# data[].attributes.wish_content.attachment_props.question
How great is the need for guidance and best practices around integrating AI-derived LMI and in some cases, AI-derived sources of data for policy and program decision-making?

# data[].attributes.wish_content.attachment_props.poll_choices[].choice_text
Not at all (I do what I want when I want how I want)

# data[].attributes.wish_content.attachment_props.question
Do you feel that Canada has enough guidelines and best practice around use of OJP data and AI-derived data/LMI?

# data[].attributes.wish_content.attachment_props.question
How much do you trust Online Job Postings data and AI-derived sources of data?

# data[].attributes.body
Very hard to enforce things with the private sector. The onus is on ourselves in terms of the methodologies and understand the caveats.

# data[].attributes.body
Is there's a caveat or an 'asterisk', we need to say something and be transparent. We need to be able to explain the data. this is what is means but here is what you need to take into account.

# data[].attributes.body
Ai is great for lit review- but it's not yet there for 'fine' labour market information.

# data[].attributes.body
Wouldn't have it driving boat but I'd have it as a passenger.

# data[].attributes.body
Conference Board's way of using it is interesting- using it as behind the scenes to supplement data.

# data[].attributes.body
Very skeptical of AI and find it hard to fact check- but it's improving.

# data[].attributes.body
Need for more discourse

# data[].attributes.body
One team has a very long list of caveats. They have cross-referencing procedures. A few years ago they did analyses on how job postings data change over time. they felt comfortable using the data with the caveats in mind.

# data[].attributes.body
They use it as a 'temperature check'.

# data[].attributes.body
Relationship between skills and jobs.

# data[].attributes.body
LMI is like the old fashioned rock bridge that brings you from one point to another and is made up large rocks (which can be statscan reports, or other studies) and small rocks (news stories, talking to those in sectors). The combination of 'rocks' together makes the crossing (or your LMI decision) all the stronger. AI is a smaller rock in this bridge at this time, but in time, can become a larger and stronger piece.

# data[].attributes.body
Need for guidance and best practices!