Algorithmic estimations of worker behavior are gaining popularity. Passive Sensing–enabled AI (PSAI) systems leverage behavioral traces from workers’ digital tools to infer their experience. Despite their conceptual promise, the practical designs of these systems elicit tensions that lead to workers resisting adoption. This paper teases apart the monolithic representation of PSAI by investigating system components that maximize value and mitigate concerns. We conducted an interactive online survey using the Experimental Vignette Method. Using Linear Mixed-effects Models we found that PSAI systems were more acceptable when sensing digital time use or physical activity, instead of visual modes. Inferences using language were only acceptable in work-restricted contexts. Compared to insights into performance, workers preferred insights into mental wellbeing. However, they resisted systems that automatically forwarded these insights to others. Our findings provide a template to reflect on existing systems and plan future implementations of PSAI to be more worker-centered.

Learning individual level treatment effects from observational data is a problem of growing interest. For instance, inferring the effect of delivery promises on purchase of products on an e-commerce site or selecting the most effective treatment for a specific patient. Although the scenarios where we want to estimate the treatment effects in presence of multiple treatments is quite common in real life, most existing works related to individual treatment effect (ITE) are focused primarily on binary treatments and do not have a natural extension to the multi-treatment scenarios. In this paper we present MEMENTO ? a methodology and a framework to estimate individual treatment effect for multi-treatment scenarios, where the treatments are discrete and finite. Our approach is based on obtaining matching representations of the confounders for the various treatment types. This is achieved through minimization of an upper bound on the sum of factual and counterfactual losses. Experiments on real and semi-synthetic datasets show that MEMENTO is able to outperform known techniques for multi-treatment scenarios by close to 10% in certain use-cases. The proposed framework has been deployed for the problem of identifying minimum order quantity of a product in Amazon in an emerging marketplace and has re- sulted in a 4.7% reduction in shipping costs as proved from an A/B experiment.

Speed of delivery is critical for the success of e-commerce platforms. Faster delivery promise to the customer results in increased conversion and revenue. There are typically two mechanisms to control the delivery speed - a) replication of products across warehouses, and b) air-shipping the product. In this paper, we present a machine learning based framework to recommend air-shipping eligibility for products. Specifically, we develop a causal inference framework (referred to as Air Shipping Recommendation or ASPIRE) that balances the trade-off between revenue or conversion and delivery cost to decide whether a product should be shipped via air. We propose a doubly-robust estimation technique followed by an optimization algorithm to determine air eligibility of products and calculate the uplift in revenue and shipping cost.