Charlie

Project information:

Official name of the project: CHARLIE

Duration of the project: 30.12.2022 - 29.6.2025

Project coordinator: VAMK

Project partners: ISQ e-learning (Portugal), Vaasa University of Applied Sciences (Finland), innovation training Center, S.L. (Spain), HELIXCONNECT EUROPE S.R.L (Romania), AARHUS UNIVERSITET (Denmark), University of the Balearic Islands UIB (Spain)

Funding: EU, Erasmus+

Platform: Smart Design

Project Code: xxxxxx

Language: Finnish (Translated to English)

Project Description

Challenging Bias in Big Data use for AI and Machine Learning – The challenge of data bias in teaching artificial intelligence and machine learning

Various AI solutions are becoming more widespread at a rapid pace. Although AI and machine learning are based on mathematics, they are not always correct. AI solutions may contain algorithmic bias, which can be discriminatory toward certain groups of people or minorities. For example, a platform that shows only a limited number of jobs to one gender is an example of discriminatory algorithmic bias.

“CHARLIE challenges bias and partiality in AI by promoting the ethical competences and approaches of different AI stakeholders. We must be able to create AI solutions that work fairly and transparently. CHARLIE is a step towards ethically better AI solutions,” says Project Manager Elise Raittila from Design Centre MUOVA.

Objectives

The aim of the CHARLIE project is to address the challenge of bias in big data by promoting ethical and inclusive approaches in the teaching of technology, AI, and machine learning.

Increase the ability of higher education institutions to provide students with online learning opportunities that meet societal needs and are tailored to students’ learning needs.
Develop engineering students’ social and ethical competences as they apply positive, critical, and ethical thinking and approaches to learning AI and machine learning.
Improve teachers’ capacity to effectively use digital and inclusive methods in teaching AI and machine learning (particularly in online education).
Create synergies between higher education, adult education, and youth education (ages 12–18) in teaching AI ethics.
Enable the transferability of higher education courses on AI bias to adult and vocational education.
Raise awareness of AI data bias at the societal level.