Artificial intelligence (AI) transforms how transactions and social interactions are organized in society. AI systems and their supporting algorithms are increasingly making important decisions in medical diagnoses, crime prediction, and personalized content delivery. These systems can mimic or even surpass human intelligence in solving complex problems, making them unique among technologies.
While AI offers significant benefits, it also brings unexpected risks and challenges that need effective management. AI systems learn from data and programmed rules, which can lead to unforeseen behaviors and safety hazards. Bias in AI data and algorithms can result in unfair outcomes, such as in credit scoring and criminal sentencing. Additionally, the autonomy of AI systems raises concerns about human control and ethical implications in applications like caregiving and military use. Legal responsibility for AI-caused harm is often unclear, and the automation of jobs could lead to significant job displacement.
Governments face the challenge of managing the rapid adoption of AI and its risks. Traditional governance methods may be inadequate due to the fast-paced nature of AI development. Therefore, new regulatory and governance structures are needed. There is a growing recognition of AI governance, with new frameworks and self-regulatory initiatives emerging. Recent developments include new AI strategies from various countries, increased government funding, and industry involvement in AI regulation.
This special issue addresses the governance challenges of AI, exploring emerging approaches, policy building, and legal and regulatory issues. It aims to highlight the current state of AI governance, identify gaps, and suggest future research directions.
Recognizing the Risks of AI:
Scholars have raised concerns about safety issues in AI deployment. A significant challenge is AI’s inability to handle unforeseen ‘corner cases’ not covered during training. For example, a fatal accident involving an Uber self-driving car occurred due to the system failing to recognize a pedestrian crossing the road. Simulations can anticipate some scenarios, but not all. The complexity of machine learning (ML) makes it hard to understand AI decisions, complicating safety.
AI interactions with users can also pose risks due to automation bias, where users trust automated decisions too much. The design of human-machine interfaces is crucial, especially in personal care robots, autonomous vehicles, and service providers.
AI’s decision-making autonomy reduces human control, creating challenges for assigning responsibility and legal liability for AI-caused harm. Current legal frameworks assume human control, but ML processes operate independently. Excessive liability risks could hinder innovation, prompting the need for balanced liability frameworks.
AI systems can also exhibit behaviors conflicting with societal values, raising ethical concerns. Algorithmic bias and discrimination are major issues, as ML can reproduce societal inequalities present in training data. Balancing equity and efficiency in AI decision-making remains a contentious issue.
Data privacy and surveillance are significant concerns with AI. AI systems collect, store, and transmit vast amounts of personal data, raising risks of misuse. Issues include ownership of data, adherence to privacy laws, and potential surveillance.
What is AI governance?
AI governance refers to the rules and standards that ensure AI systems are safe, ethical, and fair. It involves creating frameworks to guide AI development and use, addressing risks like bias, privacy issues, and misuse while promoting innovation and trust.
Since AI can reflect human biases, governance helps monitor and update algorithms to prevent harmful decisions. Involving developers, users, policymakers, and ethicists ensures AI aligns with societal values.
Ultimately, AI governance aims to align AI behavior with ethical standards and societal expectations, providing oversight to prevent negative impacts.
Why AI governance is needed:
I governance is crucial when machine learning algorithms make decisions. Biases in these algorithms, such as racial profiling, can lead to incorrect and unfair outcomes, like denying healthcare or loans and misidentifying criminal suspects. Governance addresses how to manage these unjust decisions and protect human rights.
As AI rapidly expands across industries, concerns about ethics, transparency, and compliance with regulations like GDPR grow. Without proper governance, AI systems risk biased decisions, privacy violations, and data misuse. AI governance ensures AI technologies are used constructively, protecting user rights and preventing harm.
Examples of AI governance:
Examples of AI governance include various policies, frameworks, and practices that organizations and governments use to ensure responsible AI use. Here are a few key examples:
- General Data Protection Regulation (GDPR): While not solely focused on AI, the GDPR is crucial for AI systems handling personal data in the EU. It emphasizes data protection and privacy, which are vital for AI governance.
- OECD AI Principles: Adopted by over 40 countries, these principles promote responsible AI usage, emphasizing transparency, fairness, and accountability.
- Corporate AI Ethics Boards: Many companies have ethics boards to oversee AI projects, ensuring they adhere to ethical standards. For instance, IBM’s AI Ethics Council reviews new AI products to ensure they align with the company’s principles. These boards typically include members from legal, technical, and policy backgrounds.
The future of AI governance:
The future of AI governance relies on collaboration between governments, organizations, and stakeholders. Success in this area requires developing clear AI policies and regulations that protect the public while fostering innovation. Key aspects include complying with data governance rules, and prioritizing safety, trustworthiness, and transparency.
Many companies are working on AI governance. In 2022, Microsoft released the “Responsible AI Standard,” a guide for managing AI risks ethically. Other companies like Amazon, Google, IBM, Meta, and OpenAI are also committed to implementing governance standards.
The U.S. government is actively involved, with the White House’s National AI Initiative Office and the National AI Advisory Committee focusing on AI issues. The National Institute of Standards and Technology has developed a framework for managing AI risks.
Despite these efforts, some experts believe there’s a gap in AI accountability and integrity. In March 2023, tech leaders like Elon Musk and Steve Wozniak called for a temporary halt to AI research and the establishment of legal regulations. In May, OpenAI’s CEO, Sam Altman, testified before Congress, advocating for AI regulation.
Recent actions include President Biden’s executive order for safe AI development and the World Economic Forum’s AI Governance Summit, which aimed to promote responsible AI globally.
AI regulation can increase complexity and costs for businesses, but understanding and adapting to these changes can help manage their impact. AI governance is crucial for responsible AI use in both private and public sectors.
