The journey from the first Industrial Revolution to the present has been a saga of disruptive innovations, shaping our world through a series of purposeful and irreversible advancements. We now find ourselves in the midst of the fourth industrial revolution, characterized by a remarkable array of breakthroughs like genetic engineering, novel material sciences, innovative energy solutions, the expansion of internet technology, and most notably, artificial intelligence (AI). This era marks a significant phase in human technological evolution, an epoch that could be termed as the Axis Era of technological advancement.
As society increasingly embraces the empowerment offered by these emerging technologies, there’s a simultaneous and conscious effort to construct a governance framework around them. This is a crucial step to avert the pitfalls of excessive reliance on technology, a concept philosopher Martin Heidegger described as ‘technological Gestell’ or fetishism. In this dynamic landscape, there’s a collective vigilance to balance the interplay between technology, human values, nature, and societal norms.
AI is emblematic of these transformative technologies. From its conceptualization in the 1950s to its current widespread application, AI’s journey has been fueled by advancements in computing power, the proliferation of big data, and algorithmic breakthroughs. Today, AI is integral to the production of knowledge, technological development, and the creation of new products. It stands as the cornerstone of digital and economic transformation, driven by deep learning technologies and a comprehensive computing infrastructure. This evolution paves the way for a smarter, more interconnected future.
The expansion of AI is distinguished by its profound integration, high complexity, and capacity for technological breakthroughs. It fosters a convergence of various elements, enhances cross-domain interactions, and facilitates the fusion of multiple states of being. However, the progress in AI research and application also challenges the existing ethical and moral order. It necessitates walking a fine line between the benefits AI offers and the ethical risks it entails. AI’s unique attributes, such as its often opaque core technology, human-like forms, potential for cross-domain application, complex stakeholder interests, multi-dimensional risks, and wide-ranging social impacts, give rise to various ethical concerns. These include issues related to privacy, discrimination, technological dominance, the digital divide, echo chambers, and the Matthew effect.
Historically, the discourse on these ethical challenges has been led by technologically advanced countries. These nations have been the first to encounter such challenges and have thus initiated various ethical frameworks, declarations, and regulations. However, with the progress made by countries like China and other emerging economies, the ethical implications of new technologies are now being recognized and addressed by a broader segment of the global community. It’s imperative for countries previously on the sidelines of these discussions, such as China, to actively participate and contribute constructively to the global dialogue on AI ethics and governance.
In this blog, we aim to navigate these complex waters by providing a comprehensive look at the ethical governance of AI. We will explore the various facets of AI’s impact on society, delve into specific applications such as autonomous vehicles, and reflect on the broader implications of AI’s integration into our daily lives. Through this exploration, we seek to understand and articulate practical pathways for the ethical and responsible use of AI in our rapidly evolving world.
Ethical Governance in the Realm of Emerging Technologies
The evolution of ethical governance in the context of emerging technologies has followed a distinct and progressive path. In the 1960s, new technologies like nuclear energy and chemical industries led to significant environmental concerns. These challenges prompted governments to develop regulatory policies to ensure the ethical deployment of such technologies. The main strategy during this period was technology assessment, an approach that emphasized evaluating the impacts of technological development.
The decision-making model that dominated this era relied heavily on experts—both political and technical. These experts, with their combined policy experience and technical knowledge, were responsible for making institutional arrangements and choosing governance tools based on their predictions of technology’s trajectory.
By the 1980s, advancements in genetics shifted the discussion towards the ethical uncertainties associated with artificial life. This period was marked by technological disasters, such as the European mad cow disease crisis and the Chernobyl Nuclear Power Plant accident, which eroded public trust in institutions’ ability to make sound technological decisions. Consequently, the previous governance model based on regulation and assessment began to wane. In its place emerged the precautionary approach, emphasizing caution in the face of ethical dilemmas and knowledge deficits in emerging technologies.
The commercialization of genetically modified crops in this era brought the precautionary principle to the forefront of governance discussions. The 1990s then saw a shift towards considering the broader ethical, social, and economic implications of technology, particularly highlighted by the Human Genome Project. This led to the development of the Ethical, Legal, and Social Implication (ELSI) model, which called for incorporating broader ethical values and socio-economic considerations into technology governance.
The dawn of the 21st century brought breakthroughs in nanotechnology, signaling the onset of the fourth technological revolution. This era introduced the concept of anticipatory governance, which integrated social values, ethics, and public preferences into the scientific research process. The goal was to shape technologies from their inception to ensure they were ethically sound.
However, as nanotechnology didn’t lead to the expected industrial revolution, focus shifted towards the governance and innovation of emerging technologies. This shift gave rise to the concept of Responsible Research and Innovation (RRI), which became a mainstream paradigm in the European Union’s 2020 Framework Program. RRI represented a paradigm shift from traditional risk-based governance to one focused on shaping the responsibilities of scientific researchers. It also emphasized the need for science and technology innovation, institutional responses to innovation, the redefinition of public responsibility in scientific development, and the establishment of public participation in science.
Ethical Governance of AI: A Paradigm Shift Towards Responsible Technology
The progression of ethical governance in emerging technologies lays a foundational understanding for the ethical governance of artificial intelligence (AI). Present studies on AI ethical governance concentrate on three primary areas: conceptual understanding, framework development, and subject focus.
Conceptualizing AI Ethical Governance
At the conceptual level, diverse perspectives shape AI’s development and application through defining core concepts, objectives, and values. While the AI concept remains a topic of debate, it hasn’t impeded AI’s application from a governance standpoint. Key concepts shaping the human-technology relationship include “Beneficial AI,” “Ethical AI,” as per the UK House of Lords in 2017, “Trustworthy AI” as outlined by the OECD in 2019, and “Responsible AI,” proposed by China’s New Generation AI Governance Committee. These ideas help stakeholders contemplate AI’s future direction while guiding ethical governance through their interpretations and requirements for products and behavior.
Frameworks for AI Governance
At the framework level, research seeks to localize and modularize AI’s ethical concerns, building upon the innovations and expansions of existing governance theories. This involves embedding AI ethical issues within current theoretical frameworks or adapting elements from these theories to the AI ethical governance domain. The objective is to create pathways for addressing ethical challenges in AI’s emergence, application, and development. Key focus areas include accountability and explainable AI, addressing responsibility issues; discrimination in data mining, focusing on fairness; and privacy by design, emphasizing data privacy.
Focusing on the Subject Matter
Subject-level research employs practical rationality to define the scope, tools, and paths in AI ethical governance. It primarily examines the responsibility and power distribution among different stakeholders, encompassing technical, organizational, and policy aspects of AI ethical governance.
Evolving Governance Mechanisms
Recently, discussions on governance mechanisms and frameworks for emerging technologies have birthed more comprehensive theoretical models. These models delve into the power dynamics among various actors like government regulators, enterprises, and the public in regulating emerging technologies. The adoption of tentative and adaptive governance models reflects a consideration of the dynamic, uncertain, and innovative nature of these technologies. This includes understanding the balance of constraints and incentives between regulators and the regulated, leveraging frontline regulators’ discretion for more effective self-regulation.
From Scientific Rationality to Social Rationality
The evolution of ethical governance in emerging technology signifies a shift from a purely scientific rationality to a more socially conscious rationality, emphasizing ethics and morality. This shift provides a rich theoretical foundation for AI’s ethical governance in today’s smart era. Although current research outlines a systematic approach to AI ethical governance, it often lacks a deep exploration of AI’s ethical governance complexities, including empirical data analysis, model training, application evaluation, and feedback.
Bridging the Gaps
Existing studies have not thoroughly examined AI’s ethical issues across its entire life cycle, from R&D to application, nor have they proposed adaptive governance solutions or clearly defined the roles of different actors in AI’s ethical governance. This blog seeks to bridge these gaps by constructing an integrated framework for AI governance. This framework will address the recognition of problems, application scenarios, and role configurations, and clarify practical pathways for AI’s ethical governance, using autonomous driving as a key exploratory case.
Crafting a Framework for Ethical Governance of AI
The pursuit of ethical governance in the realm of artificial intelligence (AI) is informed by an array of global initiatives. Over 70 programs worldwide, led by various national and regional governments, intergovernmental organizations, research institutions, non-profits, scientific societies, and corporations, have proposed principles for AI’s ethical deployment. These principles revolve around ten central themes: human-centric design, collaborative development, equitable access, fairness, transparency, privacy protection, both internal and external security, accountability, and sustainable long-term applications.
A landmark in these efforts was UNESCO’s release of a Recommendation on the Ethics of Artificial Intelligence on November 24, 2021. This document, the first of its kind to provide a global normative framework, outlines ten principles and eleven action areas for regulating AI technologies. It emphasizes that AI development and application should honor four key values: the respect, protection, and enhancement of human rights and dignity; the promotion of environmental and ecosystem health; the encouragement of diversity, inclusion, and equity in workplaces; and the fostering of a peaceful, just, and interdependent human society.
However, the challenge lies in translating these high-level ethical principles into tangible practice. This requires a systematic approach that involves problem identification, solution pathway selection, and role assignment to various stakeholders.
Problem Identification
Fremont E. Kast’s systems theory offers a comprehensive lens to analyze management issues, suggesting that all problems stem from goal-oriented systems, socio-psychological systems, activity structure systems, and technical systems. In the context of AI, ethical governance should encompass four main systems:
- The Technology System: This focuses on controlling the risks associated with AI technology, including the knowledge, artifacts, products, hardware, software, and services it encompasses.
- The Value System: This system assesses the moral rationality and value orientation of people toward technology, ensuring AI doesn’t disrupt the balance between technology, people, society, and nature.
- The Innovation System: Here, the focus is on the socialization of AI technology, emphasizing the restraint of innovative behaviors and the integration of ethical responsibility within technological advancements.
- The Order System: This involves the distribution of rights, powers, interests, and responsibilities within a technical framework, aiming to maintain stable social order amid the impact of AI applications.
In the technological system, ethical problems of AI manifest as risks that are difficult to predict, explain, calculate, evaluate, and control. Biases in data and the black-box nature of algorithms further exacerbate these challenges. In the value system, the ethical problem is relational, reflecting a disorder in the ethical relationship between humans and technology, as well as between technology and society. The innovation system’s ethical challenge lies in balancing the instrumental pursuit of AI advancements with moral and ethical considerations. Lastly, in the order system, AI’s ethical problem is derivative, leading to a reconfiguration of rights, power, interests, and responsibilities, which can affect social justice and harmony.
Path Selection
The path to ethical AI involves embedding ethical values at every stage of its lifecycle:
- Research and Development Phase: Integrating AI technologies with ethical norms, including stakeholder analysis, risk clarification, and the alignment of AI development with specific value norms.
- Design and Manufacturing Phase: Implementing an ethical assessment oriented toward the future of the technology, anticipating potential risks, and incorporating societal functioning into the design process.
- Experimental Promotion Phase: Ensuring AI systems, products, or services align with social systems and values, and adapting them proactively through regulations and laws to uphold fairness and justice.
- Deployment and Application Phase: This stage involves the large-scale implementation of AI technologies in various environments, focusing on ethical construction, user acceptance, and compliance with moral values.
Role Configuration
Effective AI ethical governance requires the involvement of multiple stakeholders, including technical experts, business professionals, social sector representatives, scholars, government officials, and the public. These actors should assume different responsibilities and form a coalition for ethical governance. Their roles span providing factual information, ethical expertise, analytical ideas, regulatory tools, and expressing the general public’s ethical concerns.
In conclusion, the ethical governance of AI demands a collaborative and multidisciplinary approach, where various stakeholders contribute to crafting a framework that ensures AI’s development and application are guided by ethical principles and societal values. This collaborative effort aims to navigate the complexities of AI ethics, ensuring its benefits are harnessed responsibly and equitably.
Conclusion
As we stand at the crossroads of technological advancement and ethical responsibility, the governance of artificial intelligence (AI) presents a unique and critical challenge. The journey towards ethical AI governance is not just a regulatory path but a collective mission that intertwines technology with societal values and moral considerations. The frameworks and principles discussed in this article underscore the importance of a holistic and collaborative approach, one that transcends national boundaries and sectoral interests.
The diversity of perspectives, from governmental bodies to private entities and the general public, enriches the dialogue and ensures that the governance of AI is not just about mitigating risks but also about enhancing the positive impacts of AI on society. As we navigate through the complexities of AI ethics, it is crucial to remember that the decisions we make today will shape the future of AI and, in turn, the future of humanity.
Our collective efforts in establishing robust ethical governance frameworks for AI are a testament to our commitment to responsible innovation. By embedding ethical considerations into every stage of AI’s development and application, we are not only ensuring the technology’s alignment with human values but also fostering an environment where AI can thrive as a force for good.
In conclusion, the ethical governance of AI is an ongoing and dynamic process, one that requires continuous engagement, adaptation, and vigilance. It is an opportunity for us to harness the power of AI in a way that respects human dignity, promotes societal well-being, and upholds the principles of justice and equity. As we embark on this journey, let us remain steadfast in our commitment to steering AI towards a future that benefits all of humanity.