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Towards a Responsible AI Framework for the Design of Automated Decision Systems in DFO: a Case Study of Bias Detection and Mitigation (view source)
Revision as of 15:28, 3 December 2021
, 15:28, 3 December 2021no edit summary
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 ==
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.
=== Responsible AI ===
Responsible AI is a governance framework that documents how a specific organization is addressing the challenges around artificial intelligence (AI) from both an ethical and legal point of view.
[[File:AiGuidingPrinciples.png|thumb|367x367px|The research “ The global landscape of AI ethics guidelines “ , source: <nowiki>https://www.nature.com/articles/s42256-019-0088-2</nowiki>]]
In an attempt to ensure Responsible AI practices, organizations have identified guiding principles to guide the development of AI applications and solutions. According to the research “The global landscape of AI ethics guidelines” [1], some principles are mentioned more often than others. However, Gartner has concluded that there is a global convergence emerging around five ethical principles:
· Human centric and socially beneficial
· Fair
· Explainable and transparent
· Secure and safe
· Accountable
The definition of the various guiding principles is included in [1].
