AI for the common good

The programme is organised around the different dimensions of innovation instead of around AI, because the specific context of an institution plays a crucial factor in innovation, and a better connecting factor than an unknown and often misunderstood scientific field like AI.

However, because of the many prejudices among novices in the domain of AI, and the wildly varying backgrounds, we will start with a compulsory module to introduce AI. Equally, we include a final and compulsory “case study” module where the students need to integrate what they have learnt, honing their holistic understanding of AI and innovation, as well as their interdisciplinary skills. These case studies will be published online and serve as marketing materials.

Please note that for the first edition of the program (February-June 2024), only people with a degree from a Belgian university will be able to apply.

For those with a degree from outside Belgium, applications will open in June 2024.

The program has been developed for professionals and recent graduates with an affinity for IT, and that are directly or indirectly involved, or wish to be involved in innovation with AI. This means the training is particularly useful for you in the following roles:

• Innovation, project and product managers
  
• Functional analyst, solution & enterprise architects
• Project officers at funding agencies
• Business developers
• HR managers and recruiters
• Legals

As FARI is supported by the European Resilience & Recovery Fund (RRF), we can provide a 50% discount to participants from the following target groups until the end of 2024:

• Public administrations, public institutions and research institutions
• Educational institutions
• NGOs and non-profit organisations
• Brussels based companies
• Brussels citizens

Module 1: Introduction to AI (4 days - compulsory)

Gives a basic introduction to the concepts and principles of AI. It explains what AI is, why it is an important evolution, how it works conceptually, what the limits are, and potential. It is mainly aimed to remove common misconceptions, create a common ground and language between the participants and empower them with a framework of understanding for future learnings.

• Thursday 1 February
• Tuesday 6 February
• Thursday 8 February
• Tuesday 20 February

Module 2: Technology & Data (3 days - compulsory)

Examines the technical and data aspects of AI: how does it work? What data should I collect? How to organise a proper data governance? What is the relation between software and hardware in AI? What is the impact on my technology stack? Who are the (cloud) service providers and what are mature Open-Source solutions?

• Tuesday 12 March
• Thursday 14 March
• Tuesday 19 March
  

Module 3: Governance & Organisation (4 days)

Looks at the impact on implementation and processes: what organisational structures to create to guarantee a trustworthy implementation of AI? How to monitor and evaluate AI (on the governance
level)? How to increase the privacy and security characteristics of solutions? What are potential threats? What are the regulations, frameworks and standards?

• Thursday 21 March
• Tuesday 26 March
• Thursday 28 March
• Tuesday 16 April
  

Module 4: People & Culture (3 days)

Zooms in on all the aspects related to people : how to create top performing teams? How to hire? What will the impact be on my staff? What training programmes should I organise? Whom should I involve in AI -related projects? What is the impact on society, on our values, on our thinking?

• Thursday 18 April
• Tuesday 23 April
• Thursday 25 April
  

Module 5: Economics & Business of AI (3 days - only available after following Modules 1-4)

Discusses the impact on the economy, on business models and pricing, and details how calculations on Return-on-Investment can be made. Also, more insights are given into the AI market.

• Dates tbd

Module 6: Case Study/Thesis (3 days - compulsory)

Each student is obliged to write a case study, either within their own company (lifelong learners) or with an external partner (fresh graduates). The learning goal is for students to integrate all what they have learnt and look at a holistic, interdisciplinary way at AI innovation.

The students are asked to identify the opportunities of AI within their institution and create a roadmap that considers the maturity of the organisation, and assesses the potential impact on ethics, legal, technical and governance structures (processes), business model, competitive position, people & training, data management, safety & security. It helps graduates orient. It allows employees to explore potential opportunities, determine the maturity of their organisation, and assess its strengths & weaknesses.

For each of the lectures/workshops, the students are expected to spend an equal amount of studying at home.

• Dates: tbd