Our values

In order for antibiotic development to benefit all Canadian communities equitably, and taking into account the differentiated impacts of antimicrobial resistance (AMR) on certain populations, the research activities of the PandemicStop-AI consortium are grounded in principles of social justice and sustainability.  

The consortium recognizes that AMR does not affect populations uniformly and that some groups are more vulnerable, including infants, older adults, people living with underlying healthconditions (immunocompromised individuals or those with chronic diseases), First Nations, Inuit and Métis populations, people with disabilities, people of sexual and gender diversity, as well as those living in low-resource settings. 

In this context, the consortium’s scientific mission naturally aligns with an EDI approach aiming to generate knowledge, tools, and innovations that can help reduce these health inequities. The integration of EDI is thus conceived as a cross-cutting principle, applied both in the conduct of research projects and in governance structures, partnership development, and the animation of the consortium’s scientific ecosystem. From this perspective, collaboration, dialogue and co-construction with all stakeholders, including more vulnerable populations, constitutecentral levers of the consortium’s EDI strategy. This strategy notably seeks to raise awareness, build capacity, and foster accountability among those involved at all stages of research activities, from project design to the dissemination of results. 

In this regard, PandemicStop-AI is actively engaged within the AMRQ (AntiMicrobial Resistance Québec) network, which aims to bring together Québec stakeholders committed to fighting AMR and to identify concrete avenues for action, collaboration and the collective structuring of efforts at the provincial level. 

In line with these commitments, integrating the notion of social impact into the analysis, interpretation, and dissemination of results from AI-based research is essential. This approach helps ensure that the knowledge produced is both scientifically rigorous and attentive to its societal implications, particularly with respect to its effects on populations most exposed to the risks associated with AMR. Members of the PandemicStop-AI consortium thus acknowledge the existence of algorithmic bias in AI systems and affirm the importance of identifying, assessing and mitigating such biases to prevent and reduce the potentially discriminatory effects that may result.