1. Introduction

AI is becoming increasingly recognised as a powerful instrument for making better decisions in public policy. With its large-scale data-analysis and predictive power, AI provides public agencies with opportunities to make policy interventions more accurate, effective and timely. AI-based DSS can be used in a variety of public domains, ranging from better use of healthcare resources to better management of social welfare and urban planning. In healthcare, for example, AI-based predictive models can predict demand for medical assets, speeding up emergency response and allowing policymakers to better allocate resources. Similarly in social welfare, machine learning algorithms are used to decide eligibility, calculate benefits and identify fraud, thus streamlining the system and reducing the bureaucracy. These benefits aside, AI-driven public policy presents considerable challenges as well. Limitations in infrastructure, such as limited data storage and processing power, hinder AI deployment, especially in areas where digital infrastructure is scarce. Second, moral concerns, like algorithmic biases and issues of transparency, threaten fairness and the public good. AI models trained on the past could inadvertently reinforce the biases we already have, creating unequal results in areas such as criminal justice, social services and healthcare. These biases, without proper oversight, might obfuscate the perceived legitimacy of AI-based policy decisions, disproportionately affecting the poorest and driving social inequalities. Data privacy and security is also a big concern, because to make AI a part of public policy, it will require access to massive amounts of sensitive data. Data protection laws, including the General Data Protection Regulation (GDPR), require very strict data processing, consent, and breach reporting [1]. Thus, policymakers and AI developers need to collaborate to make AI systems legal while respecting people’s privacy.

The paper examines the use and shortcomings of AI in public policy DSS, applying cases to health, social welfare and urban planning. Using these examples, the paper evaluates the performance of AI in terms of accuracy, efficiency, fairness and stakeholder acceptance. By examining how AI can be used to drive public policy and its inherent shortcomings, this paper aims to provide an integrative view of AI governance, by proposing ways to overcome barriers and increase public confidence in AI-driven policymaking.

2. Literature Review

2.1. AI in Public Policy Decision Making

AI transformed public policy, increasing the precision and efficacy of resource use, health care service delivery, and the management of social welfare. Machine learning (ML) algorithms can extract patterns and predict outcomes from huge amounts of data so that policymakers can better respond to society’s demands. Predictive analytics, for example, is able to predict medical resource demand, speed emergency response and efficiently distribute resources during crisis events, like pandemics. A practical example is how prediction models can be employed in healthcare systems to control bed occupancy, admission time, and workforce allocations, thereby aligning resources with patient demand and service needs [2]. We also see AI usage in welfare schemes where application processing, benefits calculation and fraud detection systems all employ ML algorithms to filter applicants, detect patterns and anticipate future needs. These applications show that AI can be applied to traditionally time-consuming policy domains, making it possible for governments to get better, data-driven policy outcomes.

2.2. Ethical Considerations

AI in public policy is itself a topic of deep ethical concern, mainly over the potential bias of AI algorithms. AI models generated on discriminatory datasets can inadvertently reinforce discrimination and result in arbitrary or unjust policies. Predictive policing algorithms, for example, deployed in the police force have been accused of biasedly criminalising certain demographic groups because of historical biases. In order to prevent these biases, public policy AI systems should be easy to explain. AI models must have access to policymakers’ decision-making mechanisms so that they can be held accountable and confirm public interest decisions. Making AI-based policy equitable, just and inclusive requires fairness-oriented ML methods and frequent model checks [3]. If we develop ethical codes and oversight groups devoted to checking AI systems in governance, we can avoid abuse and build confidence in AI-based policies.

2.3. Data Privacy and Security

Bringing AI into public policy involves working with large amounts of sensitive data, including citizens’ personal information. Privacy and security of data is therefore paramount. Countries need to be compliant with data protection laws, like the General Data Protection Regulation (GDPR), which stipulates very strict policies on the processing of data, consent, and breaches. AI designers and policymakers will need to work together to develop data anonymisation mechanisms that protect individuals’ privacy without undermining data usefulness. Discriminatory privacy, using statistical noise to shield individuals’ data, and secure multi-party computation, where data can be processed without exposing it are promising methods. These can be implemented to significantly reduce the possibility of data breaches and data snooping, increasing public confidence in AI-based governance [4].

3. Experimental Methods

3.1. Case Study Selection

In order to illustrate AI use in public policy, we have chosen case studies for health, social welfare and urban planning because of their varying data requirements and major societal impacts. Each case illustrates how AI could be used in practice to address specific policy issues. The research in public health, for instance, deals with AI-based pandemic planning, involving predictive models of resource allocation and disease surveillance. In social welfare, we explore the use of AI in eligibility for benefits and fraud detection. Finally, for city planning, the research examines how AI regulates traffic, observes air quality and anticipates infrastructure maintenance requirements [5]. These examples highlight how AI can be utilised in real-world decisionmaking in public policy.

3.2. Evaluation Metrics

In evaluating the use of AI in DSS, this paper employs a series of evaluation measures that aim to identify the technical and social effects of AI on policy outcomes. These metrics — accuracy, efficiency, fairness, stakeholder engagement — provide an all-encompassing measurement that transcends mere performance metrics and enables AI applications to serve society at large.

Accuracy reflects how accurate and reliable AI predictions in policy contexts are for informed decision making. In healthcare or social services planning, for example, accuracy means AI-generated predictions are in good alignment with real-life demand and not at the expense of over- or under-provisioning needed resources. Correct predictions result in more successful policies, whose consequences are much more closely aligned with predicted outcomes. Efficiency evaluates how AI can streamline operations, shorten decision-making cycles, and reduce resource use when implementing policies. Rather than just speeding up the process of data and analysis, an effective AI system saves people time and effort in doing so. For instance, in social welfare programmes, AI could speed up the application review process, easing wait time for applicants and freeing up resources for other vital tasks [6]. Efficiencies help reduce costs and allow agencies to spend their budgets more effectively. Fairness evaluates the capacity of the AI system to deliver equal outcomes across a broad range of demographics so as to minimize bias and avoid imposing unequal outcomes on any one population by means of AI. This measurement is particularly relevant in public policy where policies need to be equitable and inclusive. Egalitarian AI-driven DSS entails algorithms that are carefully designed and monitored to catch biases, which could otherwise cause discriminatory decisions in healthcare, criminal justice or social welfare. Fairness fosters trust and ethical leadership. Stakeholder Engagement is used to assess the system’s ability to accommodate and respond to feedback from stakeholder stakeholders, including citizens, policymakers, and other relevant organizations [7]. This score reflects how inclusive the AI system is, where decision-making is not only data driven but also responsive to many different viewpoints and requirements. High stakeholder participation is the foundation for public support and legitimacy for AI-based policy, because it gives affected stakeholders a chance to participate in policy decisions. Taking stakeholder ownership increases transparency and accountability, making policy making more democratic.

3.3. Data Collection and Analysis

This research employs a mix-methods approach, gathering quantitative data on system performance and qualitative insights through stakeholden interviews. Quantitative data, including system accuracy and processing times, is collected from the selected case studies to provide objective performance metrics. To evaluate the overall effectiveness of Al in publid policy, we calculate an aggregate performance score, \( P \) , using a weighted formula:

\( P={w_{1}}\cdot A+{w_{2}}\cdot E+{w_{3}}\cdot F+{w_{4}}\cdot T \) (1)

where \( A \) represents system accuracy, \( E \) represents processing efficiency, \( F \) represents fairness in decision-making, \( T \) represents stakeholder trust, and \( {w_{1}},{w_{2}},{w_{3}},{w_{4}} \) are weights assigned based on the importance of each metric in a specific policy context [8].

Qualitative data from interviews with policymakers, data scientists, and beneficiaries provide insights into the subjective impact of Al systems, such as ease of use, trust in Al-driven decisions, and perceptions of fairness. Integrating both data types enables a comprehensive analysis of Al’s effectiveness and social implications in public policy, with the formula providing a standardized metric for performance comparison across case studies.

4. Results and Discussion

4.1. Effectiveness of AI in Decision Support

AI-enabled public policy requires access to huge amounts of personal information, including people’s names. Data privacy and security is thus a key consideration, because data that is mishandled can expose you to major liabilities, such as hacking and loss of public confidence. To tackle these issues, governments need to follow data protection laws, including the General Data Protection Regulation (GDPR), which provides strict rules for how data is handled, who is allowed to use it, and how to report a breach. The paper experimented and tried to see whether different data protection methods were applicable in public policy AI. As summarized in Table 1, differential privacy and secure multi-party computation achieved superior data protection scores by drastically reducing the likelihood of privacy violations and preserving the utility of the data for policy purposes. Differential privacy, for instance, adds statistical noise to hide people’s data, while secure multi-party computation analyse data without revealing the raw data, thus protecting sensitive data. These findings, as discussed and portrayed in Figure 1, highlight the obvious advantages of employing these methods for data security and public confidence. The bar graph compares the relative performance of each technique on metrics such as data anonymization, processing speed and compliance with the regulations [9]. This experiment highlights the need for AI designers and policymakers to work together to create systems that are data privacy-focused, and thus encourage citizens’ confidence in AI-led governance.

Table 1: Data Protection Techniques Performance

Figure 1: Comparative Effectiveness of Data Protection Techniques

4.2. Challenges in AI Integration

Figure 2: Severity of AI Integration Challenges and Policymaker Concern

Although laudable, AI as a public policy tool presents challenges. Physical barriers, like poor data storage and processing power, keep AI out of action, especially in the low-tech zones. Figure 2 Infrastructure restrictions received an 8/10 impact severity rating in Table 2, with 85% of policymakers citing this issue as a concern. Removing these limitations means investing heavily in digital infrastructure and in policy workers, both of which are essential for the successful application of AI [10]. The ethical issues, especially algorithmic biases, are not resolved in many of these cases. As illustrated in Table 2 and shown in Figure 2, ethical concerns are ranked highest in impact (9 of 10) and level of policymakers’ concern (92%). Absent a comprehensive control, neo-biased algorithms can fuel inequality in areas like social welfare and criminal justice, and destroy fairness and confidence in AI-based policies. There is also public skepticism around AI’s influence over policy decisions, although 76% of policymakers believe that it has an impact. Mechanisms for increasing transparency, including open-access AI model documentation, can encourage trust in AI’s governance capacity. This work is necessary to meet the 7-out-of-10 intensity of public skepticism as illustrated in Figure 2. These openness practices are essential for increasing public acceptance and understanding of AI-guided public policy [11].

Table 2: Challenges in AI Integration and Their Severity

5. Conclusion

The applications of AI in public policy are revolutionary, driving decisions through greater data-driven understanding, greater precision and optimal resource allocation in areas such as health, social welfare and urban development. AI applications in these areas have shown that technology can empower governments to be responsive and efficient – matching resources more precisely to citizens’ needs. But bringing AI into practice as public policy is not an easy task. AI is thwarted by infrastructure barriers, especially in places with limited technological infrastructure, and they have to be addressed by investing heavily in digital infrastructure and worker education. Ethical issues, especially algorithmic biases, are of prime importance because biased AI models can result in unjust policy. Fairness, transparency and accountability when making AI-based decisions are crucial for maintaining public confidence and avoiding unjust consequences. Privacy concerns also require that AI algorithms be built with robust data protection to protect private data without sacrificing the power of data for policymaking. From now on, a more sensible compromise between ethical, open and secure AI will be the key to successful and socially responsible adoption of AI in public administration. Researchers should continue to refine AI models, address ethical and technical issues, and set up rigours for ethical AI deployment in public policy, in order to unlock AI’s potential to be used for the greater good.