Responsible Design Patterns for Machine Learning Pipelines
- URL: http://arxiv.org/abs/2306.01788v3
- Date: Wed, 7 Jun 2023 21:55:28 GMT
- Title: Responsible Design Patterns for Machine Learning Pipelines
- Authors: Saud Hakem Al Harbi, Lionel Nganyewou Tidjon and Foutse Khomh
- Abstract summary: AI ethics involves applying ethical principles to the entire life cycle of AI systems.
This is essential to mitigate potential risks and harms associated with AI, such as biases.
To achieve this goal, responsible design patterns (RDPs) are critical for Machine Learning (ML) pipelines.
- Score: 10.184056098238765
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Integrating ethical practices into the AI development process for artificial
intelligence (AI) is essential to ensure safe, fair, and responsible operation.
AI ethics involves applying ethical principles to the entire life cycle of AI
systems. This is essential to mitigate potential risks and harms associated
with AI, such as algorithm biases. To achieve this goal, responsible design
patterns (RDPs) are critical for Machine Learning (ML) pipelines to guarantee
ethical and fair outcomes. In this paper, we propose a comprehensive framework
incorporating RDPs into ML pipelines to mitigate risks and ensure the ethical
development of AI systems. Our framework comprises new responsible AI design
patterns for ML pipelines identified through a survey of AI ethics and data
management experts and validated through real-world scenarios with expert
feedback. The framework guides AI developers, data scientists, and
policy-makers to implement ethical practices in AI development and deploy
responsible AI systems in production.
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