tinyMl Summit 2023: Responsible Design of Edge AI: A Pattern Approach for Detecting and…

Responsible Design of Edge AI: A Pattern Approach for Detecting and Mitigating Bias
Wiebke HUTIRI, PhD Candidate, Delft University of Technology

n the past years, there have been many incidents of biased AI systems that have systematically discriminated against certain groups of people. There is thus growing societal and governmental pressure for fair and non-discriminatory AI systems, and a pressing need to detect and mitigate bias in machine learning (ML) workflows.
With ML systems being an integral component of Edge AI, the need for detecting and mitigating bias to design trustworthy Edge AI is evident. However, while bias has been widely studied in several domains that deploy AI, very few studies examine bias in the Edge AI setting. Despite Edge AI being prolific (for example, it is estimated that in 2024 there will be 8.4 billion voice assistants deploying voice-based Edge AI, a number roughly equal to the human population), research on bias and fairness in Edge AI remains scarce. Best-practice approaches exist for detecting and mitigating bias in ML systems, but these approaches are not common in Edge AI workflows. Progress in developing trustworthy Edge AI systems thus remains slow, increasing the potential for harm resulting from biased systems, and its legal repercussions.

This presentation introduces patterns for detecting and mitigating bias in Edge AI. Patterns capture proven design experience in generalizable templates so that they can be reused in future design projects. This makes them effective at capturing and communicating design knowledge, and a popular tool in object-oriented programming, software engineering, and other engineering design disciplines. The pattern catalog that I present has been developed from best-practice knowledge in ML fairness and has been adapted to account for new design knowledge specific to detecting and mitigating bias in Edge AI systems. The presentation will demonstrate how the patterns can be used to detect and mitigate bias in voice activation systems comprising keyword spotting and speaker verification components.
Bias in Edge AI is a new area of study. So is the use of patterns to communicate transferable knowledge from ML fairness to Edge AI applications. By learning how to detect and mitigate bias from best practices in ML fairness, and by making this knowledge transferable between
Edge AI domains, responsible design of Edge AI can be significantly facilitated.

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