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Generative AI Skills Challenge
Generative AI Skills Challenge

Our Design Philosophy

A foundational objective of the Generative AI Skills Challenge is to ensure that AI responsibly serves the public good by providing opportunities and solutions that will enable organizations to train and upskill the workforce in generative AI to keep worker skills relevant in the ever-changing digital economy. With that, it is critically important that we develop a design philosophy that accommodates applicants from communities and contexts with systemic inequities and a digital divide. Consequently, the following key considerations will be used to guide the application design and selection judging processes of the Challenge: 

  1. Ensure that generative AI serves the public good to transform society for the better, uplift people’s lives, and advance economic growth for individuals in underserved communities – and does not further widen the digital divide in these communities. 
  1. Avoid/mitigate the ills or negative aspects of this technology by enabling projects and local communities to determine the trustworthiness of its outputs and advance the Principles of Responsible Generative AI. 
  1. One size does not fit all: Underscore inclusion, diversity, equity, and access (IDEA) by observing that there are a diverse range of communities that can bring their expertise to bear on these issues but who are frequently left out of the generative AI conversation. Therefore, to meet the principles of IDEA, the engagement and design strategy will be based on regional representation and associated localized parameters (region as categorized by Microsoft’s global strategy):  
  • Africa 
  • Asia (includes Australia and New Zealand) 
  • Europe 
  • Latin America 
  • North America 
  1. Design for Human Systems – Ensure that training and upskilling are part of a holistic process i.e., award money, access to infrastructure including technical resources and assistance, and membership within an enabling ecosystem or community. 
  • With this approach, applicants should ask questions such as: What does this mean for the worker (and especially frontline worker) in this region? For local industries? How will the skills be introduced to the workforce (including addressing language barriers and translation needs)? Is the workforce ready to absorb this technology as is? How does it handle existing concerns that AI will replace humans? 
  1. Balance technology with Sociotech: Acknowledge the digital divide and gender inequality, and understand infrastructural challenges and existing policies across regions. Even if organizations lack access to large datasets, quality data, necessary tools, or data science expertise and resources, they may have great ideas for new approaches to solving local problems or offer suggestions for applying existing technologies to local contexts.