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== Executive Summary ==

== [[Executive Summary]] ==

Automated Decision Systems (ADS) 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 (ADS) 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.

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 Treasury’s Board directive on automated decision making.

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 Treasury’s Board directive on automated decision making.

== Introduction ==

== [[Introduction]] ==

Unlike traditional Automated Decision Systems (ADS), Machine Learning (ML)-based ADS do not follow explicit rules authored by humans. 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 ADS 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 (ADS), Machine Learning (ML)-based ADS do not follow explicit rules authored by humans. 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 ADS 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.