini, Roberta Calegari

3 Talks / TAILOR

Symbolic Knowledge Extraction from Opaque Machine Learning Predictors: GridREx & PEDRO (talk, 04/08/2022) — Roberta Calegari (Federico Sabbatini, Roberta Calegari)
Clustering-Based Approaches for Symbolic Knowledge Extraction (talk, 31/07/2022) — Roberta Calegari (Federico Sabbatini, Roberta Calegari)
Grounding Trustworthy AI: from high-level principles to technical requirements (talk, 25/07/2022) — Roberta Calegari (Roberta Calegari, Michela Milano, Catelijne Muller)

Tags: H2020 TAILOR

TAILOR: Foundations of Trustworthy AI – Integrating Reasoning, Learning and Optimization

TAILOR: Foundations of Trustworthy AI – Integrating Reasoning, Learning and Optimization

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