Responsible Design Patterns for Machine Learning Pipelines

TL;DR

This paper introduces a comprehensive framework of responsible design patterns for machine learning pipelines, aiming to embed ethical principles and mitigate risks like bias throughout AI development.

Contribution

It proposes new responsible AI design patterns for ML pipelines, developed through expert surveys and validated with real-world scenarios, to promote ethical AI practices.

Findings

Framework effectively guides ethical AI development

Design patterns mitigate risks like bias and unfairness

Validated through expert feedback and real-world scenarios

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.