Failure Modes in Machine Learning Systems

by   Ram Shankar Siva Kumar, et al.

In the last two years, more than 200 papers have been written on how machine learning (ML) systems can fail because of adversarial attacks on the algorithms and data; this number balloons if we were to incorporate papers covering non-adversarial failure modes. The spate of papers has made it difficult for ML practitioners, let alone engineers, lawyers, and policymakers, to keep up with the attacks against and defenses of ML systems. However, as these systems become more pervasive, the need to understand how they fail, whether by the hand of an adversary or due to the inherent design of a system, will only become more pressing. In order to equip software developers, security incident responders, lawyers, and policy makers with a common vernacular to talk about this problem, we developed a framework to classify failures into "Intentional failures" where the failure is caused by an active adversary attempting to subvert the system to attain her goals; and "Unintentional failures" where the failure is because an ML system produces an inherently unsafe outcome. After developing the initial version of the taxonomy last year, we worked with security and ML teams across Microsoft, 23 external partners, standards organization, and governments to understand how stakeholders would use our framework. Throughout the paper, we attempt to highlight how machine learning failure modes are meaningfully different from traditional software failures from a technology and policy perspective.


page 1

page 2

page 3

page 4


Adversarial Machine Learning – Industry Perspectives

Based on interviews with 28 organizations, we found that industry practi...

MLOps with enhanced performance control and observability

The explosion of data and its ever increasing complexity in the last few...

On Combining Machine Learning with Decision Making

We present a new application and covering number bound for the framework...

Multiparty Dynamics and Failure Modes for Machine Learning and Artificial Intelligence

Overoptimization failures in machine learning and artificial intelligenc...

Quantifying dynamics of failure across science, startups, and security

Human achievements are often preceded by repeated attempts that initiall...

Modeling Hierarchical System with Operads

This paper applies operads and functorial semantics to address the probl...

SoK: A Study of the Security on Voice Processing Systems

As the use of Voice Processing Systems (VPS) continues to become more pr...

Please sign up or login with your details

Forgot password? Click here to reset