Public Sector Decision Research Articles

Integrating Equity in Public Sector Data-Driven Decision Making: Exploring the Desired Futures of Underserved Stakeholders

Public sector agencies aim to innovate not just for efficiency but also to enhance equity. Despite the growing adoption of data-driven decision-making systems in the public sector, efforts to integrate equity as a primary goal often fall short. This typically arises from inadequate early-stage involvement of underserved stakeholders and prevalent misunderstandings concerning the authentic meaning of equity from these stakeholders' perspectives. Our research seeks to address this gap by actively involving undersevered stakeholders in the process of envisioning the integration of equity within public sector data-driven decisions, particularly in the context of a building department in a Northeastern mid-sized U.S. city. Applying a speed dating method with storyboards, we explore diverse equity-centric futures within the realm of local business development, a domain where small businesses, particularly women-and minority-owned businesses, historically confront inequitable distribution of public services. We explored three essential aspects of equity: monitoring equity, resource allocation prioritization, as well as information and equity. Our findings illuminate the complexities of integrating equity into data-driven decisions, offering nuanced insights about the needs of stakeholders. We found that attempts to monitor and incorporate equity goals into public sector decision-making can unexpectedly backfire, inadvertently sparking community apprehension and potentially exacerbating existing inequities. Small business owners, including those identifying as women-and minority-owned, advocated against the use of demographic-based data in equity-focused data-driven decision-making in the public sector, instead emphasizing factors such as community needs, application complexity, and uncertainties inherent in small businesses. Drawing from these insights, we propose design implications to assist designers of public sector data-driven decision-making systems to better accommodate equity considerations.

Enacting the socio-economic duty: inequalities of outcome and section 1 Equality Act 2010

The first section of the landmark Equality Act 2010 has been lying dormant for 14 years. The ‘public sector duty regarding socio-economic inequalities’ – passed by the Labour government in 2010 – was never brought into force in England by the Conservative-led coalition that replaced them. The new Labour government has committed to its resurrection. Drawing on evidence from Scotland and Wales, where the ‘socio-economic duty’ has been in force since April 2018 and March 2021 respectively, this article offers three reflections for policy makers and those who will be tasked with its implementation in England: the promise of its ‘holistic’ incorporation into the Equality Act 2010 of impact assessment processes; the ongoing necessity for support with evidence collection and data sharing; and the duty’s potential as a catalyst for broader organisational change far beyond a strict interpretation of its legal remit. Although (very) far from a panacea, if coupled with well-executed guidance, training and oversight, its commencement could help to encourage greater consideration and transparency of evidence on socio-economic inequalities in key areas of public sector decision making.

The effects of artificial intelligence and victims’ deservingness information on citizens’ blame attribution towards administrative errors

ABSTRACT This study examines how citizens attribute blame to government authorities for administrative errors made by artificial intelligence (AI) compared to human decision-makers. Based on blame attribution theory, we conducted a vignette-based survey experiment with 1,098 Chinese citizens, revealing that respondents assign less blame for errors caused by AI or AI-assisted decisions. Additionally, disclosing victims’ deservingness information heightened blame attribution. These findings contribute to the literature on administrative accountability, highlighting how citizens respond to AI-related errors and informing the growing use of AI in public sector decision-making.

Cross‐Domain Information Integration in Government: Hierarchies and Responsibilities

ABSTRACTCross‐domain integration of information is increasingly identified as a priority across public sector contexts because (in theory) it enables the use of more information, including information from groups and communities historically excluded from public sector decision making. In this paper, we reject the tendency to take ‘integration’ for granted, arguing the need to position cross‐domain integration as an information practice, and conducting mixed methods thematic analysis of government strategic documents to validate the utility of this approach. We find that depending on the type of information proposed to be integrated — digital data versus the knowledge of peoples and communities — our sample of Canadian government institutions treats cross‐domain integration with differing levels of procedural rigour and detail. Reflecting ASIS&T 2024 themes of prioritizing responsibility and reflexivity in information practice, and of cultivating community partnerships through practice, not merely in name, we discuss the information hierarchies that emerge in the cross‐domain information integration in government and the associated impacts on stakeholder communities.

Artificial intelligence and complex sustainability policy problems: translating promise into practice

Addressing sustainability policy challenges requires tools that can navigate complexity for better policy processes and outcomes. Attention on Artificial Intelligence (AI) tools and expectations for their use by governments have dramatically increased over the past decade. We conducted a narrative review of academic and grey literature to investigate how AI tools are being used and adapted for policy and public sector decision-making. We found that academics, governments, and consultants expressed positive expectations about AI, arguing that AI could or should be used to address a wide range of policy challenges. However, there is much less evidence of how public decision makers are actually using AI tools or detailed insight into the outcomes of use. From our findings we draw four lessons for translating the promise of AI into practice: 1) Document and evaluate AI’s application to sustainability policy problems in the real-world; 2) Focus on existing and mature AI technologies, not speculative promises or external pressures; 3) Start with the problem to be solved, not the technology to be applied; and 4) Anticipate and adapt to the complexity of sustainability policy problems.

Building a Systems Map: Applying Systems Thinking to Unhealthy Commodity Industry Influence on Public Health Policy.

Unhealthy commodity industries (UCIs) engage in political practices to influence public health policy, which poses barriers to protecting and promoting public health. Such influence exhibits characteristics of a complex system. Systems thinking would therefore appear to be a useful lens through which to study this phenomenon, potentially deepening our understanding of how UCI influence are interconnected with one another through their underlying political, economic and social structures. As such this study developed a qualitative systems map to depict the complex pathways through which UCIs influence public health policy and how they are interconnected with underlying structures. Online participatory systems mapping workshops were conducted between November 2021 and February 2022. As a starting point for the workshops, a preliminary systems map was developed based on recent research. Twenty-three online workshops were conducted with 52 geographically diverse stakeholders representing academia, civil society (CS), public office, and global governance organisations (CGO). Analysis of workshop data in NVivo and feedback from participants resulted in a final systems map. The preliminary systems map consisted of 40 elements across six interdependent themes. The final systems map consisted of 64 elements across five interdependent themes, representing key pathways through which UCIs impact health policy-making: (1) direct access to public sector decision-makers; (2) creation of confusion and doubt about policy decisions; (3) corporate prioritisation of commercial profits and growth; (4) industry leveraging the legal and dispute settlement processes; and (5) industry leveraging policy-making, norms, rules, and processes. UCI influence on public health policy is highly complex, involves interlinked practices, and is not reducible to a single point within the system. Instead, pathways to UCI influence emerge from the complex interactions between disparate national and global political, economic and social structures. These pathways provide numerous avenues for UCIs to influence public health policy, which poses challenges to formulating a singular intervention or limited set of interventions capable of effectively countering such influence. Using participatory methods, we made transparent the interconnections that could help identify interventions in future work.

The management of bias and noise in public sector decision-making: experimental evidence from healthcare

ABSTRACT In six randomized online experiments with 2,647 medical doctors we test whether – depending on the choice architecture – physicians engaged in prescribing decisions in public organizations fall prey to systematic error (i.e. bias) and make significantly different choices when faced with the same clinical case (i.e. level noise). Results show that experts tend to make irrational choices that are influenced by outgroup bias, social comparison, past behaviour, confirmation bias, loss aversion, equivalence framing, and asymmetric dominance. We also find evidence of significant level noise, that is, between-prescriber variability, with the distribution of responses differing remarkably across experimental arms.

How do citizens perceive the use of Artificial Intelligence in public sector decisions?

Artificial Intelligence (AI) has become increasingly prevalent in almost every aspect of our lives. At the same time, a debate about its applications, safety, and privacy is raging. In three studies, we explored how UK respondents perceive the usage of AI in various public sector decisions. Our results are fourfold. First, we found that people prefer AI to have considerably less decisional weight than various human decision-makers; those being: politicians, citizens, and (human) experts. Secondly, our findings revealed that people prefer AI to provide input and advice to these human decision-makers, rather than letting AI make decisions by itself. Thirdly, although AI is seen as contributing less to perceived legitimacy than these human decision-makers, similar to (human) experts, its contribution is seen more in terms of output legitimacy than in terms of input and throughput legitimacy. Finally, our results suggest that the involvement of AI is perceived more suitable for decisions that are low (instead of high) ideologically-charged. Overall, our findings thus show that people are rather skeptical towards using AI in the public domain, but this does not imply that they want to exclude AI entirely from the decision-making process.

Management Accounting in the Malaysian Public Sector: Analysing the Adoption of Financial and Risk Analysis

Purpose: This paper aimed to analyse the introduction of financial analysis and risk management for internal decision-making in the Malaysian public sector and determine the limitations/ gaps as well as areas for improvement. Design/ Methodology/ Approach: The paper was based on the analysis of documents, government circulars and guidelines pertaining to financial statement analysis and risk management, including interviews with officers at two ministries. Findings: Policy and guidelines were established for financial analysis and risk management in the Malaysian public sector. However, given the complexities and unique context of each ministry, there is a need for clearer and more context-specific guidelines regarding financial and risk analysis. It is also crucial that the conducted financial and risk analyses are relevant and meet the specific needs of public sector entities. The usefulness of information relies on a comprehensive understanding of the context and types of decision needed. Therefore, fostering collaboration and engagement between the management accountant unit and other key units within a ministry is crucial to conduct an effective financial and risk analysis. Originality/ Value: This study offered insights into the financial and risk analysis reporting framework and its implementation in the Malaysian public sector. Keywords: Management accounting, public sector accounting, financial statement analysis, risk management, public sector accountants.

The role of simulation and modeling in artificial intelligence: A review

This paper aims to comprehensively explore the pivotal role of simulation and modeling in the field of Artificial Intelligence (AI). It focuses on elucidating the diverse applications of simulation and modeling in training AI systems, optimizing algorithms, and enhancing decision-making processes. To achieve this objective, we conducted an extensive review of the literature from the Scopus database, employing a well-defined selection process. We utilized keywords such as “simulation,” “modeling,” “Artificial Intelligence,” and related terms to identify relevant papers published within the last 10 years. The selection criteria included assessing the relevance, quality, contribution, and recent citations of the papers. After a rigorous screening process, we selected 40 papers with the highest overall scores for inclusion in our review. The selected papers encompass a wide range of domains where simulation and modeling play a vital role in advancing AI applications. These domains include manufacturing, healthcare, energy consumption prediction, public sector decision-making, education, environmental modeling, and more. Our review highlights how AI leverages simulation and modeling to improve predictive accuracy, optimize resource allocation, and enhance decision-making processes across diverse sectors. We also discuss the potential future directions in the integration of simulation and modeling with AI, emphasizing its significance in various fields.

Algorithmic bias: Looking beyond data bias to ensure algorithmic accountability and equity

As algorithms increasingly aid public sector decision making in the United States, it becomes important to understand how to effectively tackle algorithmic bias in systems that local, state, and federal government entities use and procure, including what kinds of policies are currently in place or proposed. There is a prevalent belief that algorithmic bias arises primarily from statistical biases present in the data used to train or develop the algorithm, but bias may also arise during data collection, problem specification, how and where algorithms are deployed, and within the broader societal contextualization of algorithms. So far, enacted policies in in the US center on temporary bans of particular types of algorithms, transparency, and post-hoc bias audits, as well as more wide-ranging (but non-binding) policy frameworks; all largely focus on quantitative notions of fairness when they assess bias, leaving room for more comprehensive legislation to meaningfully address this issue going forward.

Reflections on What Financial Economics Can and Cannot Teach Us About the Social Discount Rate

The principles of financial economics provide equally important insights into the optimal choice of discount rates for both public and private sector decision-makers. However, most governments largely ignore those principles, taking their cost of capital for most purposes as their borrowing rate. This article reviews the arguments often made in support of status quo discounting practices, along with the counterarguments to them. Governments have a choice between several methodologies for risk adjustment, and the practical and conceptual reasons that favor a fair value approach are recapped. The limitations of a financial economics approach become apparent for decisions involving very long time horizons, such as for climate policies. For policies with long-term impacts, intergenerational concerns become paramount, projections of cash flows and discount rates become highly uncertain, and present value calculations are an intrinsically unreliable measure of value. No approach to discount rate selection can overcome those problems; alternative decision criteria need to be established. However, most government investments involve much shorter horizons, and the adoption of standard approaches to risk adjustment could significantly improve social welfare.

Cost–benefit analysis of partnership working between fire and rescue and health services across England and Wales during the COVID-19 pandemic

ObjectivesFire and rescue services undertook a range of additional activities to support the National Health Service (NHS) in managing extreme service demand during the COVID-19 pandemic. This study aims to...

"It wasn't built for us": The possibility of Indigenous food sovereignty in settler colonial food bureaucracies

This article examines the extent to which Indige­nous-led food systems and sovereignty goals, frameworks, and priorities are recognized, affirmed, and supported within the agri-food pub­lic sector. For this study, we focus on the Ontario Ministry of Agriculture, Food and Rural Affairs (OMAFRA), but the findings and analysis have implications for settler-Indigenous relations more broadly. First, we situate Indigenous food systems and sovereignties within the context of agri-food bureaucracies in Canada. We then present the research design, which involved 27 interviews with people working within or collaborating with OMAFRA on issues related to agricultural land use, programming, and development, and Indige­nous relations and food systems. The find­ings are categorized into five themes: differing needs, visions, and priorities; land access, conver­sion, and health; representation; consultation and consent in agri-food programming; capacity build­ing. The findings reveal major gaps in Indigenous represen­tation, leadership, and control, and an absence of Indigenous-led planning and decision-making in the agri-food public sector. The findings further show that non-Indigenous people lack cru­cial knowledge concerning treaties and Indigenous rela­tionships to land and stewardship, which cre­ates ongoing and significant barriers to reconcilia­tion. We close by discussing key barriers and opportuni­ties for supporting Indigenous food sys­tem and sovereignty programming and ways forward for deepening settler knowledge of Indigenous issues and experiences. The perspec­tives shared in this study are intended to provide food system research, planning, policy, and practice with insights in order to begin to address structural injustices and better support Indigenous food sovereignty.

Gender bias perpetuation and mitigation in AI technologies: challenges and opportunities

AbstractAcross the world, artificial intelligence (AI) technologies are being more widely employed in public sector decision-making and processes as a supposedly neutral and an efficient method for optimizing delivery of services. However, the deployment of these technologies has also prompted investigation into the potentially unanticipated consequences of their introduction, to both positive and negative ends. This paper chooses to focus specifically on the relationship between gender bias and AI, exploring claims of the neutrality of such technologies and how its understanding of bias could influence policy and outcomes. Building on a rich seam of literature from both technological and sociological fields, this article constructs an original framework through which to analyse both the perpetuation and mitigation of gender biases, choosing to categorize AI technologies based on whether their input is text or images. Through the close analysis and pairing of four case studies, the paper thus unites two often disparate approaches to the investigation of bias in technology, revealing the large and varied potential for AI to echo and even amplify existing human bias, while acknowledging the important role AI itself can play in reducing or reversing these effects. The conclusion calls for further collaboration between scholars from the worlds of technology, gender studies and public policy in fully exploring algorithmic accountability as well as in accurately and transparently exploring the potential consequences of the introduction of AI technologies.

Is unsubsidised energy transition possible? Feasibility of replacing diesel buses with electric ones

This study assesses the economic viability of replacing the existing diesel vehicles with electric ones in the public transport sector of mid-sized European cities. It analyses the win-win situation that would result from a public-private financing of such vehicles. The proposed methodology is designed to revisit and improve public sector decision-making and its tools. The procedure entails an analysis of the acquisition and operating costs of electric buses, accounting separately for internal costs and externalities, applying different discount rates for the concessionaire and for public funds. The data used for this study comes from literature review and open-source data for Zaragoza, a medium size city in Northeast Spain. The results obtained show that investment in electric buses, by itself, is not profitable if approached only from the private sector and therefore requires public subsidy. The study, replicable in other locations, reveals the importance of this type of analysis during the decision-making process, considering public funds for sustainable urban mobility development as one solution to address energy transition.

New Public Analytics: Bringing in the Human

In Professor Yeung’s insightful and much-needed article ‘The New Public Analytics as an Emerging Paradigm in Public Sector Administration’, the focus —rightfully so— is on the use of data analytics as a form of computational analysis in the context of public administration. It delves into the question of how the turn to data-driven approaches in order to inform and even automate public sector decision-making, may bring along dangerous anomalies. In this reflection on Yeung’s article, the focus will not directly be on the technological aspects of New Public Analytics (NPA), but it will shift the focus to the human side of this new paradigm. In the end, this reflection will still be about technology, but it will take a —I hope fruitful— detour, by approaching technology through its deep-rooted connection with human life.

“It's your fault!” – said a public client to modernity advocates: an exploration of UK public sector’s viewpoints on the modern methods of construction

Purpose The staggering demand for construction projects to meet a spectrum of public needs is projected to outstrip the industry’s supply capability. The modern methods of construction (MMC) offers wider control due to shifting key construction processes offsite. Public clients play a significant role due to their purchasing power; however, their uptake of MMC is low, despite the benefits. The purpose of this study is to reveal the reasoning behind such low adoption. The research gap, herewith, is our lack of understanding of the influence of public clients perceptions on their adoption’s indecision. Design/methodology/approach This study used a qualitative approach to investigate the motives behind the public sector’s low MMC adoption. Semi-structured interviews with 14 of the United Kingdom’s public sector decision-makers, industry leaders and experts have been conducted. Perspectives were argued against the diffusion of innovation (DOI) theory. Findings Overall, the innovation’s attributes informed the authors of the positive perceptions from the public sector, demonstrating that the low adoption of MMC is not linked to any embedded issues with the innovation itself rather being predominantly related to the dynamics between supply and demand. The former (supply), reflected a failure in communicating confidence, and the latter (demand), attained characteristics that are limiting wider uptake. Originality/value To the best of the authors’ knowledge, this is the first study to apply the DOI theory to reveal the relationship between UK public clients’ perceptions and their decision-making. Moreover, this paper addresses the scant attention to the use of theories to explain the flow of innovations in the construction context.

MCDM methods to address sustainability challenges, such as climate change, resource management, and social justice

The novelty of the proposed research lies in its focus on developing and refining MCDM methods to address the complex and multifaceted nature of sustainability challenges, and evaluating the effectiveness and practicality of these methods in real-world decision-making contexts. While previous research has explored the application of MCDM methods to sustainability decision-making, this study aims to advance the field by addressing the following novel aspects: Comprehensive evaluation of MCDM methods: The study aims to comprehensively evaluate the effectiveness and practicality of various MCDM methods in addressing sustainability challenges, including climate change, resource management, and social justice. This evaluation will consider the strengths and limitations of each method, and identify opportunities for improvement. Incorporation of stakeholder values: The study will incorporate stakeholder values into the decision-making process, ensuring that the resulting decisions reflect the diverse perspectives and priorities of all stakeholders. This approach differs from traditional decision-making methods, which often prioritize the perspectives of a select few. Real-world decision-making contexts: The study will evaluate the effectiveness and practicality of MCDM methods in real-world decision-making contexts, providing insights into the challenges and opportunities associated with the implementation of these methods. This will help decision-makers to better understand how to apply MCDM methods to real-world sustainability challenges. The proposed research offers a novel and comprehensive approach to addressing sustainability challenges through the development and application of MCDM methods. By evaluating the effectiveness and practicality of these methods in real-world decision-making contexts, this study aims to provide decision-makers with a more comprehensive and informed approach to sustainability decision-making that reflects the diverse perspectives and priorities of all stakeholders. Future research in the area of MCDM methods to address sustainability challenges could focus on several areas, including: Cross-disciplinary collaborations: Given the complex and multifaceted nature of sustainability challenges, there is a need for cross-disciplinary collaborations between decision-makers, scientists, and stakeholders to develop and implement effective sustainability strategies. Future research could explore how MCDM methods can facilitate these collaborations and promote interdisciplinary dialogue and knowledge sharing. Evaluation of long-term sustainability outcomes: While MCDM methods can help decision-makers to evaluate alternatives based on multiple criteria and stakeholder values, it may be challenging to evaluate the long-term sustainability outcomes of these decisions. Future research could explore how MCDM methods can be used to evaluate the long-term sustainability outcomes of decisions, and how the effectiveness of these methods can be measured over time. Incorporation of ethical considerations: Sustainability decision-making involves ethical considerations related to distributive justice, procedural justice, and environmental ethics. Future research could explore how MCDM methods can incorporate these ethical considerations into the decision-making process to ensure that sustainability decisions are not only efficient and effective but also ethically justifiable. Evaluation of MCDM methods in different decision-making contexts: MCDM methods may be more suitable for certain decision-making contexts than others. Future research could evaluate the effectiveness and practicality of MCDM methods in different decision-making contexts, such as public sector decision-making, corporate decision-making, or community-based decision-making. Development of user-friendly decision support tools: While MCDM methods can be effective in addressing sustainability challenges, they may be challenging to implement in practice. Future research could focus on the development of user-friendly decision support tools that can help decision-makers to implement MCDM methods more easily and effectively. Overall, future research in the area of MCDM methods to address sustainability challenges should focus on advancing the understanding and application of these methods in real-world decision-making contexts. By addressing the challenges and limitations of these methods and developing new approaches to address emerging sustainability challenges, researchers can help decision-makers to make more informed and sustainable decisions for the benefit of society and the environment.

Pro profits or non-profits? A principal-agent model for analyzing public sector planning decisions and empirical results from planning applications in Hong Kong

This paper introduces a principal-agent analysis of the public sector decisions made by a non-democratic planning body under public scrutiny. The central idea is that divergence between the interests of the principal (citizens), whose objective is to maximize the total value of the land in Hong Kong, and the agent (planning approval authority), whose concern is about Approval Risk, which is the agent's risk of being accused of collusion or even bribery. This principal-agent analysis can be applied to any public-sector decision-making process in a society with the rule of law and freedom of speech. It was found by a logit analysis of 1440 sets of disaggregate data that planning applications by non-profit applicants for uses in Government/Institution/Community (GIC) zones were more likely to be approved than those by for-profit private organizations, in line with the idea of a lower perceived Approval Risk. Amongst the applications by private organizations, proposed high-value land uses were less likely to be approved than those proposed for low-value uses, probably because the former results in potentially higher economic gain for the applicants. The legislative change that took effect in July 2005 has allowed a wider public participation in the planning application process. This policy change was used in this study as a test condition for the effect on planning approvals of an increase in the principal's involvement in the decision-making process. It was found that the policy change did increase the success rate of planning applications made by private organizations, though the impact was mainly on the applications for low-value land uses only. The empirical result shows that the perceived Approval Risk for high-value land uses was too high to be affected by the policy change.