Turning Secondhand Textile Records into Collective Evidence

Independent collectors, sorters and exporters of secondhand textiles hold valuable information about the materials they handle and the jobs they support. Yet that information often sits in filing cabinets, spreadsheets and accounting systems, making it hard to present a unified picture of the sector’s economic and environmental contribution. Chris McLellan, speaking at the SMART 2026 Annual Convention in Montréal, argued that artificial intelligence could help bridge this gap by turning existing paperwork into comparable data.

The core problem is not a lack of records but their dispersal. Each business knows how much material it processes, where it goes, and how much is reused or recycled. However, when policymakers ask for industry-wide figures, the evidence remains fragmented across thousands of operators. McLellan described the situation as having the evidence exist but scattered, which weakens the sector’s ability to advocate for itself.

How AI can assemble the pieces

McLellan proposes using AI to extract agreed-upon information from documents that businesses already produce, such as scale tickets, invoices, container packing lists and export declarations. A business could upload a PDF or photograph a document, and an application would pull out details like weight, grade, origin and destination. The system could then standardise formats and convert units, making records from different operators comparable.

To demonstrate the concept, McLellan built a basic prototype for his presentation. With AI assistance, it took less than an hour and cost virtually nothing. While this is a starting point rather than a fully operational system, it shows that the technical barrier is lower than many assume.

He advocates a visible process where businesses review and approve extracted information before submission. More autonomous AI agents could eventually reduce the workload further, but that would require greater confidence in the system.

Trust comes before technology

The bigger challenge is persuading competitors to share data. Information about volumes, buyers, grades and prices is commercially sensitive, and business owners have legitimate concerns about who might access it. McLellan acknowledges that owners worry, reasonably, that shared data could be seen by competitors, used against them, or slip out of their control.

His proposed approach starts with minimising the information collected, removing identifying details at source, and letting contributors decide which projects their data can support. For example, a company might allow its information to inform policy advocacy without agreeing to its use in industry benchmarking. He calls this a “Bill of Rights for Data Owners.” Contributors should also be able to withdraw their information, and copies should be minimised so that control remains meaningful.

An association or nonprofit would be the natural organiser, but participation should rest on clear governance rather than institutional reputation alone. McLellan advocates a written charter, independent scrutiny and rules that prevent larger businesses from dominating.

Why the timing matters

McLellan sees particular urgency around extended producer responsibility (EPR) and decisions affecting textile exports. His argument is that businesses need evidence of their activities if they want those activities properly reflected in policy and funding discussions. Who receives money from EPR programs is an economic question. Whether export markets remain open is a demand question. Where infrastructure money goes depends on what volumes people believe exist.

Data collaboration would not resolve the sector’s underlying commercial pressures. McLellan acknowledges shipping costs, infrastructure constraints, contamination and market demand as substantive barriers. As he put it, data itself doesn’t sort a single bale. Its purpose is to strengthen the industry’s participation in decisions about those problems.

AI needs checking, too

Using AI to produce regulatory evidence brings risks. McLellan warns that systems can misread documents, confuse units or invent missing information. A mistake repeated across submissions could undermine confidence in the wider dataset. He therefore calls for every data entry to remain traceable to an original document held by the contributing business. Human approval before submission and checks for anomalies would provide additional safeguards.

There is another risk: leaving the industry poorly documented while AI systems increasingly mediate how people learn about it. McLellan worries that limited studies can become disproportionately influential when broader evidence is unavailable. When an industry has no numbers, other people’s numbers become evidence by default.

His case is for collective visibility without surrendering individual control. AI could make assembling the evidence easier, but businesses need a reason to contribute, and confidence that doing so will not compromise them.