In January 2018, CIFAR and the Brookfield Institute for Innovation + Entrepreneurship (BII+E) formed a partnership to design and host five AI Futures Policy Labs aimed at generating greater awareness of the long-term implications of AI and exploring the future of AI policy in Canada. Between June and October 2018, CIFAR and BII+E held labs in Toronto, Edmonton, and Vancouver, with the participation of 91 emerging policy leaders.
On November 22, 2018, CIFAR and BII+E hosted the fourth AI Futures Policy Lab in Ottawa, Ontario. This event brought together 28 emerging policy leaders with the aim of:
- Building capacity of future public service leaders to understand the policy implications of AI and respond appropriately
- Providing policymakers with a direct line of sight into the AI sector: the myths and hype, the evolving state of technological advances, and potential applications
- Contributing to early government responses to emerging AI technologies
To achieve these aims, this lab was designed to raise awareness of the opportunities and challenges associated with current AI capabilities and applications, encourage critical thinking around potential future scenarios, and facilitate the development of policy recommendations. Feedback from the previous three AI Futures Policy Labs were used to re-design certain aspects of the lab’s agenda and content. Participants were presented with a case study featuring a current AI application associated with a specific policy domain (i.e. housing, justice, education, immigration, and hiring). Facilitators guided each respective group through their case study and accompanying prompts. During the final session of the day, groups presented policy recommendations related to the opportunities and challenges associated with their case study. The agenda developed for the day is provided in appendix A.
Case Study Policy Domains
Prior to the lab, six sets of case studies were developed. Each set was associated with a specific domain: housing, justice, education, health, immigration, and hiring. Participants were organized into groups of approximately five people, and assigned to a domain to discuss as a group.
AI is impacting the housing sector in multiple ways, from smart-home devices like Nest to intelligent tools that help to curb energy use, and services that even act as the middle-man between landlords and tenants. Advancements in this domain afford residents with potential benefits, but also create challenges regarding privacy and safety in a domestic environment. Within this domain, participants examined Naborly (appendix B), a tenant screening application that generates risk scores to help landlords make smarter letting decisions.
The legal sector is being impacted by recent developments in AI and machine learning capabilities that have enabled applications to automate legal research, due diligence processes, contract review and management practices, and help to predict legal outcomes. Participants within this domain were given the chance to explore the policy impacts of ROSS intelligence (appendix C), an artificially intelligent legal research tool that applies natural language processing to increase lawyer’s ability to identify relevant information.
There is vast potential for AI to transform education in ways that make learning more accessible, provide personalized curriculum, and support educators in delivering content. Participants in this group analyzed Nestor (appendix D), an artificially intelligent class assistant that uses machine learning algorithms and advanced facial recognition to analyze the attention levels of students listening to online lectures.
A number of large companies within Canada are integrating AI screening applications into their hiring processes. Participants within this group examined Ideal (appendix E), a talent intelligence application that centralizes data gathered from applicants resumes, chatbot conversations, and online assessments to screen and analyze candidates in real time. Ideal then identifies and provides the employer with a shortlist of strong candidates.
Participants within this group examined an AI app that is automating Canada’s immigration process by sorting applications into two streams: simple or complex (appendix F). This effort has been undertaken by Immigration, Refugees, and Citizenship Canada (IRCC) in an attempt to ease the backlog of immigration applications that immigration officers are faced with. Applications deemed as simple do not need to undergo review, and are processed at a faster rate than those that are identified as complex. Complex cases must be reviewed by a human, resulting in longer processing times.
A number of large companies within Canada are integrating AI screening apps into the hiring process. Participants within this group examined Ideal (appendix G), a talent intelligence application that centralizes data gathered from applicant resumes, chatbot conversations, and online assessments that screen and analyze candidates in real time. Ideal then identifies and provides a shortlist of strong candidates to employer.
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In partnership with the Brookfield Institute for Innovation + Entrepreneurship
(BII+E), this project has been designed to help emerging policy leaders across Canada understand and respond to the opportunities and challenges that accompany the rapid development of artificial intelligence.