Duration: 57:06

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Machine learning (ML) and automatic algorithmic decision making have started to play central and crucial roles in our daily lives. At the same time, more and more data used to train ML algorithms are now collected through crowdsourcing or other forms of participatory computation involving people. With people being both the source and the ultimate target of these algorithms, which are increasingly being used to assist in making important and sometimes life-changing decisions, new and interesting challenges arise.

I will present our studies that address the challenges of incentivizing societal contributions for building better and more robust ML algorithms. In the first part of the talk, I will demonstrate how ML techniques can be leveraged to quantify the value of human reported information when there is no ground-truth verification. I show how these results help design better incentive mechanisms to encourage user input and help make high-quality data collection more efficiently, compared to existing, non-ML based methods. In the second part, I will show how the Multi-Armed Bandit type of techniques can help resolve above data collection problem in a sequential setting. I will conclude my talk with future works.

See more at https://www.microsoft.com/en-us/research/video/incentivizing-societal-contributions-via-machine-learning/