Future of Work: Equitable Digital Systems (EDS) project team is excited to share the outcomes of a nearly three-year collaboration — during COVID.

Clusive is a free, flexible, adaptive and customizable learning environment. It is a web application for students and teachers that addresses access and learning barriers present in digital and open learning materials.

For this article, Antranig is considering this problem in the context of corporate apologies for technological practice and initiatives such as data feminism that seek to transfer power from privileged groups to those at the margins of society.

The Future of Work and Disability project formed a study group to understand and examine intersecting topics of AI, automation, standards and employment as they relate to persons with disabilities.

To say that the route to almost all essential services is through digital systems is stating the obvious. Despite this reality, more than twenty-five years of regulations requiring accessible digital technology, a plethora of guidelines and resources to support accessibility, and a thriving industry checking and repairing inaccessible websites, people who require alternative ways to access digital systems face ever-multiplying barriers.

This video presentation examines how AI technology is utilized in recruitment and selection, its implications for candidates with disabilities, and the question of accessibility and diversity.

In May, we completed our second set of co-design sessions with the Equitable Digital Systems (EDS) project. EDS is a project that explores how to make digital systems more inclusive for persons with disabilities in the workplace.

Learning data science comes with a steep learning curve, and inaccessible tutorials, resources and tools create barriers. To support a more inclusive environment in the field of data science, We Count has created a resource library.

These automated hiring and matching algorithms, implemented by major corporations such as LinkedIn, Amazon and others can be positioned in the wider context of automated processes, that use machine learning/AI algorithms, and support the infrastructure of society. These systems inevitably result in inequitable outcomes.

The pluralistic data infrastructure supports communities in taking collective ownership of data that relates to them and curating its relationships with data from other sources.