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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.

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 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 in 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.   

== Introduction ==

== Introduction ==

== The Path Forward ==

== The Path Forward ==

Responsible AI is the only way to mitigate AI risks, and bias risks are considered a subset of such risks. As DFO moves towards adopting AI to support decision-making and improve service delivery, there is a need to ensure that these decisions are not only bias-aware, but also accurate, human-centric, explainable, and privacy-aware.

The development of a process for bias identification and mitigation is a step towards a framework that supports responsible use of AI. In order to fully develop this framework, guidance for additional processes is required. In particular, the theme of Explainability is another topic with requirements that are unique to the use of AI. Next steps in this area will require the identification of tools and the development of guidance to support the use of interpretable models and explainability algorithms for black-box models. Further to this, a more general process is required to enable project teams to assess their compliance across all themes of responsible AI. The OCDS is currently undertaking the development of these resources.

 

DFO is in the process of defining guiding principles to guide the development of AI applications and solutions. Once defined, various tools will be considered and/or developed to operationalize such principles.

== Bibliography ==

== Bibliography ==

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