Many worry about how our leaders make decisions. But what if we considered how an AI, like ChatGPT, could make these decisions instead of elected officials?

To be clear, this an exploration rather than a call for action. By comparing how AI might handle different issues with how real mayors have approached them, we can ask ourselves: Could AI do a better job?

Let's dive into how ChatGPT’s decisions stack up against those of real-life leaders. This comparison might show us a new, possibly better way to handle important community issues.

Challenges and Considerations

AI government begs many questions, especially when it comes to ethics, human empathy, and practical issues. Is it ethical for AI to make decisions that affect people’s lives. AI doesn’t understand human emotions or experiences the way people do. Can it handle complex social problems properly? Should a machine decide things that deeply impact individuals when it can’t truly understand their feelings? Do the feelings of the few outweigh the needs of the many?

There is often criticism that governments are corrupt or too influenced by business interests. AI works with data and algorithms, lacking the emotional insight and personal touch that humans bring to leadership. This lack of empathy might result in decisions that are fair; but fail to fully consider the human side of things.

There are also practical challenges with using AI in politics. One major issue is making sure AI decisions are accountable and transparent. Even though AI can provide unbiased advice, we need to have ways to oversee its decisions and fix any biases in its training data. It’s also important that AI decisions fit with societal values and laws. Plus, putting AI into existing political systems means making big changes, like updating regulations and training people to work with AI.

AI’s role in addressing special interests and social issues is also complex. While AI might avoid favoritism and act fairly, it could miss out on the specific needs of different groups, or even perpetuate injustice. For example, AI might challenge local “not in my backyard” attitudes by focusing on broader societal benefits rather than individual concerns. It might also help fight systemic issues like racism by promoting fair policies based on data rather than past biases.

Overview of Historical Cases

When evaluating how algorithms stack up against personal experience, gut instincts, and loyalty, it's crucial to understand the fundamental differences between data-driven decision-making and human judgment. Human traits are shaped by individual experiences, cultural backgrounds, and emotional responses.

Algorithms, such as those used in AI, rely on vast amounts of data to identify patterns and make recommendations. They operate based on predefined rules and statistical analysis, which can lead to highly objective outcomes. However, they lack the ability to experience emotions, personal biases, or contextual subtleties that often inform human decisions.

To explore these differences in practical terms, let’s look at three real-life examples of city leadership decisions and how they could contrast with AI-driven approaches:

1. San Francisco Same-Sex Marriage

In 2004, San Francisco’s decision to issue same-sex marriage licenses, despite their lack of legal recognition at the state level, became a defining moment in the struggle for LGBTQ+ rights. This pivotal action brought to light the intricate balance between legal constraints, civil rights advocacy, and public opinion. By comparing the real-world actions taken by San Francisco with a roleplayed hypothetical approach, we gain valuable insights into how different strategies address such complex issues.

San Francisco’s approach involved several points:

• Legal Ambiguities: The city issued same-sex marriage licenses under a temporary legal framework, understanding that these licenses might face legal challenges. This approach allowed the city to meet immediate demands while awaiting judicial review.
• Civil Rights Framing: The decision was framed as a fundamental civil rights issue. Emphasizing equality and non-discrimination, San Francisco positioned the issuance of licenses as a stand against systemic injustice and a step toward broader social change.
• Community Engagement: The city actively engaged with LGBTQ+ advocacy groups and community leaders, launching educational campaigns to explain the significance of the decision. Public forums and town hall meetings provided platforms for community dialogue and feedback.
• Public Opinion Management: Transparent communication strategies were employed, including public forums and positive storytelling, to manage public opinion and address backlash.
• Monitoring and Adaptation: San Francisco established feedback mechanisms to gauge public sentiment and adjust strategies based on community response and emerging issues.

https://chatgpt.com/share/25c57b4d-7648-4eab-9ca5-47848f774018

I want to roleplay. You are the mayor of San Francisco. The year is 2004. Same-sex marriage is not legal at the state level. Are you willing to issue same-sex marriage licenses?

AI chose more or less the the same approach:

• Legal Clarity: The roleplayed approach offers a more precise legal framework, reducing ambiguity and enhancing the city's preparedness for legal challenges. This contrasts with the real-world approach, which, while effective, operated within the constraints of existing legal uncertainties.
• Civil Rights Framing: By incorporating a global human rights context, the roleplayed approach provides a broader and more compelling argument for equality, potentially garnering greater support and legitimacy.
• Public Opinion Management: Proactive management using real-time data and tailored communication strategies in the roleplayed approach addresses emerging issues more effectively, compared to the more reactive measures of the real-world approach.

While San Francisco’s real-world actions in 2004 were groundbreaking and impactful, the AI offered a more detailed and strategic framework for addressing complex social and legal challenges.

2. Chicago Race Riots

During the Chicago Race Riots of 1968, the city experienced severe unrest following the assassination of Martin Luther King Jr. The riots, fueled by longstanding racial tensions, economic disparities, and systemic discrimination, involved widespread violence, looting, and arson. The crisis posed a significant challenge for city officials.

In response to the Chicago Race Riots, several key actions were taken:

• Use of Lethal Force: Authorities authorized the use of force to control the situation. This included police and National Guard interventions to address looting and arson. The use of force, especially lethal, was controversial and risked escalating the violence and deepening mistrust between the community and law enforcement.
• Deployment of National Guard: The National Guard was mobilized to support local law enforcement. While this provided additional resources, it also heightened the perception of military control over civilian areas, which could exacerbate tensions.
• Imposition of Curfew: A citywide curfew was enforced to reduce nighttime violence. This measure aimed to restore order but also disrupted daily life and was seen as punitive by some residents.
• Restriction of Media Access: Efforts were made to control media coverage of the riots, aiming to manage public perception and prevent the spread of misinformation. However, this also led to criticisms of censorship and a lack of transparency.

ChatGPT was provided with a prompt explaining the situation. The full conversation is available at https://chatgpt.com/share/b46a7d7b-f371-40b9-930b-fd3f65920977

I want to roleplay. You are the mayor of a chicago sized city. Race riots are occurring. I want advice on specific decisons.

In 1968, the city is a tense and divided place, grappling with deep racial tensions and widespread disillusionment. The community’s demographics reflect a mix of ethnicities, with significant racial segregation between neighborhoods. The local police force is predominantly white, leading to strained relations with the city's Black population, who face systemic discrimination and economic hardships.

The civil rights movement is in full swing, demanding equality, justice, and the end of segregation. Nationally, there have been significant advances in civil rights, but in many communities, change is slow, and frustration is growing. This tension has been building for years, as promises of equality have often gone unfulfilled.

In this climate, the assassination of a key civil rights leader triggers widespread outrage. The city, like many others across the country, erupts in protests, which quickly turn into violent riots. These disturbances are marked by clashes between demonstrators and law enforcement, looting, arson, and widespread destruction. The riots are not just about the assassination; they reflect years of built-up anger over poverty, discrimination, and police brutality.

The local government faces intense pressure to restore order while addressing the underlying issues fueling the unrest. Law enforcement is mobilized on a large scale, and the mayor must navigate the fine line between quelling the violence and acknowledging the protesters' grievances. The eyes of the nation are on the city as it becomes a flashpoint in the broader struggle for civil rights and social justice.

Rather than repeat the entire conversation; the key differences are outlined:

• Use of Force: AI emphasized the use of non-lethal force and de-escalation techniques rather than lethal force. This aimed to minimize harm and maintain community trust, addressing the immediate crisis without escalating tensions further.
• Deployment of State Military Forces: The National Guard was envisioned in a supportive role, rather than as a confrontational force. This approach sought to enhance the capabilities of local law enforcement while avoiding the negative perception of military overreach.
• Curfew Implementation: Historical curfews were often seen as punitive, while AI advocated for a fair and transparent curfew policy, ensuring that essential activities were accommodated. This aimed to balance public safety with minimizing disruption to daily life.
• Media Access: Historical efforts to restrict media coverage led to accusations of censorship. Instead of restricting media coverage, the AI supported open media access and balanced reporting. This was intended to maintain transparency and public trust while managing sensitive information effectively.
• Directive on Addressing Root Causes: The roleplayed approach prioritized not only immediate crisis management but also efforts to address underlying issues such as economic disparity and racial injustice. This holistic view aimed to provide both short-term stability and long-term solutions.

AI offered a more nuanced strategy for managing civil unrest. By prioritizing non-lethal methods, transparency, and community engagement, it aimed to address both immediate needs and underlying issues. While historical responses focused on immediate stabilization, the AI approach could have fostered a more constructive relationship between authorities and the community.

3. New York City Sugar Tax

The obesity epidemic is a major public health issue which significantly contributes to conditions like diabetes and heart disease. Finding effective policy measures to address this issue involves navigating complex challenges, including public resistance, political dynamics, and practical implementation.

In the real world, former Mayor Michael Bloomberg’s proposal for a soda tax in New York City serves as a prominent example. The tax aimed to reduce sugary drink consumption and generate revenue for public health initiatives. However, the proposal faced considerable obstacles:

• Public Resistance: The tax was perceived as an infringement on personal choice, leading to substantial opposition from the beverage industry and parts of the public.
• Political and Public Support: The tax struggled to secure broad political and public backing. The proposal’s benefits were not always clearly communicated, and the revenue use was not sufficiently detailed.
• Communication Challenges: The messaging around the tax was criticized for not effectively addressing public concerns or emphasizing the health benefits and the intended use of the revenue.
• Implementation Issues: The tax encountered legal and logistical challenges that complicated its potential enactment.

Using lessons learned in Mexico, and other US cities, AI addressed some of the limitations https://chatgpt.com/share/a4860fde-558d-4a55-98b1-bd5605851f46

• Gradual vs. Immediate Implementation: AI advocated for a phased implementation of the tax, which can help mitigate resistance and allow for adjustments based on public feedback. This contrasts with the real-world approach, which faced challenges due to its immediate and unphased nature.
• Enhanced Communication: AI emphasized a comprehensive public education campaign to clearly communicate the tax’s benefits and revenue use. This is a departure from the real-world approach, where the messaging was less effective and struggled to gain public support.
• Proactive Stakeholder Engagement: AI recommended active engagement with stakeholders, including industry leaders and community organizations, to build support and address concerns. This approach addresses the real-world challenge of opposition and resistance by fostering collaboration and understanding.
• Transparency and Trust: By clearly outlining the use of revenue and demonstrating tangible benefits, AI aimed to build public trust and support. New York lacked transparency and clarity on revenue allocation

By incorporating gradual implementation, comprehensive communication, proactive stakeholder engagement, and transparent revenue allocation, AI attempted to address the shortcomings of the real-world strategy. It provided a potentially effective framework for overcoming resistance, building support, and ultimately achieving the public health goals associated with a soda tax.

Who Decides Right and Wrong?

Understanding that our values and morals change over time is important when thinking about how AI could help govern society. Even today things like slavery, making being LGBTQ+ illegal, and beating children, are still accepted in many places. What we think is right or wrong can shift, and any AI that helps make decisions would need to reflect the values of its time, not just follow a set rulebook.

An AI that governs needs to be flexible and keep up with these changes. An AI made in a time when discrimination was common might operate very differently from one designed in a more progressive era. We may not even know what biases to look for.

It would be foolish to declare we’re at the peak of moral understanding. Our current values reflect what we know and believe now, and future societies might have even better ideas about fairness and justice.

The goal should be to make sure AI can, over time, drive society to be the best it can be.

The Future of AI in Governance

The integration of AI into governance and policy-making is an exciting and rapidly evolving area that could reshape how societies are managed. AI is already making waves in various governance tasks, such as analyzing public sentiment, predicting economic trends, and optimizing resource allocation. Governments are increasingly adopting AI to handle large amounts of data, inform policy decisions, and improve administrative efficiency. For example, AI-driven tools help identify crime hotspots and streamline bureaucratic processes, making public services more responsive and efficient.

Looking to the future, the role of AI in governance could become even more transformative. One potential development is the creation of AI systems that can make real-time decisions. As AI technology advances, it could enable a more flexible approach to governance, where policies are continuously updated based on the latest data and trends. This real-time capability could help tackle complex social issues more effectively by providing deeper insights into how different policies impact society and predicting their long-term effects.

Another exciting possibility is the emergence of AI government officials. These AI systems could engage directly with citizens, providing a new way for people to interact with their government. Innovations like AI-powered chatbots might allow citizens to voice their concerns, give feedback, and participate more actively in decision-making. This could lead to a more inclusive and responsive form of governance, where public opinions and needs are more easily integrated into policy formation.

Moreover, as AI takes on a larger role in governance, there will be an increasing focus on ethical considerations and transparency. It's crucial to develop frameworks that ensure AI operates fairly and without bias, maintaining democratic principles and public trust. Guidelines will need to be established to prevent AI from reinforcing existing biases and to ensure that its use supports rather than undermines societal values.

In this evolving landscape, AI's potential to drive societal progress is substantial. From real-time decision-making to more interactive and inclusive governance models, AI could significantly enhance how societies are managed.

Conclusion

In considering the role of AI in governance, the potential benefits are clear. AI systems, driven by data and potentially devoid of personal biases, may offer a level of consistency and impartiality that can enhance decision-making processes. With the ability to analyze vast amounts of information quickly, AI can identify patterns and insights that might be missed by human leaders. This capability allows for well-informed, evidence-based decisions that could drive more effective policy outcomes.

Moreover, the transparency of AI systems could transform how decisions are made and communicated. An AI-driven administration could offer unprecedented clarity into its decision-making processes, enabling citizens to understand and engage with the rationale behind policies. This level of transparency could reduce the influence of special interests and mitigate issues such as corruption and favouritism.

The integration of AI into governance is not without its challenges. Ethical considerations, such as ensuring fairness and addressing potential biases in training data, must be carefully managed. While AI can provide a highly objective perspective, it is crucial to address these concerns to maintain public trust and equity.

While AI governance presents compelling advantages, it is essential to approach its implementation with caution and thoughtfulness. The goal should be to harness the strengths of AI to complement existing governance structures, enhancing decision-making processes and ensuring that the administration serves the best interests of all citizens. As we explore these possibilities, it is important to remain mindful of both the potential and the limitations of AI, striving to create a balanced and effective governance system that leverages the best of both technology and human insight.