Human-machine Collaboration in Real-World Machine-Learning Applications

Abstract

Automation tools like machine learning are a necessity in our big data world. Thanks to the Internet and advancements in all facets of computer and storage technology, almost everyone has a voice in the Internet connected world. However, there are still very real physical limits in our physical world. This dichotomy---the seemingly limitless nature of technology enabled data colliding with the physical limits of the real world---has made automation tools a necessity, and predictive models powered by machine learning algorithms are one such tool. The promise of machine learning to accurately predict future human behavior and human preferences has lead practitioners and researchers alike to apply machine learning automation tools to tasks such as product recommendations and speculatory activities such as long term job applicant success. However, due to the mercurial nature of humans, developing mathematical intermediaries to attempt to model and predict human behavior is challenging and not a straight-forward task. One way of harnessing the power of machine-learning backed automation to help reduce the scale of many real-world applications in more challenging domain settings is by having humans and machines collaborating in non-trivial ways. In this dissertation, we delineate the various ways in which humans and machines collaborate in challenging real-world applications. Moreover, we highlight three specific ways in which we can use human-machine collaboration to keep or increase utility and reduce real-world harm when using these systems in the wild: 1) humans enabling computers with domain specific knowledge, 2) computers providing humans with algorithmic explanations, 3) humans and computers working together in decision making.