With Artificial Intelligence (AI) becoming more pervasive within different industries, its transformational power arrives with considerable security threats. AI is moving faster than policy, whereas the lightning-quick deployment of AI technologies has outpaced the creation of broad regulatory frameworks, raising questions about data privacy, ethical implications, and cybersecurity. This gap is driving regulators to intervene with guidance in creating standards that reduce the risks.
A report by the World Economic Forum suggests that best-practice guidelines are necessary for maintaining systematic transparency, accountability, and social alignment in the design of AI systems. It is reasonable to assume that regulators will ultimately shape the use of AI and, more specifically, the strategies needed to reduce security concerns. The overarching goal for the regulators will be to curate a secure and trusted ecosystem of AI. This can be achieved by gauging existing regulatory attempts and suggesting prospective ways forward.
The Importance of Regulatory Oversight in AI
A regulatory eye should always be watching the development and implementation of AI technologies. This should aid future researchers, as AI systems that learn from such data without guidelines can inadvertently perpetuate biases, resulting in unfair outcomes in all industries. It can also have broad impacts on hiring, lending, and law enforcement practices.
Machines all too often perpetuate existing discriminations, and we need mechanisms to make sure it doesn’t happen. Regulations can enforce ethical standards to avoid potential risks and to ensure fairness in the world of AI. Regulatory bodies are at the forefront of protecting this data and the right to privacy of the consumers.
In Europe, regulations such as GDPR require companies to obtain explicit consent from users before collecting personal data. It also provides users with the ability to view, extract, or delete the details upon request. Data breaches and data misuse are difficult to prevent, which is why these compliance regulations aim to protect consumer privacy and security.
The mitigation of bias is important if AI technologies are ever going to be widely accepted. That is where regulatory oversight comes in to ensure that AI is safe, reliable, and responsible for fostering this trust. People are more likely to adopt these technologies when they know that there are regulations that will enforce responsible development and use of AI, which prevents their overuse. Regulators are also expected to implement transparency and accountability standards, which can take the form of explanations of how their algorithms work for companies.
This transparency, in turn, helps to make AI less mysterious, reassuring the public that such technologies are being used responsibly.
Key Regulatory Bodies Involved in AI Governance
Regulatory bodies at international, national, and industry-specific levels are important to the governance of AI. Some of the main organizations involved in this effort include:
International Organizations
1. Organization for Economic Co-operation and Development (OECD)
The OECD set up the AI Principles for providing direction on AI, which is human-centered, innovative, trustworthy, and respects human rights and democratic values. The guidelines serve as a roadmap for this type of policy-making among member countries — to have AI work well for as many people as possible.
2. United Nations (UN)
The UN is at work on developing global AI standards through agencies such as UNESCO. A major guiding principle is to work so that the developments with AI are done in concert with regulations concerning human rights, responsible development, and ethical considerations.
National Regulatory Agencies
3. U.S. Federal Trade Commission (FTC)
The FTC has the mission of protecting the public from deceptive or unfair business practices and from unfair methods of competition. It also partners with other enforcement agencies to implement interagency guidance and agency-specific rules relating to AI.
4. GDPR: The EU General Information Protection Regulation
GDPR is the legislation in effect in the EU. While primarily focused on privacy, It contains parts that are very pertinent to AI, such as in the fields of data collection, processing, user consent, and transparency. A reasonable interpretation of GDPR extends it to ensure that AI systems will respect the privacy and data security of an individual.
5. Financial Industry Regulatory Authority, Inc. (FINRA)
FINRA is described as “a Self-Regulatory Organization (SRO) that oversees broker-dealer firms, registered brokers, and market dealings in the US. Empowered by the Securities and Exchange Commission (SEC), FINRA writes rules that brokers must abide by, evaluates firms’ compliance with those rules, and disciplines brokers that fail to adhere.”
In the financial industry, the use of AI is closely monitored under the watchful eye of FINRA to make sure it adheres to industry standards and regulations, monitors financial fraud through AI systems, and assures that AI is also open and fair.
Industry-Specific Regulatory Bodies
6. Health Level Seven International (HL7)
In the healthcare domain, HL7 is “standards developing organization dedicated to providing a comprehensive framework and related standards for the exchange, integration, sharing, and retrieval of electronic health information that supports clinical practice and the management, delivery and evaluation of health services.” These standards are vital to ensuring the safety, efficacy, and interoperability of AI systems in healthcare.
Non-Regulatory Guidance
7. National Institute of Standards and Technology (NIST)
While not a regulatory body, one of the most respected authorities that issues guidance documents for technology professionals is NIST. These documents are often used as the basis for achieving compliance with regulations and Standards. NIST offers specific information on a variety of topics and currently hosts 2,190 documents specific to information technology and 1,413 specific to cybersecurity.
Going Beyond Regulations and Standards
Beyond technical standards and laws, ethical guidelines are crucial for guiding the responsible use of AI. Various guidance, such as the AI ethics guidelines developed by the European Commission, provides principles for developing and deploying AI systems ethically. These guidelines emphasize transparency, accountability, and fairness, ensuring that AI technologies are used in ways that respect human rights and societal values.
Strategies for Minimizing AI Security Risks
To protect AI systems from cyber threats, it’s crucial to practice basic cybersecurity hygiene, such as using encryption to safeguard data, implementing secure coding practices, and ensuring regular updates and patches to fix vulnerabilities. All security professionals emphasize that organizations adhering to comprehensive security protocols and cybersecurity regulation significantly reduce their risk of data breaches.
Conducting regular audits and compliance checks is essential for identifying and mitigating security risks in all systems. These audits help ensure that the systems comply with industry standards and regulations.
Transparency and accountability are key to building trust in AI technologies. Developers should openly communicate how AI systems are designed and used and who is responsible for their operation. This transparency allows users to understand the potential risks and benefits of AI. At the recent World Economic Forum (WEF) conference, a common theme was that transparent AI practices lead to higher user trust and better risk management.
Challenges and Opportunities for Regulators
Balancing innovation with security: One of the toughest jobs regulators face is finding the right balance between encouraging innovation and simultaneously providing security. At the same time, AI technologies have the potential to deliver great strides and economic development.
Conversely, AI systems also represent serious security vulnerabilities when they are not managed correctly. Regulators, therefore, need to put security versus innovation into place at every level, ensuring that all its frameworks offer data and privacy protection.
AI is developing so fast that it is possible that the regular frameworks for the rules will not keep pace to address the public’s concerns. For example, the rapid acceleration in development can result in a shift between security and ethical standards. To address this, regulators, governments, and other market organizations will need to update guidelines and standards in line with the latest developments in AI. This type of proactive work can prevent problems from arising.
Regulators should work with industry and expert stakeholders to help ensure socially beneficial AI. They can collaborate with each other to gain insights and also create complete strategies that incorporate both innovation and security. This partnership approach also helps to ensure that the AI-related regulatory activity is as practicable, enforceable, and in alignment with actual AI technology development and deployment as possible.
Potential Impact of Effective Regulation
A key aspect in mitigating the risks brought by the implementation of AI is the role of the regulators. Regulatory governance plays a crucial role in the development and deployment of AI — ensuring quality, safety, and transparency in the design, architecture, and delivery of AI services.
Their work strikes a fair equilibrium between innovation and security toward promoting public confidence in AI technologies. As AI technology develops over time, continuous interaction of regulators, developers, and users will be required to face new challenges and opportunities and to maximize the positive impact of AI on society. It is also fair to predict that AI-specific regulatory bodies will become commonplace in this emerging technological frontier.
About the Author:
Micheal Chukwube is an Experienced Digital Marketer, Content Writer, and Tech Enthusiast. He writes informative, research-backed articles about tech, cybersecurity, and information security. He has been published on Techopedia, ReadWrite, HackerNoon, and more.
Editor’s Note: The opinions expressed in this guest author article are solely those of the contributor and do not necessarily reflect those of Tripwire.
