Technology, AI & the Automated City

A standalone candidate question bank — every figure sourced and checkable, every question identical for every candidate. Also part of the full twelve-category bank.

How to use this bank

Any Torontonian, any candidate, any office. These questions presuppose no position — they ask what a candidate would do, how they would decide, and how residents would check. Jurisdiction tags keep the questions honest about who holds the lever: [CITY] council/mayor/city boards control this directly; [CITY→PROVINCE] the lever is provincial, and the honest city question is what to ask Queen’s Park and what to do if the answer is no. Where a question carries a figure, it is cited below — candidates are welcome to dispute any figure; the source trail is public. This bank never endorses, scores, or ranks any candidate or party. The universal set at the foot works for any office on the ballot.

Technology, AI & the Automated City

12.1 [CITY] In 2025 Toronto drafted its own AI policy in-house and set out to build the first municipal algorithmic impact assessment in Canada. When a city algorithm makes or informs a decision about a resident — a queue, an inspection, a benefit — what should that resident be told, and what should they be able to appeal?

12.2 [CITY→PROVINCE] Ontario’s Automated Vehicle Pilot Program has been extended to October 2027, and a 2025 automated delivery-vehicle pilot was approved for Toronto streets by the province — with the city holding no regulatory authority over it, only invited comment. What conditions should the city seek over automated vehicles on its own streets, and how would you pursue that authority?

12.3 [CITY] New provincial rules will require privacy impact assessments before public institutions collect personal information, phasing in from July 2026. What standards would you hold the city’s technology vendors to on residents’ data — and what would you do when a vendor won’t meet them?

12.4 [CITY] Name the one city service whose digital experience you would fix first, and how the fix would also work for residents without smartphones, home internet, or English.

12.5 [CITY] Ontario’s municipalities employ more than 235,000 people, and automation will reach city work during the next term. What commitments would you make to the city’s workforce and to service quality as that happens — and who should decide which tasks are automated?

12.6 [CITY] Toronto Public Health is already piloting a predictive AI tool — drawing on more than 20 years of restaurant-inspection records, plus 311 calls and wastewater data — to help decide which food businesses get inspected first. The city’s own report on the proposal doesn’t describe a step where a person double-checks the computer’s call, or a way for a flagged business to appeal. Should a system like this need a documented human-review step and an appeal path before it goes into use — and if so, who is responsible for building that in?

12.7 [CITY] About 58,000 Toronto residents — roughly 2% of the city — still lack affordable home internet, even as more city services move onto apps and websites. What would you do, concretely, so that resident is never the one left out when a city service goes digital-first?

Universal set — for any candidate, any office

U.1 How will residents know if you succeeded? Name two or three public, checkable measures you are willing to be judged on at the end of the term — and where residents will find them.

U.2 What is one thing you would copy for Toronto from another city — anywhere in the world — and what makes you confident it would work here?

U.3 Name one thing the city currently does or funds that you would stop or shrink — and what you would say, face to face, to the residents who value it.

U.4 When the evidence in front of you and the residents you represent disagree, how do you decide? Give a real example of a position you changed, and why.

U.5 Name one problem residents raise constantly that the office you seek cannot fix directly. What do you tell them honestly — and what would you still do about it?

Sources & receipts

1. 2025 Toronto in-house AI policy; first municipal algorithmic impact assessment in Canada — City of Toronto AI governance materials (12.1).
2. Ontario Automated Vehicle Pilot Program (O. Reg. 306/15) extended to October 13, 2027; 2025 automated delivery-vehicle pilot approved provincially with no city regulatory authority — provincial and City of Toronto reporting (12.2).
3. Bill 97 MFIPPA amendments: privacy impact assessments required, phasing in from July 2026 — provincial legislation analysis (12.3).
4. Ontario’s 444 municipalities employ more than 235,000 people — Association of Municipalities of Ontario (12.5).
5. Toronto Public Health predictive-AI food-inspection pilot proposal: draws on 20+ years of DineSafe data, 311 metrics, and wastewater surveillance data; the report describes a pre-deployment bias review but no documented per-decision human-review or appeal mechanism — Toronto Public Health report, “Use of Artificial Intelligence by Toronto Public Health” (12.6).
6. Approximately 58,000 Toronto residents (about 2%) lack affordable in-home internet access — City of Toronto ConnectTO Program Update, July 2025 (12.7).

This page went through an independent verification pass before publication (fact-check against primary sources, claim by claim, including a live re-fetch of every figure against its original source). The internal verification record has been removed from this public page; the sourced facts and figures above are unchanged by that removal.