Human-in-the-loop Artificial Intelligence

By Fabio Massimo Zanzotto. arXiv.org, 2017

336 citations recorded by the source corpus.

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.

Subfields: Information Systems, Management Science and Operations Research, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/b2277775e67ad13a5ab22d86a055e1f49a4cc8f5

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Information Systems

1. Yuxin Peng
2. Serge J. Belongie
3. Jinhui Tang

In Management Science and Operations Research

1. Yuxin Peng
2. Yi-Zhe Song
3. Oisin Mac Aodha
4. Serge J. Belongie
5. Jian Yang
6. Jinhui Tang

In Sociology and Political Science

1. Wei Xu
2. Marvin J. Dainoff
3. Oisin Mac Aodha
4. Zaifeng Gao
5. Serge J. Belongie
6. Jian Yang
7. Jinhui Tang
8. Liezhong Ge

Citations in the corpus

• Transitioning to Human Interaction with AI Systems: New Challenges and Opportunities for HCI Professionals to Enable Human-Centered AI (2021, background): “For instance, when the confidence of the system output is low, humans can intervene by adjusting input, creating a feedback loop to improve the system’s performance (Zheng et al., 2017; Zanzotto, 2019).”
• Fine-Grained Image Analysis With Deep Learning: A Survey (2021, background): “Human-in-the-loop methods [165] combine the complementary strengths of human knowledge with computer vision algorithms.”