EJQ4 - Spring 2025 - Journal - Page 31
mental Team) that is experienced with writing Phase 1 ESAs. Our AI Team
monitors the latest industry trends and the strengths and weaknesses of
various approaches. This team, dedicated to advancing the practical adoption of AI within our organization, collaborated with our Environmental
Team, who worked closely with them to develop an AI-assisted solution to
support the preparation of Phase 1 ESAs.
The development was iterative, incorporating feedback, testing, and real
data over multiple (>20) iterations to re昀椀ne the solution’s capabilities
over simple to complex cases. Developing the tool involved turnkey software development, con昀椀guring Large Language Models, and integrating software services to extract data from large Ecolog reports, structure
it into a usable format, and then use AI to identify relevant information
based on guidance from the Environmental Team. Funding was allocated
not only for technological research and development and ongoing testing
and iterative improvements, but also for collaborative e昀昀orts across disciplines and departments.
During the iterative e昀昀ort, the Environmental Team provided the AI Team
with a number of Phase 1 ESAs of varying complexities from single 200 m²
properties in smaller municipalities with dozens of records to transit hubs
in Toronto where the study area could have thousands of historic records. The AI
Team reviewed the Phase 1 ESAs and designed the AI-assisted solution to output
data in formats compatible with our existing report templates, so it’s easy for our
Environmental Team to assess and manipulate.
As many environmental practitioners know, data quality of historic environmental records is inconsistent. Depending on the data source and its age, records in an
Ecolog report may be incomplete. Knowing this, the team also tested the accuracy
of the AI-assisted solution when there were gaps in the input data. The AI Team
was able to con昀椀gure the solution’s generative capabilities to address gaps where
they existed.
Outcomes and capabilities
Consistent with our previous experience with AI, content generated by AI may look
great until you review it in detail. Our experiments with the accuracy of AI’s generative capabilities led us to the conclusion that we cannot currently allow AI to
generate text that was not already included in inputted Ecolog reports. Hallucinated (i.e., false) content generated by AI was too easily missed by report writers and
senior reviewers and would be a legal liability. Data gaps in Ecolog reports require
human interpretation and explanation. Where data gaps exist in input reports, the
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