Artificial intelligence (AI) has been a powerful force for change in recent years, altering a variety of societal spheres from retail, finance, healthcare, and logistics to education and entertainment. Deep ethical, social, and legal issues are being raised as AI technology continues to permeate all parts of daily life. These issues call for intelligent and inclusive public involvement and participation in AI governance.
The development, deployment, and application of AI technology are governed by a wide range of policies, laws, and regulations. Most governance models that are built on traditional legal systems often lag behind technological advancements. The rapid pace of AI innovation and proliferation presents unique challenges in ensuring appropriate oversight. The conventional top-down approach to governance is increasingly being challenged by the need for a more flexible, adaptable, inclusive, and participatory governance model.
Public engagement plays a pivotal role in democratic governance. It empowers citizens to actively participate in shaping laws and policies that impact their lives. Given the far-reaching impact of AI technologies, traditional governance models and structures alone may not be sufficient to address the multifaceted challenges it poses. Public engagement, therefore, becomes especially critical to fostering trust, transparency, and legitimacy in the governance of AI technologies.
The power dynamics between AI developers, businesses, governments, and the public are complex, necessitating a collaborative approach that includes a diverse array of stakeholders. In addition to the general public, effective AI governance should also include the voices of stakeholders with a direct interest in AI technologies. These stakeholders encompass a wide array of groups, including but not limited to, AI developers, researchers, businesses, ethicists, civil society organizations, and representatives from marginalized communities. Incorporating their perspectives in the decision-making and governance processes helps to ensure that AI governance laws, regulations, and policies are well-informed, well-rounded, and inclusive.
Understanding AI Governance
Traditional Legal Systems and AI Governance
In many jurisdictions, AI technologies are primarily governed in a top-down governance structure by existing legal frameworks that address related issues, such as privacy, data protection, intellectual property, and liability. These legal frameworks provide a foundation for addressing AI-related challenges, but they may not be fully equipped to handle the challenges posed by the ever-evolving nature of AI. As AI systems become more sophisticated and autonomous, the traditional legal system faces several challenges in effectively governing AI.
Firstly, the pace of technological advancement often outpaces the ability of the legal system to keep up. AI technologies evolve rapidly, and traditional legal systems may struggle to address novel issues that arise from AI development and deployment. Legislators and policymakers find it challenging to craft regulations that are both relevant and effective, without stifling AI innovation or overlooking potential risks and harms.
Secondly, understanding AI technologies requires specialized technical expertise. However, many legal professionals and policymakers may lack the necessary technical knowledge to navigate the intricacies of AI systems effectively. This knowledge gap can impede the development of nuanced regulations and policies that comprehensively address AI's intricacies, including issues related to bias, transparency, and accountability.
Further, determining liability and responsibility for AI systems presents a complex challenge. In traditional legal systems, establishing accountability is often straightforward where human actors are involved. However, most AI systems operate autonomously, making it difficult to attribute responsibility when these systems cause harm or make erroneous decisions. The question of whether AI developers, users, or other stakeholders should be held liable remains an ongoing legal debate.
Additionally, traditional legal systems may not explicitly address ethical considerations related to AI technologies. While legal frameworks focus on ensuring compliance with laws and regulations, they may not encompass broader ethical concerns associated with AI, such as fairness, justice, non-discrimination, and human rights. Ethical AI governance demands a deeper understanding of societal values and norms, which can be complex to translate into legal language.
Another area of concern is the ownership and protection of AI-generated outputs such as creative works, inventions, or data analysis. The question of whether AI-generated content can be copyrighted or patented, and to whose benefit, remains a subject of ongoing legal debate.
Finally, global coordination and harmonization of AI governance present significant challenges. AI systems often operate beyond national borders, requiring international cooperation to establish common standards and norms. Achieving consensus among countries with diverse interests, priorities, legal systems, and ethical standards can be a complex and time-consuming process.
Why the top-down Governance Structure is Inadequate for AI Governance
The top-down governance structure refers to a hierarchical approach in which authority and control predominantly emanate from a central authority or a few select individuals. For most regulated industries or professions, this approach involves policymakers, regulators, and industry leaders setting rules and standards for regulation and governance. While this model might seem efficient in terms of centralized control, it has several inherent flaws that make it unsuitable for addressing the complex challenges posed by AI.
One of the key reasons why the top-down governance structure is not suitable for AI governance is the complexity and diversity of AI applications. AI technologies span various sectors, each with distinct needs and risks. Centralized decision-making may not account for the divergent and nuanced requirements of each domain and may lead to overregulation or inadequate oversight.
The top-down governance structure tends to be rigid and slow to adapt to dynamic and rapidly evolving industries. Top-down governance typically entails lengthy bureaucratic processes which make it difficult to keep up with the swift pace of AI innovation. By the time laws or regulations are implemented, they may already be outdated, leaving gaps in addressing emerging risks or failing to harness new opportunities.
Decisions made by a limited group of experts or policymakers in a top-down governance structure may not consider the diverse perspectives of stakeholders, including researchers, developers, ethicists, and the public. This approach may sideline valuable input from these diverse parties, resulting in decisions that do not reflect the broader interests and perspectives of society. Additionally, the concentration of power in a few hands raises concerns about potential biases and a lack of transparency. Decisions made by a limited group of individuals could be influenced by their own interests or limited understanding of the societal implications of AI technologies.
AI governance requires agility and adaptability to address the unique challenges that arise in different domains and contexts. A top-down approach might lead to one-size-fits-all regulations which may not effectively address the specific needs and concerns of different domains or communities. To ensure the responsible and equitable development of AI, a more inclusive and collaborative governance model is essential. Engaging a diverse range of stakeholders is crucial for developing comprehensive and balanced AI policies that promote innovation while safeguarding ethical considerations.
The Importance of Public Engagement and Participation
Public engagement and participation assist policymakers to cover the diverse concerns of AI governance more comprehensively. The diversity of the information collected when engaging those directly affected by AI systems provides policymakers with more information on the matter as well as practical approaches to solving any issue.
Recent studies have shown that when AI systems are not at par with the values of the individuals and communities who use them, they easily fail to solve the problems that they were designed to tackle. Multidisciplinary input is important as it ensures nuanced coverage of all areas of AI that require regulation as well as providing information on the values of the communities that the AI systems are meant to serve. For instance, an AI specialist is able to provide input on the issue of transparency and accountability and the extent to which transparency should be regulated in order to ensure the protection of the Intellectual Property rights of the owner of an AI system.
It is important to ensure that there is public participation when implementing policies and regulations targeted against AI technology biases. Public participation provides an open platform for people of all genders, races, tribes, realities of life, ethnicities, and more, to come forward and provide diverse viewpoints. This reduces the chances of bias against certain groups of people due to a lack of representation and information. It ensures the implementation of laws and regulations that promote the creation of AI technologies whose output is equal and fair to all persons, and devoid of discrimination and bias.
Public engagement and participation in AI governance will enhance the confidence of the AI sector in policymakers hence building a trust relationship. This is because those in the AI sector will be provided with a platform to share their views and issues and when the said issues are solved to the best of their interest, they will have good reason to trust the policymakers. This will in turn result in more adherence to the laws and regulations that shall be implemented.
Challenges and Limitations
Private sector actors currently dominate the AI industry. The public sector, which is a majority stakeholder in AI, plays a very minor role in AI governance and decision-making as private tech firms take up significant control through various means such as lobbying. These tech firms make decisions based on what is popular in the market and what is more profitable to them. This is a challenge as what is popular or most profitable may not always be what is best for the public at large. Most times, the most profitable or most popular routes may result in exploitation and infringement of human rights such as the rights to privacy and equality.
The lack of a full understanding of AI and AI development limits the public’s ability to be critical of AI governance. Actors in the AI industry may use this as an opportunity to exploit those who are not well-informed to make decisions that are profitable to a select few at the expense of the rights and freedoms of the majority.
The lack of knowledge of AI and AI development may also discourage the public from actively participating in AI governance. This may result in decisions being made that may be biased against certain persons or that may be detrimental to the majority due to lack of proper information and representation.
AI is dynamic and evolving very fast. It is therefore quite difficult to cover every possible angle of AI in governance. This may lead to a number of loopholes or ineffective laws being enacted.
There is a need for the use of proactive deliberative and consultative mechanisms to balance out the interests of private sector actors with those of the public sector, AI developers, and other stakeholders regarding AI governance. This will reduce the risk of the AI governance processes being dominated by actors who may not pursue the public good but only their own personal interests. Legitimate democracy and effective management of AI governance need to be effected to ensure the representation of all the people in AI governance.
There is also a pressing need for an increase in literacy programs and public information regarding AI and AI governance. These programs should tackle the basics of AI and AI governance to educate members of the public so that they can understand the importance of AI governance, their participation in the same, and what AI governance entails. This will increase the number of participants in AI governance hence reaching the main goal of public participation which is inclusivity.
AI developers and policymakers, while taking into consideration the rights and freedoms of the public, AI stakeholders, and AI business owners, should engage together in multidisciplinary panels to create laws and regulations that account for the constant growth and evolution of AI without stifling AI innovation or overlooking potential risks and harms. With the help of AI professionals, policymakers will be able to formulate relevant and effective laws that will encompass the growth of the industry without the risk of the laws quickly becoming outdated.
A hybrid system of AI governance is an alternative to top-down governance. This is a system that would incorporate the most desirable aspects of top-down governance together with a middle-out approach that ensures adaptability, speed, and multidisciplinary engagement in AI governance. Where this approach is being used, there is a collection of higher quality data input as all relevant actors in the AI industry are given an opportunity to share their knowledge and strategies that will ensure the enactment of well-informed, well-rounded, and inclusive laws. This reduces the chances of biased laws and regulations. The diversity of persons involved in policymaking also enhances the equal representation of all persons.