Skip to main content
Go to the home page of the European Commission (opens in new window)
English en
CORDIS - EU research results
CORDIS

ODD-ML: Out-of-Distribution Deployable Machine Learning

Project description

Rethinking how AI adapts to the real world

Machine learning powers everything from medical diagnostics to climate models. However, moving from the lab to the real world is not easy. Data shifts, unforeseen conditions, and human complexity can challenge even the best algorithms. The ERC-funded ODD-ML project aims to solve this problem by putting people back at the centre of machine learning. Instead of relying solely on past data, ODD-ML will design AI systems that actively learn from human experts. Specifically, the machines will capture human intuition, experience, and even biases. By bridging human insight and machine reasoning, the project seeks to create AI that adapts more intelligently to new situations, boosting trust, reliability, and real-world impact.

Objective

ODD-ML addresses the open secret of machine learning (ML), which is that model deployment often fails. The problem arises because deployment contexts may differ from the data used to train ML models in unexpected ways. In an increasingly data-driven era, this severely impedes progress in ML-powered R&D and our ability to tackle societal grand challenges with existing ML tools.

To solve this pervasive issue, I propose a radical alternative to current ML approaches, placing human experts at the core of iterative design-build-test-learn (DBTL) loops. My approach comprises the interlinked steps of re-conceptualizing the deployment issue as a need for active learning from domain experts and other indirect sources and, to succeed here, recognizing the imperfect and often tacit knowledge and limited time of human experts, designing ML systems that can rapidly reverse-engineer expert knowledge.

I will achieve this with a combination of ideas transformative for human-AI collaboration: human inductive biases will be inferred from computational-rationality-based cognitive models, amortized on pre-computed solutions for speed, allowing interactive online use. I envision widespread impact in ML, on complex decision-making, and broadly across R&D domains.

Fields of science (EuroSciVoc)

CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
This project's classification has been human-validated.

Programme(s)

Multi-annual funding programmes that define the EU’s priorities for research and innovation.

Topic(s)

Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.

Funding Scheme

Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.

HORIZON-ERC - HORIZON ERC Grants

See all projects funded under this funding scheme

Call for proposal

Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.

(opens in new window) ERC-2024-ADG

See all projects funded under this call

Host institution

AALTO KORKEAKOULUSAATIO SR
Net EU contribution

Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.

€ 2 495 282,00
Address
OTAKAARI 1
02150 Espoo
Finland

See on map

Region
Manner-Suomi Helsinki-Uusimaa Helsinki-Uusimaa
Activity type
Higher or Secondary Education Establishments
Links
Total cost

The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.

No data

Beneficiaries (1)

My booklet 0 0