We Count badges enable earners to showcase their proficiency in the growing fields of AI, data systems and inclusive data practices as well as other skills. Badges, once earned, will be owned completely by the earner and can be shared across social media platforms, such as LinkedIn and Instagram, to display their achievement and showcase what has been learned. These badges are designed to address the interests, skills and availability of diverse participants. Earners can expect to work with and learn from a range of experts:
- Industry and public service professionals in data systems
- Technical and research assistants that specialize in data science
- Inclusive design facilitators with expertise in inclusive co-design
The badges fall under a variety of categories, such as Learner, Brainstormer, Collaborator and Communicator, and can be undertaken independently or together as part of a continuing project.
How are badges earned?
We Count badges are earned by registering with and participating in We Count initiatives or challenges and completing assessments. You can “stack” your badges to earn new badges.
- Participate in a We Count initiative and follow the “apply for badge” link to CanCred Factory on the website or, in some cases, in an email that you receive from We Count
- Create or open your free CanCred account, which gives you your free badge CanCred Passport for storing your badges
- Apply for the badge
- Share your badge
See our currently available badges.
The Learner badge allows the recipient to show off their knowledge without a comprehensive assessment.
The Brainstormer badge demonstrates that the earner has helped We Count discover or outline solutions and approaches to in-house initiatives and inclusive design challenges.
The Collaboration badge demonstrates that the earner has participated in a co-creation or co-research activity.
The Communicator badge demonstrates that earners have delivered presentations, project reports and visualizations of findings culminated in earlier phases of a challenge activity.
Earn a badge
Learner: Bias in Machine Learning
Machine learning, a subset of artificial intelligence (AI), depends on the quality, objectivity and size of training data used to teach it. We Count encourages participants and learners to explore this concept to help inform more equitable decisions and supports by understanding data gaps and biases.
You will learn:
- How predictive algorithms and data mining affect different populations in a discriminatory manner, and
- How specific data resources are used to train and reinforce machine learning models to produce biased outputs.
Learn and earn badges from this event: