Funding program
The Applied AI Institute offers annual funding programs meant to advance its strategic directions and accelerate interdisciplinary research on AI at Concordia University.
Open Calls
AI2 Spring Call for Proposals: Collaborations with Industry
The Applied AI Institute invites applications to its annual funding program meant to accelerate AI adoption across sectors. The program funds new or on-going applied AI research projects in collaboration with industry or non-profit partners. The total funding commitment for this second year of the program is $500k, and out of this, we are expecting to fund approximately 10 projects.
Eligible expenses are limited to salaries and benefits for part-time or full-time research professionals. Research assistants and research associates are eligible; however, MSc or PhD bursaries or funding awards are not. Industry projects will be funded by the program at 50%, with the remaining 50% to be provided through a cash contribution by a private partner, which must include a minimum of 15% overhead fees. Please note that the industry partners’ contributions cannot come from provincial funds. Projects involving a non-profit organization will be funded at 100% by the program. Successful projects will have the potential to receive multi-year funding.
We encourage innovative proposals that address real-world challenges.
How to apply
To apply, please complete this form by September 30th (extended deadline). In the form, you will need to provide the following information:
- Names and CVs of research team
- Project description
- Description of alignment with guiding principles
- Industry partner details
- Letter of support from the industry partner
- Budget
- Confirmation of eligibility
If you have any questions about your eligibility, or the application process, please email lindsay.rodgers@concordia.ca
Applications will be ranked by the Selection Committee using the following criteria:
Value | Criteria | Description |
---|---|---|
30 | Fit with AI2 guiding principles | Describe how the research program meaningfully responds to public interest, demonstrating a commitment to principles of equity and justice. |
30 | Feasibility | Highlight past research accomplishments and capacity to undertake the proposed program of research. |
40 | Encouragement of AI Adoption | Describe how the proposed research program will encourage the private sector or non-profit organizations to adopt artificial intelligence and the potential impact on the broader community. |
Previous Calls
Applications are now closed for 2023. Please check back or subscribe to the Applied AI Institute newsletter to learn about the next application period.
Funding Programs
We have offered the following funding programs:
- Matching Funding
- Approximately $25k available, up to $5K per call, for applied artificial intelligence research projects.
- We encourage early career researchers to apply and will prioritize funding projects evenly across the four faculty.
- Successful applicants commit to public engagement through a workshop, seminar, poster session, or other kind of event.
- Working Group Funding
- Approximately $45k available, up to $10k per call for inter-cluster and cluster-based working groups centered around a theme related to applied AI. This might be Art and AI, Life-sciences and AI, or other variations our members propose.
- We will provide up to 5k of funding for each working group. In addition, we will provide $1k honorariums each for 3-5 graduate students who will participate in the projects of the working group.
- Successful applicants commit to public engagement through a workshop, seminar, poster session, or other kind of event.
2022 AI Auditing Seed Funding
- Seed Projects (approximately $25k available) meant to accelerate research into the assessment, development, and auditing of AI systems including, but not limited to, identifying and auditing high-risk AI systems. Potential research projects may include: designing novel methods of algorithmic assessment; developing safe, ethical and trustable AI systems; and, innovative means to explain AI systems to better public knowledge and accountability.
How to Apply
Expressions of intent for all applications must include:
- A one-page statement about the proposed research or grant application that includes:
- A summary of the research program for a non-specialist audience;
- A statement on how your team composition and program of research advances AI2’s strategic directions, with particular emphasis on how you address equity, justice, and structural barriers to participation;
- Evidence of past research success and capacity to undertake current research; and,
- Possibilities for future collaborations or grant applications.
- In the case of funding matching, a description of the granting program and when you expect to be notified of your results
- A list of collaborations with their academic affiliations.
- A one-page preliminary budget statement.
- An up-to-date CV of the Principal Investigator and key collaborators.
Evaluation Criteria
Applications will be ranked based by the Operating Committee using the following criteria:
Interdisciplinarity of team
Describe how your team advances interdisciplinary collaboration, inviting members from at least two faculties (e.g. Arts and Science, Management, Engineering).
Potential for new collaboration / new grants
Identify potential opportunities and next steps for research grant applications and/or new academic partnerships.
Feasibility
Highlight past research accomplishments and capacity to undertake the proposed program of research
Fit with AI2 guiding principles.
Describe how the research program meaningfully responds to public interest, demonstrating a commitment to principles of equity and justice. Additionally, explain how you have addressed barriers that could prevent participation from underrepresented groups.
Applied AI Institute Guiding Principles
- Engaged leadership caring for AI’s community and consequences;
- Distributed research that considers collaboration, mutual aid, and academic freedom as key drivers of success;
- Equity and justice acknowledging that bias is structural as well as statistical;
- Inter-disciplinarity in theory and practice, knowing that data is social and technical;
- Public interest to ensure AI improves, not entrenches, the status quo