Designing Recommender Systems with Reachability in Mind @ ICML2020-Participatory Approaches to ML

ICML 2020

Designing Recommender Systems with Reachability in Mind @ ICML2020-Participatory Approaches to ML

Oct 24, 2020
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Recommendations systems are a ubiquitous part of everyday life helping us navigate the immense amounts of information available online. But following recommendations can lead to unintended consequences such as polarization and radicalization. We propose a way of evaluating recommendations system by looking at the degree of agency they grant to individuals using the system. People have agency within a system if they can modify how the algorithm perceives their preferences. In our research we propose a way of quantifying the degree of agency in recommenders and argue that it is a promising new directions for evaluating algorithms that interact with humans. For more detail about this research check out our short paper: https://people.eecs.berkeley.edu/~sarahdean/stochastic_reachability.pdf To see more recent work in Participatory Approaches to Machine Learning check out the workshop at ICML 2020: https://participatoryml.github.io/ To see more of our other work see our research websites: Sarah Dean: https://people.eecs.berkeley.edu/~sarahdean/ Mihaela Curmei: https://mcurmei627.github.io/research/

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