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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. 

The objective of this paper is to establish the fundamental public value principles that should govern safe and trusted artificial intelligence (AI). Public value is a dynamic concept that encompasses several dimensions. AI itself has evolved quite rapidly in the last few years, especially with the swift escalation of Generative AI. Governments around the world are grappling with how to govern AI, just as technologists ring alarm bells about the future consequences of AI. Our paper extends the debate on AI governance that is focused on ethical values of beneficence to that of economic values of public good. Viewed as a public good, AI use is beyond the control of the creators. Towards this end, the paper examined AI policies in the United States and Europe. We postulate three principles from a public values perspective: (i) ensuring security and privacy of each individual (or entity); (ii) ensuring trust in AI systems is verifiable; and (iii) ensuring fair and balanced AI protocols, wherein the underlying components of data and algorithms are contestable and open to public debate. 

This article is an exploratory analysis of the impact of the California Consumer Privacy Act (CCPA) on data breaches that result in exposing sensitive private data of consumers. The CCPA applies to large for-profit businesses that collect and disseminate personal information of Californian consumers. It provides for consumer rights and imposes notification and security requirements on businesses that collect private information. We analyzed how CCPA affects data breach notifications that are required by the state's Office of Auditor General, for the period 2012 to 2023. The analysis provides interesting insights into the impact of CCPA on the pattern of data breaches. Our principal finding is that privacy breaches reduced to some extent after CCPA. Importantly, CCPA has helped in the overall improvement in reporting privacy breaches. We surmise that the CCPA brought more data breaches into light. 

Blockchain technology that came with the introduction of Bitcoin offers many powerful use-cases while promising the establishment of distributed autonomous organizations (DAOs) that may transform our current understanding of client-server interactions on the cyberspace. They employ distributed consensus mechanisms that were subject to a lot of research in recent years. While most of such research focused on security and performance of consensus protocols, less attention was given to their incentive mechanisms which relate to a critical feature of blockchains. Unfortunately, while blockchains are advocating decentralized operations, they are not egalitarian due to existing incentive mechanisms. Many current consensus protocols inadvertently incentivize centralization of mining power and inequitable participation. This paper explores and evaluates alternative incentive mechanisms for a more decentralized and equitable participation. We first evaluate inequality in existing Proof of Stake (PoS) based incentive mechanisms, then we examine three alternatives in which rewards scheme is more partial to low-stakeholders. Through simulation, we show that two of our alternative mechanisms can reduce inequality and offer an attractive solution for sustainability of blockchain-based applications and DAOs. 

This paper illustrates the application of machine learning algorithms in predictive analytics for local governments using administrative data. The developed and tested machine learning predictive algorithm overcomes known limitations of the conventional ordinary least squares method. Such limitations include but not limited to imposed linearity, presumed causality with independent variables as presumed causes and dependent variables as presume result, likely high multicollinearity among features, and spatial autocorrelation. The study applies the algorithms to 311 non-emergency service requests in the context of Miami-Dade County. The algorithms are applied to predict the volume of 311 service requests and the community characteristics affecting the volume across Census tract neighborhoods. Four common families of algorithms and an ensemble of them are applied. They are random forest, support vector machines, lasso and elastic-net regularized generalized linear models, and extreme gradient boosting. Two feature selection methods, namely Boruta and fscaret, are applied to identify the significant community characteristics. The results show that the machine learning algorithms capture spatial autocorrelation and clustering. The features generated by fscaret algorithms are parsimonious in predicting the 311 service request volume. 

The main purpose of this paper is to illustrate the application of causal inference method to administrative data and the challenges of such application. We illustrate by applying Bayesian networks method to 311 data from Miami-Dade County, Florida (USA). The 311 centers provide non-emergency services to residents. The 311 data are large and granular. We aim to explore the equity issues and biases that might exist in this particular type of service requests. As a case study, the relationship between population characteristics (independent variables) and request volume and completion time (dependent variables) is examined to identify the disparities, if any, from the observational data. The empirical analysis shows that there are no biases in services provided to any specific demographic, socioeconomic, or geographical groups. However, the administrative data do have various challenges for inferring causality due to missing or impure data, inadequacy, and latent confounders. The precautions of applying causal techniques to analyzing administrative data like 311 are discussed.