Human-Machine Inference Networks For Smart Decision Making: Opportunities and Challenges

TL;DR

This paper reviews Human-Machine Inference Networks (HuMaINs), highlighting their architecture, inference algorithms, security challenges, and diverse applications, emphasizing their potential to outperform standalone human or machine inference.

Contribution

It provides a comprehensive overview of HuMaINs architecture, addressing design, inference algorithms, security/privacy issues, and practical use cases, outlining future research directions.

Findings

HuMaINs combine human and machine strengths for improved inference performance.

Novel signal processing and machine learning solutions are needed for HuMaINs.

Security and privacy are critical challenges in HuMaINs deployment.

Abstract

The emerging paradigm of Human-Machine Inference Networks (HuMaINs) combines complementary cognitive strengths of humans and machines in an intelligent manner to tackle various inference tasks and achieves higher performance than either humans or machines by themselves. While inference performance optimization techniques for human-only or sensor-only networks are quite mature, HuMaINs require novel signal processing and machine learning solutions. In this paper, we present an overview of the HuMaINs architecture with a focus on three main issues that include architecture design, inference algorithms including security/privacy challenges, and application areas/use cases.