The cycle every engagement follows, the commitments CAWS holds itself to, and what it means by the words it uses.
A world where no AI system is deployed in a workplace without workers having the knowledge to evaluate it, the standing to challenge it, and the power to change it. And where that standard is reflected in law, in collective agreements, and in the practice of every employer and institution that touches working people's lives.
CAWS runs a structured worker-feedback loop that surfaces how AI is actually operating, interprets the risks and opportunities with the workers experiencing them, and turns that evidence into decisions the organization can act on. Individual responses are confidential and reported only in aggregate. Smaller engagements run a shorter version of the same cycle around a single decision.
Identify which AI and algorithmic systems are present in the workplace and which work decisions they affect. This step routinely surfaces systems leadership did not know were running.
Collect worker experience directly through a structured survey designed to disconfirm as well as confirm. It asks about capability and benefit alongside harm, so the findings reflect what workers actually report.
Survey results determine what comes next. Interviews and focus groups investigate the issues, roles, and sites workers prioritized, rather than a fixed script.
Quantitative and qualitative evidence are analyzed together to identify patterns, variation across roles and sites, risks, useful applications, and capability gaps.
Findings return to workers before they are treated as final. CAWS does not present a finding that workers have not had the chance to review.
Leadership receives specific decisions, options, and tradeoffs, not a stack of findings. Every engagement ends in a decision session.
The organization moves on training, governance measures, bargaining language, reporting processes, or further research. Conditions are measured again, and the loop continues.
The people doing the work decide what matters, and no finding is treated as final until workers have reviewed it.
It is unaffiliated and available to any workforce, union or not, and its findings and resources are its own, offered for the common good.
These systems can create new hazards for the people working under them. Accountability for those hazards sits with the people who build and deploy the systems, not the workers who carry them.
Findings are reported only in aggregate, and no worker is identified to their employer or exposed for taking part.
It equips workers to weigh the risks and the benefits on their own terms, and to set the safety and governance rules needed to protect their health, safety, and livelihoods.
Anyone whose work is directed, monitored, measured, or evaluated by an AI or algorithmic system, whatever their sector, title, or employment arrangement. That includes office, professional, and technical staff as much as frontline and field roles, and people who would not describe themselves as workers at all.
AI is a broad field, and CAWS does not take a position on it in the abstract. What CAWS works on is concrete. The specific systems that make or shape decisions at work. The scanner that sets the pace, the scheduler that assigns the hours, the score that flags someone for review, the camera that watches the floor, the model that recommends who gets hired or disciplined. These are the tangible things a workplace can examine, question, and make safer. Not every one is called AI, and the label matters less than what the system decides.
The basic terms of being treated as a person at work when an AI or algorithmic system is involved in decisions about you. CAWS names these terms as:
If any of this maps to what you are seeing where you work, get in touch.