Badges
Badges enable people 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. People 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:
- Learner badge allows the recipient to show off their knowledge without a comprehensive assessment.
- Brainstormer badge demonstrates that the earner has helped We Count discover or outline solutions and approaches to in-house initiatives and inclusive design challenges.
- Collaborator badge demonstrates that the earner has participated in a co-creation or co-research activity.
- Communicator badge demonstrates that earners have delivered presentations, project reports and visualizations of findings culminated in earlier phases of a challenge activity.
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.
Earn a badge
Our initiatives and their corresponding badges are listed below.
Sentiment Analysis Workshop
Learner badge
You will learn:
- How small organizations and their members can complete a sentiment analysis and build their own data set that represents a concern of the disability community as part of our learning
- The process and the tools involved in the data ecosystem, as well as how to become a part of the data ecosystem
Designing Proof and Evidence with Minorities and Outlier Data
Learner badge
You will learn:
- How evidence-based decisions fall short of meeting the needs of marginalized, highly diverse communities
- The challenges of producing proof for a highly diverse group of small minorities and outliers who are marginalized
- How the passage of policy changes to Medical Assistance in Dying (MAiD), Bill C7, is devaluing the lives of persons with disabilities by singling out this group as not requiring a foreseeable death in order to access MAiD
Brainstormer badge
You will brainstorm:
- Acceptable forms of evidence, proof and data that could be produced by a highly diverse group of small minorities and outliers who are marginalized
- Alternative strategies and tools for minorities to collect and provide this evidence to policy makers
AI and Disability: A Double-Edged Sword
Learner badge
You will learn:
- The risks and rewards of AI for people with disabilities
- How future developments in different industries may impact the disability community
The Metric Society and the Unmeasurable
Learner badge
You will learn:
- How the rise of quantification has created and strengthened social hierarchies
- How quantification could lead to standardization but does not necessarily capture social diversity and deviance
- About the importance of bottom-up, community-driven initiatives in addressing the top-down mismanagement of personal data and access to social services
Building Indigenous Future Imaginaries
Learner badge
You will learn:
- How various Indigenous communities are contributing to AI technology to express and preserve their identity
- How Indigenous ways of knowing are contributing to the global conversation regarding society and AI
Inclusive AI for HR
Learner badge
You will learn:
- How AI in hiring systems impacts the employment of people with disabilities
- How to better develop AI-based hiring systems that are inclusive and transparent for people with disabilities
AI Hiring System Policies
Learner badge
You will learn:
- How legal frameworks and public policies can act against structural discrimination in candidate selection on the basis of disability
- About the challenges to policy regulations for AI hiring systems
AI Employment Systems
Learner badge
You will learn:
- How new innovative technology solutions can potentially mitigate hiring biases for people with disabilities
- How the “normative behaviour” screening is harming people with disabilities
AI and AT Apps
Learner badge
You will learn:
- How AI systems within various commercial software applications impact persons with disabilities and marginalized communities
- About accessibility features and design considerations present in current assistive technology applications
- By sharing your experience with technology in an AI-supported world in a succinct and organized format
Brainstormer badge
You will brainstorm:
- By using cases of AI in assistive technology apps to evaluate how they impact persons with disabilities and marginalized communities
- By exploring various accessibility features and design considerations present in current AI-powered mobile application technology
Communicator badge
You will communicate:
- Your enthusiasm and support in discussing AI in society and the implications for persons with disabilities and other groups
- A basic understanding of accessibility and usability in presentation documents using Microsoft Office or G Suite
- Your ability to synthesize qualitative data based on the content from the previous Brainstormer badge within a presentation
Bias In, Bias Out
Learner badge
You will learn:
- How predictive algorithms and data mining affect different populations in a discriminatory manner
- How specific data resources are used to train and reinforce machine learning models to produce biased outputs