Emergence of AI and its associated risks

Artificial intelligence (AI) is the ability of machines to perform tasks that normally require human intelligence, such as reasoning, learning, decision-making, and creativity. AI has been advancing rapidly in recent years, thanks to the availability of large amounts of data, powerful computing resources, and breakthroughs in algorithms and techniques. AI applications are now ubiquitous in various domains, such as health care, education, entertainment, finance, security, and more.

However, AI also poses significant challenges and risks that need to be addressed and mitigated. Some of these risks include:

- Ethical and social issues: AI systems may not align with human values and norms, or may discriminate against certain groups or individuals based on their data or biases. AI systems may also affect human dignity, autonomy, privacy, and agency. For example, facial recognition technology may be used for surveillance or profiling purposes, or autonomous weapons may cause harm or death without human oversight or accountability.

- Economic and labour issues: AI systems may disrupt the existing economic and labour structures, creating winners and losers in terms of income, employment, and skills. AI systems may also create new forms of inequality and exploitation, such as the digital divide, digital colonialism, or digital slavery. For example, automation may replace human workers in various sectors, or online platforms may extract data and value from users without fair compensation or consent.

- Security and safety issues: AI systems may be vulnerable to attacks, errors, or failures that could compromise their functionality, reliability, or integrity. AI systems may also pose threats to human or environmental security and safety, either intentionally or unintentionally. For example, cyberattacks may target AI systems to cause damage or disruption, or AI systems may cause accidents or harm due to bugs or unforeseen consequences.

These risks are not inevitable or insurmountable, but they require careful consideration and action from various stakeholders, such as researchers, developers, policymakers, regulators, users, and the public. Some of the possible ways to address and mitigate these risks include:

How can we mitigate the risks of AI?

- Developing ethical principles and guidelines for AI design and use: One of the ways to mitigate the risks of AI is to develop ethical principles and guidelines that can guide the design and use of AI systems. These principles and guidelines should reflect the values and norms of the society and the context in which AI is deployed. They should also address the potential impacts and implications of AI on human dignity, autonomy, privacy, agency, justice, fairness, diversity, inclusion, and well-being. Some examples of ethical principles and guidelines for AI are the Asilomar AI Principles, the IEEE Ethically Aligned Design, the EU Ethics Guidelines for Trustworthy AI, and the UNI Global Union Principles on Ethical AI.

- Establishing legal frameworks and standards for AI accountability and governance: Another way to mitigate the risks of AI is to establish legal frameworks and standards that can ensure the accountability and governance of AI systems. These frameworks and standards should define the roles and responsibilities of the actors involved in the development and use of AI systems. They should also specify the mechanisms and procedures for monitoring, auditing, reporting, redressing, and enforcing the compliance of AI systems with ethical principles, guidelines, laws, regulations, and human rights. Some examples of legal frameworks and standards for AI are the Council of Europe Convention on Artificial Intelligence, the OECD Principles on Artificial Intelligence, the EU Regulation on Artificial Intelligence, and the UN Human Rights Council Resolution on New Technologies.

- Implementing technical solutions for AI transparency, explainability, fairness, and robustness: A third way to mitigate the risks of AI is to implement technical solutions that can enhance the transparency, explainability, fairness, and robustness of AI systems. These solutions should enable the users and stakeholders of AI systems to understand how they work, why they make certain decisions or actions, whether they are fair or biased towards certain groups or individuals, and how they can be corrected or improved if they make mistakes or cause harm. Some examples of technical solutions for AI are the IBM Explainable AI Toolkit, the Google Responsible AI Practices, the Microsoft Fairlearn Toolkit, and the Partnership on AI About ML Project.

- Enhancing education and awareness of AI benefits and challenges: A fourth way to mitigate the risks of AI is to enhance education and awareness of the benefits and challenges of AI among various audiences. These audiences include researchers,

Developers, policymakers, regulators, users, and the public. These audiences should be informed about the potential opportunities and risks of AI, the ethical principles and guidelines for AI, the legal frameworks and standards for AI, and the technical solutions for AI. They should also be empowered to participate in the design, Use, and governance of AI, and to exercise their rights and responsibilities.

Some examples of education and awareness initiatives on AI are the UNESCO Global Dialogue on Ethics of Artificial Intelligence, the World Economic Forum Centre for the Fourth Industrial Revolution, the MIT Media Lab Center for Civic Media, and the Mozilla Foundation Responsible Computer Science Challenge.

- Fostering collaboration and dialogue among diverse actors and perspectives: A fifth way to mitigate the risks of AI is to foster collaboration and dialogue among diverse actors and perspectives involved in or affected by AI. These actors and perspectives include researchers, developers, policymakers, regulators, users, and the public from different disciplines, sectors, regions, cultures, and backgrounds. These actors

and perspectives should engage in constructive and inclusive dialogue to share their knowledge, experiences, values, and concerns about AI. They should also collaborate to co-create and co-implement solutions that can address the challenges and opportunities of AI. Some examples of collaboration and dialogue platforms on AI are the Global Partnership on Artificial Intelligence, the Partnership on AI, the AI for Good Global Summit, and the AI Commons.

AI is a powerful and transformative technology that can bring many opportunities and benefits to humanity. However, it also comes with significant responsibilities and risks that need to be handled with caution and care. By adopting a proactive and holistic approach to AI ethics and governance, we can ensure that AI serves the common good and respects human rights and dignity.