Human-in-the-loop Artificial Intelligence
This tackles the problem of unfair knowledge extraction and job displacement in AI for workers and society, but it is incremental as it builds on existing human-in-the-loop concepts with a focus on fairness.
The paper addresses the potential job market crisis caused by AI by proposing Human-in-the-loop Artificial Intelligence (HIT-AI) as a fairer paradigm that rewards knowledge producers, aiming to mitigate negative impacts without specifying concrete results or numbers.
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.