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1. Second Opinion: Supporting Last-Mile Person Identification with Crowdsourcing and Face Recognition

   Mohanty, Vikram; Abdol-Hamid, Kareem; Ebersohl, Courtney; Luther, Kurt (January 2019, Proceedings of the Seventh AAAI Conference on Human Computation and Crowdsourcing)

   As AI-based face recognition technologies are increasingly adopted for high-stakes applications like locating suspected criminals, public concerns about the accuracy of these technologies have grown as well. These technologies often present a human expert with a shortlist of high-confidence candidate faces from which the expert must select correct match(es) while avoiding false positives, which we term the “last-mile problem.” We propose Second Opinion, a web-based software tool that employs a novel crowdsourcing workflow inspired by cognitive psychology, seed-gather-analyze, to assist experts in solving the last-mile problem. We evaluated Second Opinion with a mixed-methods lab study involving 10 experts and 300 crowd workers who collaborate to identify people in historical photos. We found that crowds can eliminate 75% of false positives from the highest-confidence candidates suggested by face recognition, and that experts were enthusiastic about using Second Opinion in their work. We also discuss broader implications for crowd–AI interaction and crowdsourced person identification. 

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2. Photo Sleuth: Combining Collective Intelligence and Computer Vision to Identify Historical Portraits

   Mohanty, Vikram; Thames, David; Luther, Kurt (January 2018, ACM Conference on Collective Intelligence (CI 2018))

   Identifying people in photographs is a critical task in a wide variety of domains, from national security [7] to journalism [14] to human rights investigations [1]. The task is also fundamentally complex and challenging. With the world population at 7.6 billion and growing, the candidate pool is large. Studies of human face recognition ability show that the average person incorrectly identifies two people as similar 20–30% of the time, and trained police detectives do not perform significantly better [11]. Computer vision-based face recognition tools have gained considerable ground and are now widely available commercially, but comparisons to human performance show mixed results at best [2,10,16]. Automated face recognition techniques, while powerful, also have constraints that may be impractical for many real-world contexts. For example, face recognition systems tend to suffer when the target image or reference images have poor quality or resolution, as blemishes or discolorations may be incorrectly recognized as false positives for facial landmarks. Additionally, most face recognition systems ignore some salient facial features, like scars or other skin characteristics, as well as distinctive non-facial features, like ear shape or hair or facial hair styles. This project investigates how we can overcome these limitations to support person identification tasks. By adjusting confidence thresholds, users of face recognition can generally expect high recall (few false negatives) at the cost of low precision (many false positives). Therefore, we focus our work on the “last mile” of person identification, i.e., helping a user find the correct match among a large set of similarlooking candidates suggested by face recognition. Our approach leverages the powerful capabilities of the human vision system and collaborative sensemaking via crowdsourcing to augment the complementary strengths of automatic face recognition. The result is a novel technology pipeline combining collective intelligence and computer vision. We scope this project to focus on identifying soldiers in photos from the American Civil War era (1861– 1865). An estimated 4,000,000 soldiers fought in the war, and most were photographed at least once, due to decreasing costs, the increasing robustness of the format, and the critical events separating friends and family [17]. Over 150 years later, the identities of most of these portraits have been lost, but as museums and archives increasingly digitize and publish their collections online, the pool of reference photos and information has never been more accessible. Historians, genealogists, and collectors work tirelessly to connect names with faces, using largely manual identification methods [3,9]. Identifying people in historical photos is important for preserving material culture [9], correcting the historical record [13], and recognizing contributions of marginalized groups [4], among other reasons. 

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3. Measuring Transit Equity of an On-Demand Multimodal Transit System

   https://doi.org/10.1080/01944363.2024.2323470  

   Hwang, Uijeong; Lieu, Seung Jae; Guan, Hongzhao; Dalmeijer, Kevin; van_Hentenryck, Pascal; Guhathakurta, Subhrajit (April 2024, Journal of the American Planning Association)

   On-demand transit is attracting the attention of transportation researchers and transit agencies for its potential to solve the first-mile/last-mile problem. Although on-demand transit has been proved to increase transit accessibility significantly, its impact on transit equity and equality has not been addressed. In this study we examined the potential impact of the On-Demand Multimodal Transit System (ODMTS) in Atlanta (GA), on both transit equity and equality compared with the existing transit system. The results showed that ODMTS could have a positive impact on transit equality by reducing the disparity in transit service between neighborhoods close to and far from the existing transit network; however, it may not improve transit equity. 

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4. A-VRPD: Automating Drone-Based Last-Mile Delivery Using Self-Driving Cars

   https://doi.org/10.1109/TITS.2023.3266460  

   Imran, Navid Mohammad; Mishra, Sabyasachee; Won, Myounggyu (September 2023, IEEE Transactions on Intelligent Transportation Systems)

   Drone-based last-mile delivery is an emerging technology that uses drones loaded onto a truck to deliver parcels to customers. In this paper, we introduce a fully automated system for drone-based last-mile delivery through incorporation of autonomous vehicles (AVs). A novel problem called the autonomous vehicle routing problem with drones (A-VRPD) is defined. A-VRPD is to select AVs from a pool of available AVs based on crowd sourcing, assign selected AVs to customer groups, and schedule routes for selected AVs to optimize the total operational cost. We formulate A-VRPD as a Mixed Integer Linear Program (MILP) and propose an optimization framework to solve the problem. A greedy algorithm is also developed to significantly improve the running time for large-scale delivery scenarios. Extensive simulations were conducted taking into account real-world operational costs for different types of AVs, traveled distances calculated considering the real-time traffic conditions using Google Map API, and varying load capacities of AVs. We evaluated the performance in comparison with two different state-of-the-art solutions: an algorithm designed to address the traditional vehicle routing problem with drones (VRP-D), which involves human-operated trucks working in tandem with drones to deliver parcels, and an algorithm for the two echelon vehicle routing problem (2E-VRP), wherein parcels are first transported to satellite locations and subsequently delivered from those satellites to the customers. The results indicate a substantial increase in profits for both the delivery company and vehicle owners compared with the state-of-the-art algorithms. 

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5. Can Cash Incentives Reduce Syringe Litter? Evidence From Boston's 311 Service Requests

   https://doi.org/10.1111/psj.70063  

   Le, Angie N; Zhao, Boyuan; Cheng, Shaoming; Ganapati, Sukumar (July 2025, Policy Studies Journal)

   ABSTRACT Syringe littering in public places is a public health problem in many big cities nationwide amid the ongoing opioid crisis. Besides needle exchanges and/or safe disposal efforts, cash incentives have come into play as a policy tool to address the issue. In December 2020, the City of Boston launched the Community Syringe Redemption Program (CSRP), which offers a nominal cash “buy back” incentive for used syringes at designated centers. This study examines the impact of Boston's CSRP on reducing syringe litter. It employs a distance band‐based, near‐far identification strategy and uses the difference‐in‐differences (DID) to analyze Boston's 311 service requests related to needle pickup and the number of discarded needles discovered in response to the pickup requests before and after CSRP's implementation. Results show that both 311 requests and publicly discarded syringes have reduced significantly in neighborhoods located within a 0.5‐mile radius of the CSRP redemption center. There are no statistically significant reductions observed in 0.5–1‐mile or 1–1.5‐mile donut bands around the center. In effect, although CSRP could be effective in reducing syringe littering, its impact is geographically bounded and sensitive to the redemption centers' locations. Results from alternative models, robustness tests, and placebo tests are consistent with main findings. 

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