Seven Principles for Using AI to Protect and Dignify the teams behind the work
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Accountability
Each AI tool must have a designated human owner responsible for its output, usage, and performance. This ensures that every system is actively managed and that team members always have a clear escalation point.
Transparency
We disclose how AI is applied in client work and internal operations This includes ensuring transparency in Job Descriptions for entry level roles. Team members are informed when tasks shift to automation, so there is no ambiguity around role changes or performance metrics. We are committed to providing clarity with regard to hiring decisions that are driven by our client’s own AI initiatives.
Inclusion
AI must be validated for local relevance, language, cultural nuance, and job context. We do not implement systems that introduce bias or ignore the working realities of our teams in the Philippines, Mexico, South Africa, Canada, or beyond.
Enablement
No AI tool is introduced without training the people expected to use or interact with it. The outcome is a clearer workflow, fewer repetitive tasks, and increased capacity for team members to apply judgment and grow their skill sets.
Resilience
AI-related changes to job roles trigger a formal assessment of team impact. Where displacement risks exist, we invest in reskilling, reprofiling, and alternative internal pathways with the goal of job continuity
Redistribution
When AI leads to efficiency gains, we do not treat those as margin-only wins. A portion of value created must be reinvested into team wages, career development programs, and new roles that support or extend AI-driven work.
Protection
We apply strict data security, privacy, governance, and consent standards to all AI deployments. Tools that track or evaluate team members must include human oversight, documented criteria, and a right to appeal ensuring fairness and psychological safety.