Human-in-the-loop Artificial Intelligence

TL;DR

This paper advocates for Human-in-the-loop AI (HIT-AI) as a fairer approach that compensates knowledge producers whose insights are used by AI systems, addressing ethical concerns of knowledge theft.

Contribution

It introduces HIT-AI as a novel paradigm that ensures fair compensation for knowledge contributors in AI decision-making processes.

Findings

Proposes a reward scheme for knowledge owners

Highlights ethical issues of knowledge extraction

Suggests a fairer AI development framework

Abstract

Little by little, newspapers are revealing the bright future that Artificial Intelligence (AI) is building. Intelligent machines will help everywhere. However, this bright future has a dark side: a dramatic job market contraction before its unpredictable transformation. Hence, in a near future, large numbers of job seekers will need financial support while catching up with these novel unpredictable jobs. This possible job market crisis has an antidote inside. In fact, the rise of AI is sustained by the biggest knowledge theft of the recent years. Learning AI machines are extracting knowledge from unaware skilled or unskilled workers by analyzing their interactions. By passionately doing their jobs, these workers are digging their own graves. In this paper, we propose Human-in-the-loop Artificial Intelligence (HIT-AI) as a fairer paradigm for Artificial Intelligence systems. HIT-AI will reward aware and unaware knowledge producers with a different scheme: decisions of AI systems generating revenues will repay the legitimate owners of the knowledge used for taking those decisions. As modern Robin Hoods, HIT-AI researchers should fight for a fairer Artificial Intelligence that gives back what it steals.