Artificial fairness? Trust in algorithmic police decision-making.
Ontology highlight
ABSTRACT: Objectives
Test whether (1) people view a policing decision made by an algorithm as more or less trustworthy than when an officer makes the same decision; (2) people who are presented with a specific instance of algorithmic policing have greater or lesser support for the general use of algorithmic policing in general; and (3) people use trust as a heuristic through which to make sense of an unfamiliar technology like algorithmic policing.Methods
An online experiment tested whether different decision-making methods, outcomes and scenario types affect judgements about the appropriateness and fairness of decision-making and the general acceptability of police use of this particular technology.Results
People see a decision as less fair and less appropriate when an algorithm decides, compared to when an officer decides. Yet, perceptions of fairness and appropriateness were strong predictors of support for police use of algorithms, and being exposed to a successful use of an algorithm was linked, via trust in the decision made, to greater support for police use of algorithms.Conclusions
Making decisions solely based on algorithms might damage trust, and the more police rely solely on algorithmic decision-making, the less trusting people may be in decisions. However, mere exposure to the successful use of algorithms seems to enhance the general acceptability of this technology.Supplementary information
The online version contains supplementary material available at 10.1007/s11292-021-09484-9.
SUBMITTER: Hobson Z
PROVIDER: S-EPMC8435155 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:ImportanceSurgeons make complex, high-stakes decisions under time constraints and uncertainty, with significant effect on patient outcomes. This review describes the weaknesses of traditional clinical decision-support systems and proposes that artificial intelligence should be used to augment surgical decision-making.ObservationsSurgical decision-making is dominated by hypothetical-deductive reasoning, individual judgment, and heuristics. These factors can lead to bias, error, and preventable harm. Traditional predictive analytics and clinical decision-support systems are intended to augment surgical decision-making, but their clinical utility is compromised by time-consuming manual data management and suboptimal accuracy. These challenges can be overcome by automated artificial intelligence models fed by livestreaming electronic health record data with mobile device outputs. This approach would require data standardization, advances in model interpretability, careful implementation and monitoring, attention to ethical challenges involving algorithm bias and accountability for errors, and preservation of bedside assessment and human intuition in the decision-making process.Conclusions and relevanceIntegration of artificial intelligence with surgical decision-making has the potential to transform care by augmenting the decision to operate, informed consent process, identification and mitigation of modifiable risk factors, decisions regarding postoperative management, and shared decisions regarding resource use.
| S-EPMC7286802 | biostudies-literature
Project description:The United States has experienced prolonged severe shortages of vital medications over the past two decades. The causes underlying the severity and prolongation of these shortages are complex, in part due to the complexity of the underlying supply chain networks, which involve supplier-buyer interactions across multiple entities with competitive and cooperative goals. This leads to interesting challenges in maintaining consistent interactions and trust among the entities. Furthermore, disruptions in supply chains influence trust by inducing over-reactive behaviors across the network, thereby impacting the ability to consistently meet the resulting fluctuating demand. To explore these issues, we model a pharmaceutical supply chain with boundedly rational artificial decision makers capable of reasoning about the motivations and behaviors of others. We use multiagent simulations where each agent represents a key decision maker in a pharmaceutical supply chain. The agents possess a Theory-of-Mind capability to reason about the beliefs, and past and future behaviors of other agents, which allows them to assess other agents' trustworthiness. Further, each agent has beliefs about others' perceptions of its own trustworthiness that, in turn, impact its behavior. Our experiments reveal several counter-intuitive results showing how small, local disruptions can have cascading global consequences that persist over time. For example, a buyer, to protect itself from disruptions, may dynamically shift to ordering from suppliers with a higher perceived trustworthiness, while the supplier may prefer buyers with more stable ordering behavior. This asymmetry can put the trust-sensitive buyer at a disadvantage during shortages. Further, we demonstrate how the timing and scale of disruptions interact with a buyer's sensitivity to trustworthiness. This interaction can engender different behaviors and impact the overall supply chain performance, either prolonging and exacerbating even small local disruptions, or mitigating a disruption's effects. Additionally, we discuss the implications of these results for supply chain operations.
| S-EPMC7028279 | biostudies-literature
Project description:Punishing norm violations is considered an important motive during rejection of unfair offers in the ultimatum game (UG). The present study investigates the impact of the power to punish norm violations on people's responses to unfairness and associated neural correlates. In the UG condition participants had the power to punish norm violations, while an alternate condition, the impunity game (IG), was presented where participants had no power to punish norm violations since rejection only reduced the responder's income to zero. Results showed that unfair offers were rejected more often in UG compared to IG. At the neural level, anterior insula and dorsal anterior cingulate cortex were more active when participants received and rejected unfair offers in both UG and IG. Moreover, greater dorsolateral prefrontal cortex activity was observed when participants rejected than accepted unfair offers in UG but not in IG. Ventromedial prefrontal cortex activation was higher in UG than IG when unfair offers were accepted as well as when rejecting unfair offers in IG as opposed to UG. Taken together, our results demonstrate that the power to punish norm violations affects not only people's behavioral responses to unfairness but also the neural correlates of the fairness-related social decision-making process.
| S-EPMC4678221 | biostudies-literature
Project description:Building autonomous artificial cells capable of homeostasis requires regulatory networks to gather information and make decisions that take time and cost energy. Decisions based on few molecules may be inaccurate but are cheap and fast. Realizing decision-making with a few molecules in artificial cells has remained a challenge. Here, we show decision-making by a bistable gene network in artificial cells with constant protein turnover. Reducing the number of gene copies from 105 to about 10 per cell revealed a transition from deterministic and slow decision-making to a fuzzy and rapid regime dominated by small-number fluctuations. Gene regulation was observed at lower DNA and protein concentrations than necessary in equilibrium, suggesting rate enhancement by co-expressional localization. The high-copy regime was characterized by a sharp transition and hysteresis, whereas the low-copy limit showed strong fluctuations, state switching, and cellular individuality across the decision-making point. Our results demonstrate information processing with low-power consumption inside artificial cells.
| S-EPMC7648101 | biostudies-literature
Project description:Artificial intelligence (AI) based on convolutional neural networks (CNNs) has a great potential to enhance medical workflow and improve health care quality. Of particular interest is practical implementation of such AI-based software as a cloud-based tool aimed for telemedicine, the practice of providing medical care from a distance using electronic interfaces. Methods: In this study, we used a dataset of labeled 35,900 optical coherence tomography (OCT) images obtained from age-related macular degeneration (AMD) patients and used them to train three types of CNNs to perform AMD diagnosis. Results: Here, we present an AI- and cloud-based telemedicine interaction tool for diagnosis and proposed treatment of AMD. Through deep learning process based on the analysis of preprocessed optical coherence tomography (OCT) imaging data, our AI-based system achieved the same image discrimination rate as that of retinal specialists in our hospital. The AI platform's detection accuracy was generally higher than 90% and was significantly superior (p < 0.001) to that of medical students (69.4% and 68.9%) and equal (p = 0.99) to that of retinal specialists (92.73% and 91.90%). Furthermore, it provided appropriate treatment recommendations comparable to those of retinal specialists. Conclusions: We therefore developed a website for realistic cloud computing based on this AI platform, available at https://www.ym.edu.tw/~AI-OCT/. Patients can upload their OCT images to the website to verify whether they have AMD and require treatment. Using an AI-based cloud service represents a real solution for medical imaging diagnostics and telemedicine.
| S-EPMC6332801 | biostudies-other
Project description:Institution-centered accounts of generalized trust rely on the idea that law-breaking and state's unfairness lower individuals' propensity to trust fellow citizens because of a weaker confidence in the state. Despite the theoretical relevance attributed to this mediation mechanism, no empirical analysis in the literature has focused on examining its correlational validity. Using data from the European Social Survey (2010), the Quality of Government EU Regional data, and EUROSTAT, this paper assesses the intervening role of institutional trust on the relationship between crime rates, state's fairness, and generalized trust. Results from a Multilevel SEM (MSEM) mediation analysis indicate that trust in institutions strongly mediates the relationship between violent crimes (i.e. homicide) and generalized trust but not the one between property crimes (i.e. vehicle thefts and robberies) and generalized trust. On the other hand, indicators of fairness (i.e. impartiality and corruption) are all mediated by institutional trust, though impartiality maintains a significant direct effect. Overall, findings support the institutional approach, confirming that the negative relationship between ineffective and unfair institutions and generalized trust passes mostly through people's lost faith in the state.
| S-EPMC6685600 | biostudies-literature
Project description:Neonatal sepsis remains difficult to diagnose due to its non-specific signs and symptoms. Traditional scoring systems help to discriminate between septic or not patients, but they do not consider every single patient particularity. Thus, the purpose of this study was to develop an early- and late-onset neonatal sepsis diagnosis model, based on clinical maternal and neonatal data from electronic records, at the time of clinical suspicion. A predictive model was obtained by training and validating an artificial Neural Networks (ANN) algorithm with a balanced dataset consisting of preterm and term non-septic or septic neonates (early- and late-onset), with negative and positive culture results, respectively, using 25 maternal and neonatal features. The outcome of the model was sepsis or not. The performance measures of the model, evaluated with an independent dataset, outperformed physician's diagnosis using the same features based on traditional scoring systems, with a 93.3% sensitivity, an 80.0% specificity, a 94.4% AUROC, and a regression coefficient of 0.974 between actual and simulated results. The model also performed well-relative to the state-of-the-art methods using similar maternal/neonatal variables. The top 10 factors estimating sepsis were maternal age, cervicovaginitis and neonatal: fever, apneas, platelet counts, gender, bradypnea, band cells, catheter use, and birth weight.
| S-EPMC7518045 | biostudies-literature
Project description:INTRODUCTION:How shared decision making (SDM) works with indigenous patient values and preferences is not well understood. Colonization has affected indigenous peoples' levels of trust with institutions, and their world view tends to be distinct from that of nonindigenous people. Building on a programme theory for SDM, the present research aims to refine the original programme theory to understand how the mechanisms of trust and world view might work differently for indigenous patients. DESIGN:We used a six-step iterative process for realist synthesis: preliminary programme theory development, search strategy development, selection and appraisal of literature, data extraction, data analysis and synthesis, and formation of a revised programme theory. DATA SOURCES:Searches were through Medline, CINAHL, and the University of Saskatchewan iPortal for grey literature. Medline and CINAHL searches included the University of Alberta Canada-wide indigenous peoples search filters. DATA SYNTHESIS:Following screening 731 references, 90 documents were included for data extraction (53 peer reviewed and 37 grey literature). Documents from countries with similar colonization experiences were included. RESULTS:A total of 518 context-mechanism-outcome (CMO) configurations were identified and synthesized into 21 CMOs for a revised programme theory. Demographics, indigenous world view, system and institutional support, language barriers, and the macro-context of discrimination and historical abuse provided the main contexts for the programme theory. These inspired mechanisms of reciprocal respect, perception of world view acceptance, and culturally appropriate knowledge translation. In turn, these mechanisms influenced the level of trust and anxiety experienced by indigenous patients. Trust and anxiety were both mechanisms and intermediate outcomes and determined the level of engagement in SDM. CONCLUSION:This realist synthesis provides clinicians and policymakers a deeper understanding of the complex configurations that influence indigenous patient engagement in SDM and offers possible avenues for improvement.
| S-EPMC7154772 | biostudies-literature
Project description:IntroductionPatients vary in their preferences regarding involvement in medical decision-making. Current research does not provide complete explanation for this observed variation. Patient involvement in medical decision-making has been found to be influenced by various mechanisms, one of which could be patients' trust in physicians. The aim of this study was to examine whether trust in physicians fosters or impairs patient involvement in medical decision-making. This study also aimed to determine to what extent the relationship between trust and preferences regarding decision-making roles was influenced by the sociodemographic characteristics of the patients. We hypothesised that trust can both foster and impair patient involvement in medical decision-making.Materials and methodsA survey was sent out to members of the Nivel Dutch Health Care Consumer Panel in February 2016 (response rate = 47%, N = 703). The Wake Forest Physician Trust Scale was used to measure trust. Patient involvement was measured using two items based on the study published by Flynn and colleagues in 2006. Multiple regression analysis was used to analyse the relationship between trust and patient involvement.ResultsWe found a negative relationship between trust and patient involvement in medical decision-making in men. Women with high trust reported to be more involved in medical decision-making compared to men with high trust.ConclusionThe results suggest that trust impairs involvement in medical decision-making for men but not for women. Further research could provide a more comprehensive explanation of the variation in patient preferences regarding involvement in medical decision-making to further elucidate which underlying mechanisms could enhance patient participation.
| S-EPMC8389380 | biostudies-literature
Project description:PurposePedicled flaps are still the workhorse flaps for reconstruction of upper limb soft tissue defects in many centers across the world. They are lifeboat options for coverage in vessel deplete wounds. In spite of their popularity existing algorithms are limited to a particular region of upper limb; a general algorithm involving entire upper limb which helps in clinical decision making is lacking. We attempt to propose one for the day to day clinical practice.MethodsA retrospective analysis of patients who underwent pedicled flaps for coverage of post-traumatic upper extremity (arm, elbow, forearm, wrist & hand) soft tissue defects within the period of January 2016 to October 2017 was performed. Patients were divided into groups according to the anatomical location of the defects. The flaps performed for different anatomical regions were enlisted. Demographic data and complications were recorded. An algorithm was proposed based on our experience, with a particular emphasis made to approach to clinical decision making.ResultsTwo hundred and twelve patients were included in the study. Mean age was 27.3 years (range: 1-80 years), 180 were male, and 32 were female. Overall flap success rate was 98%, the following complications were noted marginal flap necrosis requiring no additional procedure other than local wound care in 32 patients (15%), partial flap necrosis requiring flap advancement or extra flap in 15 patients (7%), surgical site infection in 11 patients (5%), flap dehiscence requiring re-suturing in 5 patients (2.4%), total flap necrosis 4 patients (2%).ConclusionThe proposed algorithm allows a reliable and consistent method for addressing diverse soft tissue defects in the upper limb with high success rate.
| S-EPMC6354178 | biostudies-literature