Responsible Artificial Intelligence & Asking Questions Early

To bring Responsible AI to the forefront, the Institute created the AI World Forum and AI Toronto. The Institute identified key topics of impact for AI development, then recruited global speakers best placed to discuss them. At a time when most AI conferences were celebrating automation and commercial growth, and technology builds.  ? events addressed law, ethics, inclusion, quantum AI, financial crime, autonomous systems, cybersecurity, and peace and security.

The Institute’s AI conferences sought to generate intelligent and practical conversations about AI while exploring different sector developments.

Among the Institute’s most important early positions was that the innovation of Ai would eventually play a leading role in future wars, peace and security, a warning the Institute brought to the United Nations and to other forums years before it entered mainstream policy debate. The Institute advocated for responsible AI to incorporate the rule of law and checks and balances. AI systems would move into the ordinary infrastructure society depends on: hospitals, food systems, financial networks, transportation, weapons, and government administration. Once embedded, those systems could be recoded, hijacked, or weaponized. A system designed to help a hospital could be altered to injure patients. A system connected to critical infrastructure could be used to destabilize governments or harm civilians.

It was a legal vacuum. Powerful AI systems operating in the real world, making material decisions without human intervention, affecting millions of people, with no governing law, no human recourse, and no international accountability framework, as well as no harmonized law across borders.

Beyond its events, the Institute advanced Responsible AI across multiple fora. At the United Nations, Institute leaders argued that AI was becoming a system capable of influencing the rule of law itself, because code was beginning to make decisions that affected rights, access, services, money, identity, safety, and recourse. The central question the Institute brought to those rooms was who governed the systems when code began governing people.

In its UN-facing work and public advocacy, the Institute described AI as a platform technology, closer to electricity or the microprocessor than to any single industry tool. It would cut across finance, healthcare, agriculture, infrastructure, law, public services, and security. Institute leaders argued that this scale was why governing AI mattered, and why governing it badly would produce consequences that compounded across every system it touched.

On human inaccessibility, the Institute warned publicly that automated platforms were already locking people out of essential accounts and services with no meaningful path to reach a human being. Institute leaders identified this as an access-to-justice problem years before platform accountability became a mainstream legal debate.

On bias, the Institute was precise where others were vague. In public sessions and policy submissions, it warned that machine learning systems could discriminate through proxies: a system told to ignore race or gender could learn to use postal codes, neighbourhoods, or other correlated data to reach the same discriminatory result. That concern now sits at the center of algorithmic accountability debates worldwide.

The conversations the Institute curated and hosted around law, ethics, inclusion, peace and security, and responsible AI governance are the conversations governments, courts, and international institutions are grappling with today.