With the explosive development and widespread application of a new generation of strong artificial intelligence technologies represented by ChatGPT, human society has entered the era of artificial intelligence, where data, algorithms, and computing power are the core elements. Looking back in history, the wave of new technological revolutions characterized by “creative destruction” has often brought about significant changes in productivity and production relations, national governance, and even the global landscape. Generative artificial intelligence, based on large language deep learning models and characterized by overwhelming probability and intuitive “emergence,” demonstrates human-like cognitive abilities and multiple intelligences, garnering attention and application from governments worldwide and promoting the intelligent transformation of national governance—referred to as AI governance. At the same time, this has raised concerns about its risks, threats, and ethical issues. Intelligence is originally a subject of psychological research, while intelligent governance is a new proposition in the era of technocratic political science. Artificial intelligence exists objectively and is also a human-made fact. Promoting good governance through AI requires careful examination of the relationship between artificial intelligence systems, human psychological processes, and national governance processes, clarifying its potential, limitations, and future directions in advancing the modernization of national governance. Specifically, it involves the following aspects.First, the cognitive and emotional dimensions of AI governance. Cognitive psychology studies how humans process information and make decisions; AI, through automated data analysis and insight generation, alleviates the enormous information barriers and cognitive loads faced by decision-makers in governance processes, improving decision quality and efficiency. For instance, AI can analyze economic indicators, social trends, and environmental data to provide decision-makers with comprehensive reports and predictive models. Cognitive biases such as confirmation bias and availability heuristic can significantly impact human decision-making, while AI mitigates these biases by providing objective, data-driven insights. For example, predictive policing algorithms identify crime patterns and propose preventive measures through data analysis rather than relying on subjective judgment. AI’s emotional intelligence refers to machines’ ability to recognize, understand, and respond to human emotions, which is applied in public services. For example, while it cannot replace human relationships, AI chatbots can interact with citizens in a more empathetic and personalized manner, responding to their concerns and tricky questions in real-time, providing help and support, especially for those with no one to turn to; in this human-machine interaction, citizens may establish some emotional connection with robots through anthropomorphism.Second, the governance value of AI in policy processes and public services. AI can analyze vast amounts of data and simulate potential policy outcomes, assisting decision-makers in evaluating trade-offs and formulating evidence-based policies. In policy implementation, AI can promote human-machine collaboration and inter-organizational synergy, reducing administrative burdens and improving the efficiency and effectiveness of government operations. Automated systems monitor policy implementation in real-time, identifying deviations and suggesting corrective measures, thereby enhancing the agility of government interventions. For instance, AI tracks the progress of infrastructure projects, detects delays or cost overruns, and suggests adjustments to ensure timely and budget-compliant completion. AI can also play a crucial role in the evaluation and feedback processes of public policies. By continuously monitoring key performance indicators and collecting citizen feedback, it helps to timely adjust and improve policies. Furthermore, AI can help identify unintended consequences and provide suggestions to mitigate negative impacts. The application of AI in various fields of public service significantly enhances governance effectiveness. For example, in the public healthcare sector, AI can provide predictive analyses of disease outbreaks, personalized diagnosis and treatment plans, and efficient resource allocation; the intelligent government service platform of “one network for all” and smart cities enhance public service delivery by streamlining processes, quickly connecting, and ensuring eligible individuals receive timely assistance.Third, the political psychology of AI governance. Political psychology studies how psychological factors influence political behavior and decision-making. The introduction of AI in governance can reshape these processes by providing new tools and insights. However, this also raises concerns about the extent to which AI should influence political decisions and the potential for AI to be used to spread misinformation and manipulate public opinion. For example, AI-driven analysis can help politicians understand voter behavior and preferences, but it may also be used to micro-target political information and influence elections. The use of AI in governance also impacts political polarization, particularly as AI algorithms used on social media may create echo chambers and reinforce existing biases, leading to increased political polarization. AI may not be value-neutral or ideologically neutral; compared to gender or race-based algorithmic biases, algorithmic biases based on political inclination are also increasingly raising concerns. A key aspect of AI governance lies in public perception and acceptance. Political psychology can help understand the factors influencing public attitudes toward AI governance, including digital literacy, trust in technology, perceived benefits and risks, and cultural attitudes.Fourth, the limitations and challenges of AI governance. Despite its vast potential, AI still faces technical limitations. Currently, generative AI based on probabilistic predictions may struggle with understanding context and nuances, logical reasoning, and other aspects, such as “machine hallucination” and “reversal curse.” Additionally, AI systems require vast amounts of data to operate effectively; data scarcity or poor data quality may limit their usefulness in governance processes, while deepfakes, misinformation, and preference disguises undoubtedly exacerbate this issue. The deployment of AI in governance may also bring social and ethical risks, including privacy issues, potential increases in surveillance, and exacerbation of social biases and inequalities. The degree of bias in AI systems depends on the bias present in their training data, making bias and fairness urgent ethical issues in applying AI in governance. The opacity and difficulty of explanation of AI black-box algorithms may undermine public trust and pose accountability challenges. Finally, integrating AI into governance may lead to negative impacts such as “machine replacement,” facing resistance and aversion from various stakeholders, including government officials, employees, and the public, necessitating effective change management strategies and proactive arrangements, including clear communication, training and support, and compensatory measures for affected groups.Fifth, the future directions of AI governance. Specific areas may include establishing AI ethical frameworks, promoting public participation and education, fostering international cooperation, continuous monitoring and evaluation, establishing interdisciplinary approaches, and ensuring inclusive development. To harness the potential of AI while mitigating its risks, governments should develop robust ethical frameworks for AI, ensuring the responsible and ethical use of AI systems, including establishing clear guidelines for data collection and use, setting contextualized ethical scripts, implementing bias detection and mitigation measures, and ensuring transparency and value alignment of AI systems. Public participation and education are crucial for the successful integration of AI in governance; governments should actively involve citizens in discussions about AI policies, making stakeholders aware of the benefits and risks of AI, and enhancing citizens’ digital literacy, awareness of ethical considerations, and critical thinking skills through educational programs to foster public trust and support for AI, helping citizens navigate the complexities of AI technologies. Given the global nature of AI technology, international cooperation is vital in addressing the challenges and opportunities of AI in governance; governments should collaboratively develop common standards and best practices for AI deployment, share knowledge and experiences, and cooperate in research and development. The deployment of AI in governance should be accompanied by continuous monitoring and evaluation to ensure systems operate as intended and achieve their goals, including regular assessments of AI’s impact on decision-making processes, public services, and social outcomes, establishing feedback mechanisms, collecting stakeholder opinions, and making necessary adjustments to enhance the effectiveness and fairness of AI systems. Addressing the complexities of AI in governance requires advocating interdisciplinary approaches, incorporating insights from relevant fields such as computer science, psychology, law, ethics, and public policy, promoting collaboration between governments and research institutions, and the private sector to leverage collective wisdom to develop comprehensive and balanced AI strategies. To prevent AI from exacerbating social inequalities, policies must ensure diversity and inclusivity in AI research, development, and deployment, considering the needs of all social groups, especially ordinary people, including providing equal opportunities to benefit from AI, addressing the digital divide, and ensuring that AI systems are accessible and usable for everyone, regardless of their socio-economic background.In summary, artificial intelligence demonstrates immense potential in the modernization of national governance; it can enhance decision-making efficiency, improve public services, and promote citizen participation. However, the governance applications of AI also bring numerous issues, including bias, fairness, transparency, and accountability. A robust legal and ethical framework, technical infrastructure, and public trust and capability are crucial for the successful implementation of AI governance. We should better understand the potential and limitations of AI governance, develop responsible and human-centered AI governance strategies, and achieve the goal of promoting good governance through artificial intelligence in national governance through open participation and mutually beneficial sharing.
Source: China Social Sciences Net
