Changes

Automated decision systems are computer systems that automate, or assist in automating, part or all of an administrative decision-making process. Currently, automated decision systems driven by Machine Learning (ML) algorithms are being used by the Government of Canada to improve service delivery. The use of such systems can have benefits and risks for federal institutions, including the Department of Fisheries and Oceans Canada (DFO). There is a need to ensure that automated decision systems are deployed in a manner that reduces risks to Canadians and federal institutions and leads to more efficient, accurate, consistent, and interpretable decisions.

Automated decision systems are computer systems that automate, or assist in automating, part or all of an administrative decision-making process. Currently, automated decision systems driven by Machine Learning (ML) algorithms are being used by the Government of Canada to improve service delivery. The use of such systems can have benefits and risks for federal institutions, including the Department of Fisheries and Oceans Canada (DFO). There is a need to ensure that automated decision systems are deployed in a manner that reduces risks to Canadians and federal institutions and leads to more efficient, accurate, consistent, and interpretable decisions.

For the Fiscal Year (2020 – 2021), the Office of the Chief Data Officer (OCDO), and Information Management and Technical Services (IMTS) co-sponsored an Artificial Intelligence (AI) pilot project to determine the extent to which AI, coupled with other data analytics techniques, can be used to gain insights, to automate tasks, and to optimize outcomes for different priority areas and business objectives at DFO. The project was funded through the 2020 – 2021 Results Fund and was developed in partnership with programs and business areas within DFO. The results included the development of three proof of concepts:

In order to support these goals, the Office of the Chief Data Steward (OCDS) is developing a data ethics framework which will provide guidance on the ethical handling of data and the responsible use of Artificial Intelligence (AI). The guidance material on the responsible use of AI addresses 6 major themes that have been identified as being pertinent to DFO projects using AI. These themes are Privacy and Security, Transparency, Accountability, Methodology and Data Quality, Fairness, and Explainability. While many of these themes have a strong overlap with the domain of data ethics, the theme of Fairness covers many ethical concerns unique to AI due to the nature of the impacts that bias can have on AI models.

Supported by the (2021 – 2022) Results Fund, the OCDS and IMTS are prototyping automated decision systems, based on the outcome of the AI pilot project. The effort includes defining an internal process to detect and mitigate bias, as a potential risk of ML-based automated decision systems. A case study is designed to apply this process to assess and mitigate bias in a predictive model for detecting vessels’ fishing behavior. The process defined from this work and the results of the field study will contribute towards the guidance material that will eventually form the responsible AI component of the data ethics framework.  

 

Supported by the (2021 – 2022) Results Fund, the OCDO and IMTS are prototyping automated decision systems, based on the outcome of the AI pilot project. The effort includes defining an internal process to detect and mitigate bias, as a potential risk of ML-based automated decision systems. A case study is designed to apply this process to assess and mitigate bias in the predictive model for detecting vessels’ fishing behavior.  

 

Eventually, in the long term, the goal will be to develop a comprehensive responsible AI framework to ensure responsible use of AI within DFO and to ensure compliance with the Treasury Board Directive on Automated Decision-Making.  

== Introduction ==

== Introduction ==

Unlike traditional automated decision systems, Machine Learning (ML)-based automated decision systems do not follow explicit rules authored by humans <ref>V. Fomins, "The Shift from Traditional Computing Systems to  Artificial Intelligence and the Implications for Bias," ''Smart  Technologies and Fundamental Rights,'' pp. 316-333, 2020.</ref>. ML models are not inherently objective. Data scientists train models by feeding them a data set of training examples, and the human involvement in the provision and curation of this data can make a model's predictions susceptible to bias. Due to this, applications of ML-based automated decision systems have far-reaching implications for society. These range from new questions about the legal responsibility for mistakes committed by these systems to retraining for workers displaced by these technologies. There is a need for a framework to ensure that accountable and transparent decisions are made, supporting ethical practices.

Unlike traditional automated decision systems, ML-based automated decision systems do not follow explicit rules authored by humans <ref>V. Fomins, "The Shift from Traditional Computing Systems to  Artificial Intelligence and the Implications for Bias," ''Smart  Technologies and Fundamental Rights,'' pp. 316-333, 2020.</ref>. ML models are not inherently objective. Data scientists train models by feeding them a data set of training examples, and the human involvement in the provision and curation of this data can make a model's predictions susceptible to bias. Due to this, applications of ML-based automated decision systems have far-reaching implications for society. These range from new questions about the legal responsibility for mistakes committed by these systems to retraining for workers displaced by these technologies. There is a need for a framework to ensure that accountable and transparent decisions are made, supporting ethical practices.

=== Responsible AI ===

=== Responsible AI ===