On 24 January six teams presented their ideas and answered the judges’ questions; the judges then selected four ideas for the shortlist.
The Data Challenge team will continue to work with the non-shortlisted teams, exploring ways to further develop their ideas and find delivery partners across the public service. Meanwhile, the four shortlisted teams are further developing their ideas and building the partnerships required for implementation. At the Final on 19 April, they’ll again pitch their ideas to the judging panel – this time including our program champions.
Create a needs analysis tool for remote communities
Crown-Indigenous Relations and Northern Affairs Canada (CIRNAC) offers a range of support programs for indigenous and isolated communities, seeking to allocate funds in ways that are transparent, impactful and equitable. These decisions could be improved by the development of a community needs index, presenting data on topics such as a community’s purchasing power, remoteness, social capital and vulnerability to climate change. And such an index would be valuable to actors across government, informing policymaking and service delivery in fields such as economic development, infrastructure provision and health care.
Develop a predictive tool to reduce fertiliser run-off
When heavy rain is forecast, farmers in sensitive areas are asked not to apply fertilizers – which, carried into streams and rivers, can reduce oxygen levels and choke up waterways. This guidance is important, but it is a blunt instrument: farmers are dependent on public forecasts, and the advice takes no account of local geography or farming practices. Bringing together records on precipitation and water pollution, we could analyse how past rainfall has impacted water quality across the country. Then, linking this information to new weather forecasts, we could generate far more granular, accurate predictions of the local pollution risk – averting both pollution, and the issuing of alerts when the risk is low.
Use AI to improve advice to agricultural sector
Through its AgPal website, Agriculture and Agri-Food Canada provides information on thousands of federal, provincial and territorial programmes and services for farmers and others involved in agriculture and food production. Currently, users must search by selecting categories from various lists; but such systems often struggle to meet people’s needs, omitting appropriate schemes while suggesting irrelevant ones. Today’s AI Natural Language Processing technologies could transform this search function, allowing users to ask questions and providing far more accurate and personalised responses. An AI-powered AgPal search function could thus help ensure that public funds reach their intended recipients, improving the targeting of services and the support offered to businesses.
Use satellite and sonar data to locate plastic waste at sea
Plastic waste and discarded ‘ghost’ fishing gear presents a serious threat to the health of our oceans – harming sea life, damaging delicate environments, and ultimately entering the human food chain. The Government of Canada is investing in clean-up operations, but these are extremely costly: an AI-assisted platform would help target this work, much improving its efficiency and effectiveness. Drawing on satellite imagery and sonar data, the tool would create heat maps of plastic and ‘ghost gear’ densities; it could also be further developed to track other pollution sources such as oil spills.