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

Interactive and Explainable Human-Centered AutoML

Project description

Democratising automated machine learning with humans in mind

Automated machine learning (AutoML) is a matter of trust and interactivity. They are both key factors in supporting developers and researchers, but, despite the huge progress in recent years, democratisation of machine learning via AutoML is yet to be achieved. On the contrary, the EU-funded ixAutoML project is designed with human users at its heart in several stages. The team aims to bring the best of two worlds together: human intuition and generalisation capabilities for complex systems, and efficiency of systematic optimisation approaches for AutoML. They believe that their timely human-centred Interactive and Explainable Human-Centered AutoML – ixAutoML – will have significant impact on making machine learning accessible to a much wider base .

Objective

Trust and interactivity are key factors in the future development and use of automated machine learning (AutoML), supporting developers and researchers in determining powerful task-specific machine learning pipelines, including pre-processing, predictive algorithm, their hyperparameters and--if applicable--the architecture design of deep neural networks. Although AutoML is ready for its prime time after it achieved impressive results in several machine learning (ML) applications and its efficiency improved by several orders of magnitudes in recent years, democratization of machine learning via AutoML is still not achieved. In contrast to previously purely automation-centered approaches, ixAutoML is designed with human users at its heart in several stages. First of all, the foundation of trustful use of AutoML will be based on explanations of its results and processes. Therefore, we aim for:

1. Explaining static effects of design decisions in ML pipelines optimized by state-of-the-art AutoML systems.
2. Explaining dynamic AutoML policies for temporal aspects of dynamically adapted hyperparameters while ML models are trained.

These explanations will be the base for allowing interactions, bringing the best of two worlds together: human intuition and generalization capabilities for complex systems, and efficiency of systematic optimization approaches for AutoML. Concretely, we aim for:

3. Enabling interactions between humans and AutoML by taking human's latent knowledge into account and learning when to interact.
4. Building first ixAutoML prototypes and showing its efficiency in the context of Industry 4.0.

Perfectly aligned with the EU's AI strategy and recent efforts on interpretability in the ML community, we strongly believe that this timely human-centered ixAutoML will have a substantial impact on the democratization of machine learning.

Keywords

Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)

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-2021-STG

See all projects funded under this call

Host institution

GOTTFRIED WILHELM LEIBNIZ UNIVERSITAET HANNOVER
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.

€ 1 459 763,00
Address
WELFENGARTEN 1
30167 Hannover
Germany

See on map

Region
Niedersachsen Hannover Region Hannover
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.

€ 1 459 763,00

Beneficiaries (1)

My booklet 0 0