The Design of Reciprocal Learning between Human and Artificial Intelligence

Alexey Zagalsky, Dov Te'Eni, Inbal Yahav, David G. Schwartz, Gahl Silverman, Daniel Cohen, Yossi Mann, Dafna Lewinsky

Research output: Contribution to journalArticlepeer-review

13 Scopus citations


The need for advanced automation and artificial intelligence (AI) in various fields, including text classification, has dramatically increased in the last decade, leaving us critically dependent on their performance and reliability. Yet, as we increasingly rely more on AI applications, their algorithms are becoming more nuanced, more complex, and less understandable precisely at a time we need to understand them better and trust them to perform as expected. Text classification in the medical and cybersecurity domains is a good example of a task where we may wish to keep the human in the loop. Human experts lack the capacity to deal with the high volume and velocity of data that needs to be classified, and ML techniques are often unexplainable and lack the ability to capture the required context needed to make the right decision and take action. We propose a new abstract configuration of Human-Machine Learning (HML) that focuses on reciprocal learning, where the human and the AI are collaborating partners. We employ design-science research (DSR) to learn and design an application of the HML configuration, which incorporates software to support combining human and artificial intelligences. We define the HML configuration by its conceptual components and their function. We then describe the development of a system called Fusion that supports human-machine reciprocal learning. Using two case studies of text classification from the cyber domain, we evaluate Fusion and the proposed HML approach, demonstrating benefits and challenges. Our results show a clear ability of domain experts to improve the ML classification performance over time, while both human and machine, collaboratively, develop their conceptualization, i.e., their knowledge of classification. We generalize our insights from the DSR process as actionable principles for researchers and designers of 'human in the learning loop' systems. We conclude the paper by discussing HML configurations and the challenge of capturing and representing knowledge gained jointly by human and machine, an area we feel has great potential.

Original languageEnglish
Article number443
JournalProceedings of the ACM on Human-Computer Interaction
Issue numberCSCW2
StatePublished - 18 Oct 2021


  • AI
  • accuracy
  • context
  • cyber-security
  • explainabilitiy
  • feedback
  • human intelligence
  • text classification


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