Abstract - With the increasing popularity of food delivery platforms, it has become pertinent to look into the working conditions of the 'gig' workers in these platforms, primarily providing them fair wages, reasonable working hours, and transparency on work availability. However, any solution to these problems must not degrade customer experience and be cost-effective to ensure that platforms are willing to adopt them. We propose WORK4FOOD, which provides income guarantees to delivery agents while minimizing platform costs and ensuring customer satisfaction. WORK4FOOD ensures that the income guarantees are met in such a way that it does not lead to increased working hours or degrade environmental impact. To incorporate these objectives, WORK4FOOD balances supply and demand by controlling the number of agents in the system and providing dynamic payment guarantees to agents based on factors such as agent location, ratings, etc. We evaluate WORK4FOOD on a real-world dataset from a leading food delivery platform and establish its advantages over the state-of-the-art in terms of the multi-dimensional objectives at hand.

Bio: Sayan Ranu holds the joint positions of Nick McKeown Chair Associate Professor at the Department of Computer Science and Eng. and the Yardi School of AI at IIT Delhi. His research interests span the broad area of network science with a particular focus on graph neural networks, social network analysis, querying and mining Spatio-temporal data, and bioinformatics. Prior to joining IIT Delhi, he spent close to three years as an Assistant Professor at IIT Madras and a year and a half in the role of a Research Scientist at IBM Research. He obtained his Ph.D. from the Department of Computer Science, University of California, Santa Barbara (UCSB) in March 2012. In 2020, Sayan was inducted as an Associate of the Indian Academy of Sciences (IASc). He regularly publishes in premier data-mining venues including NeurIPS, WWW, SIGMOD, VLDB, and KDD. He received the Best Paper Award at the International Conference on Web Information Systems Engineering (WISE) 2016, Best-of-IEEE-ICDM-2016 selection, Most Reproducible Paper Award at SIGMOD 2018, and Outstanding Reviewer Award at WSDM 2021. Sayan regularly serves on the program committees and review panels of prestigious conferences and journals including KDD, ICDE, ICDM, WWW, AAAI, TKDE, and VLDB Journal. Sayan has been granted 5 US patents. Sayan’s dissertation on graph-based techniques for querying and mining molecular databases served as the seed idea behind a life science-based R&D start-up focusing on advanced technology for drug discovery.