Causal Argumentative Learning Assistant

Introduction

Causal AI is widely perceived as crucial in the current AI landscape as it allows capturing causal effects amongst features in data, rather than simple correlations.

Importance of Causal Discovery

Causal discovery is an important aspect of machine learning as it paves the way towards achieving Causal AI. It amounts to extracting causal graphs from data, encoding the structure of causal relations amongst features in data.

Current Challenges

There is a gap in the state-of-the-art in AI as concerns causal discovery, in that most approaches are black boxes, hard to understand and explain, and unable to engage domain experts to integrate and possibly contest the learnt graphs when they are misaligned with human knowledge and values.

Objectives of CArLA

CArLA aims at developing a platform for:

• Transparent
• Explainable
• Interactive
• Contestable causal discovery

This platform will be based upon an existing principled methodology and prototype, as well as XAI techniques, developed within the ERC Advanced ADIX project.

Benefits of the Platform

CArLA’s platform will support understanding the causal discovery process from data and experts while being able to influence it. Any developer or user of applications in high-stakes domains will benefit from using this uniquely trustworthy platform.

Demonstrators and Commercialization

CArLA aims to develop two demonstrators of the beneficial use of the platform in the high-stakes domains of healthcare and finance, to pave the way to commercialization with a spinout.