Confirmation Bias in Simulated Child Sexual Abuse (CSA) Interviews: Abuse Assumption Influences Interviewing and Decision-making Processes

PurposeResearch has shown that confirmation bias plays a role in legal and forensic decision-making processes. However, to our knowledge, no study has examined how it manifests itself when interviewing an allegedly abused child. MethodIn the present study, we used data from a series of experiments in which participants interviewed child avatars to examine how an assumption of abuse based on preliminary information influenced decision-making and interviewing style. Interview training data from eight studies with students, psychologists and police officers were included in the analyses.ResultsWe found that interviewers’ preliminary assumption of sexual abuse having taken place predicted 1) a conclusion of abuse by the interviewers after the interview; 2) higher confidence in their judgment; 3) more frequent use of not recommended question types and 4) a decreased likelihood of reaching a correct conclusion given the same number of available relevant details. ConclusionThe importance of considering how preliminary assumptions of abuse affect interview behaviour and outcomes and the implications for the training of investigative interviewers were discussed.

Uncomfortable Decisions

Some decision-making processes are uncomfortable. Many of us do not like to make significant decisions, such as whether to have a child, solely based on social science research. We do not like to choose randomly, even in cases where flipping a coin is plainly the wisest choice. We are often reluctant to defer to another person, even if we believe that the other person is wiser, and have similar reservations about appealing to powerful algorithms. And, while we are comfortable with considering and weighing different options, there is something strange about deciding solely on a purely algorithmic process, even one that takes place in our own heads.What is the source of our discomfort? We do not present a decisive theory here—and, indeed, the authors have clashing views over some of these issues—but we lay out the arguments for two (consistent) explanations. The first is that such impersonal decision-making processes are felt to be a threat to our autonomy. In all of the examples above, it is not you who is making the decision, it is someone or something else. This is to be contrasted with personal decision-making, where, to put it colloquially, you “own” your decision, though of course you may be informed by social science data, recommendations of others, and so on. A second possibility is that such impersonal decision-making processes are not seen as authentic, where authentic decision making is one in which you intentionally and knowledgably choose an option in a way that is “true to yourself.” Such decision making can be particularly important in contexts where one is making a life-changing decision of great import, such as the choice to emigrate, start a family, or embark on a major career change.

Auditing Risk Prediction of Long-Term Unemployment

As more and more governments adopt algorithms to support bureaucratic decision-making processes, it becomes urgent to address issues of responsible use and accountability. We examine a contested public service algorithm used in Danish job placement for assessing an individual's risk of long-term unemployment. The study takes inspiration from cooperative audits and was carried out in dialogue with the Danish unemployment services agency. Our audit investigated the practical implementation of algorithms. We find (1) a divergence between the formal documentation and the model tuning code, (2) that the algorithmic model relies on subjectivity, namely the variable which focus on the individual's self-assessment of how long it will take before they get a job, (3) that the algorithm uses the variable "origin" to determine its predictions, and (4) that the documentation neglects to consider the implications of using variables indicating personal characteristics when predicting employment outcomes. We discuss the benefits and limitations of cooperative audits in a public sector context. We specifically focus on the importance of collaboration across different public actors when investigating the use of algorithms in the algorithmic society.

Evaluation of Ecosystem Services in Mining Basins: An Application in the Piedmont Region (Italy)

Mining activities impact on the territorial system in various ways, affecting its environmental and socio-economic components. Specific evaluation tools can support decision-making processes in the context of the sustainable planning and management of mining activities. Within the evaluation procedures of mining activities, a growing interest in the analysis of Ecosystem Services (ES) is emerging. ES refer to the benefits that the natural system delivers to society, linking the health of ecosystems and human well-being. Starting from a real-world case related to the adoption of the Regional Plan of Mining Activities (PRAE) of the Piedmont region (Northern Italy), the paper aims to explore the ES valuation by considering three different mining quarries. The state of the art of the basins is compared with alternative planning scenarios from the point of view of the ES produced. The valuation is developed through GIS and the Simulsoil software, detecting the biophysical benefits produced and estimating their economic performance. The simulation results can be used to support the formulation of planning strategies, estimating the trade-offs in terms of competitive land-use values. The study also demonstrates that the integration of ES into Strategic Environmental Assessment (SEA) can produce a comprehensive impact assessment of a mining project, guaranteeing the protection and valorisation of the environmental system.

From Poisons to Antidotes: Algorithms as Democracy Boosters

Under what conditions can artificial intelligence contribute to political processes without undermining their legitimacy? Thanks to the ever-growing availability of data and the increasing power of decision-making algorithms, the future of political institutions is unlikely to be anything similar to what we have known throughout the last century, possibly with parliaments deprived of their traditional authority and public decision-making processes largely unaccountable. This paper discusses and challenges these concerns by suggesting a theoretical framework under which algorithmic decision-making is compatible with democracy and, most relevantly, can offer a viable solution to counter the rise of populist rhetoric in the governance arena. Such a framework is based on three pillars: (1) understanding the civic issues that are subjected to automated decision-making; (2) controlling the issues that are assigned to AI; and (3) evaluating and challenging the outputs of algorithmic decision-making.

The dynamics of dominance: open questions, challenges and solutions

Although social hierarchies are recognized as dynamic systems, they are typically treated as static entities for practical reasons. Here, we ask what we can learn from a dynamical view of dominance, and provide a research agenda for the next decades. We identify five broad questions at the individual, dyadic and group levels, exploring the causes and consequences of individual changes in rank, the dynamics underlying dyadic dominance relationships, and the origins and impacts of social instability. Although challenges remain, we propose avenues for overcoming them. We suggest distinguishing between different types of social mobility to provide conceptual clarity about hierarchy dynamics at the individual level, and emphasize the need to explore how these dynamic processes produce dominance trajectories over individual lifespans and impact selection on status-seeking behaviour. At the dyadic level, there is scope for deeper exploration of decision-making processes leading to observed interactions, and how stable but malleable relationships emerge from these interactions. Across scales, model systems where rank is manipulable will be extremely useful for testing hypotheses about dominance dynamics. Long-term individual-based studies will also be critical for understanding the impact of rare events, and for interrogating dynamics that unfold over lifetimes and generations. This article is part of the theme issue ‘The centennial of the pecking order: current state and future prospects for the study of dominance hierarchies’.

Moving From Evidence To Decisions in Guidelines: An Analysis of Guidance Documents

Background The process of moving from evidence to decisions (EtD) represents a cornerstone within guideline development methods. Little is known about the processes used by organizations in charge of guideline development and what criteria they consider when formulating recommendations. Objective To identify and describe the processes suggested for the formulation of healthcare recommendations in health care guidelines available in guidance documents. Methods We searched in spring 2020 the Guidelines International Network (G-I-N) website, MEDLINE, and The Cochrane Methodology Register to retrieve guidance documents published in the last decade by organizations dedicated to guideline development. Pairs of researchers independently selected and extracted data about the characteristics of the guidance document, including explicit or implicit recommendation-related criteria and processes considered, as well as the use of frameworks. We conducted both descriptive and bivariate analyses. Results We included 68 guidance documents, published mostly by scientific societies (58%). Most of the organizations reported a system for grading the strength of recommendations (88%), half of them being the GRADE approach. Two out of three guidance documents (66%) proposed the use of a framework to guide the EtD process. We identified 14 recommendation-related criteria. The GRADE Evidence to Decision (GRADE-EtD) framework was the most often reported framework (19 organizations, 42%), whereas 20 organizations (44%) proposed their own multi-criteria frameworks. Using any EtD framework was related with a more comprehensive set of recommendation-related criteria compared to no framework, especially for criteria like values, equity, and acceptability. A similar association was observed between the GRADE-EtD framework and either no framework or another EtD frameworks. Conclusion The use of systematic and structured processes for moving from evidence to decisions is still limited among international organizations. The use of EtD frameworks facilitates the inclusion of relevant recommendation criteria. Among the structured frameworks, the GRADE-EtD framework offers the most comprehensive perspective for evidence-informed decision-making processes. More complete and detailed reporting in the guidance documents is warranted.

Urban scale digital twins in data-driven society: Challenging digital universalism in urban planning decision-making

The article examines the impact of the virtual public sphere on how urban spaces are experienced and conceived in our data-driven society. It places particular emphasis on urban scale digital twins, which are virtual replicas of cities that are used to simulate environments and develop scenarios in response to policy problems. The article also investigates the shift from the technical to the socio-technical perspective within the field of smart cities. Despite the aspirations of urban scale digital twins to enhance the participation of citizens in the decision-making processes relayed to urban planning strategies, the fact that they are based on a limited set of variables and processes makes them problematic. The article aims to shed light on the tension between the real and the ideal at stake during this process of abstracting sets of variables and processes in the case of urban scale digital twins.

Responsiveness and Adaptability of Healthcare Facilities in Emergency Scenarios: COVID-19 Experience

The COVID-19 pandemic revealed many vulnerabilities of the contemporary built environment along with limited preparedness and low efficiency in mitigating unexpected and unprecedented challenges. This article discusses the efficiency and responsiveness of basic hospital spatial layouts in three different scenarios: normal operation; the segregation of a large number of patients and still providing them with access to emergency healthcare, typical for a pandemic; and a sudden, extremely high number of admissions typical for compound disasters and terrorist attacks. A set of parameters and a method for general adaptability assessment (GAAT) that can be used as a tool in decision-making processes as well as evaluation of both existing facilities and the new models for resilient hospitals resulting from the experience of the pandemic are proposed. The paper emphasizes why factors among which adaptability, convertibility, and scalability should be at the very core of hospital development and management strategies. It also discusses new models of adaptable healthcare facilities that enable day-to-day operations to continue alongside a pandemic, and other emergency scenarios.

Open Government Data Use in the Brazilian States and Federal District Public Administrations

This research investigates whether, why, and how open government data (OGD) is used and reused by Brazilian state and district public administrations. A new online questionnaire was developed and collected data from 26 of the 27 federation units between June and July 2021. The resulting dataset was cleaned and anonymized. It contains an insight on 158 parameters for 26 federation units explored. This article describes the questionnaire metadata and the methods applied to collect and treat data. The data file was divided into four sections: respondent profile (identify the respondent and his workplace), OGD use/consumption, what OGD is used for by public administrations, and why OGD is used by public administrations (benefits, barriers, drivers, and barriers to OGD use/reuse). Results provide the state of the play of OGD use/reuse in the federation units administrations. Therefore, they could be used to inform open data policy and decision-making processes. Furthermore, they could be the starting point for discussing how OGD could better support the digital transformation in the public sector.