Risks of Generative AI: Potential Risks and Countermeasures by Countries and Companies

1. Introduction

What is Generative AI? What is it and why does it matter?

Generative AI is a branch of artificial intelligence that has the ability to generate data. Typically, generative AI uses deep learning and neural networks to generate new data based on input data. Typical examples include text generation, image generation, and voice generation. Generative AI is particularly popular in the creative field, where it complements human creativity in writing sentences, composing music, and creating paintings.

The importance of generative AI lies in its wide range of applications. Generative AI continues to provide innovative solutions in a variety of fields, including business, entertainment, medicine, and education. However, generative AI also comes with many risks. It is necessary to understand these risks and take appropriate measures.

Purpose and Overview of this Article

The purpose of this article is to provide a detailed explanation of the risks posed by generative AI and to introduce the countermeasures that countries and companies are taking. We clarify the technical, social, and legal risks of generative AI and explore how to manage and mitigate these risks through actual countermeasure examples.

Specifically, we will cover the following:

1. Generative AI Risk Overview: Classifies the risks of generative AI and describes the characteristics and impacts of each.
2. Technological Risks: Introduces specific technological risks such as deepfakes and data privacy violations.
3. Social risks: Explains the risks to society as a whole, such as the spread of false information and the impact on employment.
4. Legal Risks: Consider risks from a legal perspective, such as copyright infringement and regulatory uncertainty.
5. Measures taken by each country and company: We will introduce the measures taken in the United States, Europe, and Japan, as well as specific initiatives undertaken by companies.
6. Assessing and Managing Generative AI Risks: Describes methodologies for risk assessment and best practices for risk management.
7. The Future of Generative AI and the Importance of Risk Management: We will consider the evolution and future prospects of generative AI, as well as the importance of risk management for sustainable technological development.

Overview of Generative AI Risks

Risk classification and characteristics

The risks of generative AI are many and varied, but can be broadly categorized into three main categories:

1. Technical risks:

• Deepfakes: Generative AI technology could be used to create realistic fake videos and audio, which could undermine the trust of individuals and organisations.
• Violation of data privacy: The data used by generative AI may violate privacy. For example, training a model with a dataset that contains personal information poses the risk of personal information being leaked.
• Model Bias: AI models may inherit biases based on their training data and produce discriminatory results, which poses the risk of compromising fairness.

1. Social risks:

• Spread of disinformation: There is a risk that disinformation created by generative AI will spread on social media and news sites, causing social unrest.
• Impact on employment: There is a risk that automation will result in many jobs being replaced by AI, leading to higher unemployment and a redefinition of occupations.
• Ethical concerns: There is a risk that AI decisions may not be ethically appropriate or that AI may act in a way that goes against human values and morals.

1. Legal Risks:

• Copyright and Intellectual Property Infringement: There is a risk that generative AI will imitate or plagiarize existing copyrighted work, which could lead to copyright infringement and intellectual property issues.
• Regulatory uncertainty: With AI regulations differing from country to country and legal frameworks still underdeveloped, there is a risk that companies will have difficulty in ensuring proper compliance.
• Challenges in international regulations and standardization: As AI technology is used globally, differences in regulations and standardization between countries will become an issue.

The risks and impacts of generative AI

The risks of generative AI have a number of implications:

1. Damage to personal and organizational credibility:

• Deepfakes can discredit individuals and organisations, which can be particularly problematic for public figures such as politicians and business executives.

1. Privacy violations and leaks of personal information:

• If the data handled by generative AI contains personal information, there is a risk that the leak of this information could violate personal privacy. For example, individuals could be harmed if their medical or financial data is leaked.

1. Social unrest and growing distrust:

• The spread of false information causes social unrest and increases distrust of reliable information sources, raising concerns about a decline in information literacy throughout society.

1. Unemployment and Economic Impact:

• As automation by AI advances, many jobs may disappear and unemployment rates may rise, which could lead to a widening of economic inequality.

1. Legal issues and litigation risks:

• Copyright infringement and other intellectual property issues increase the risk of legal disputes and litigation, and companies need to take measures to prevent this.

Technical risks

Deepfakes and their misuse

Deepfakes are fake video or audio created using generative AI techniques. They can be so realistic that it can be hard to tell the difference between real and fake. The main risks of deepfakes are:

1. Spreading disinformation: Deepfakes can be used to spread false information. For example, fake statements or actions of politicians or celebrities can be created and spread on social media to cause misunderstanding and confusion.
2. Damage to individuals’ reputations: Deepfakes can be used to harm the reputation and credibility of individuals. For example, creating fake scandalous footage poses the risk of damaging a person’s social credibility.
3. Security risks: Deepfakes could be used to defeat authentication and security systems, for example by creating fake images of faces to fool facial recognition systems.

Data privacy violations

Generative AI uses large amounts of data to train its models. If this data contains personal information, there is a risk of privacy violations. Specifically, the following issues may occur:

1. Personal information leakage: If the training data contains personal information, the generation AI may generate it incorrectly. This poses the risk of violating personal privacy.
2. Data misuse: Collected data may be misused. For example, personal purchasing history or health information may be provided to third parties without permission.
3. Data Bias: If the training data contains bias, the generative AI model may inherit that bias and produce unfair outcomes, which puts certain groups at a disadvantage.

Model bias and fairness issues

When AI models inherit biases based on their training data, they run the risk of producing unfair outcomes, especially in socially sensitive areas. Specifically, these risks include:

1. Discriminatory outcomes: If the training data contains biases in race, gender, age, etc., generative AI may reflect this and produce discriminatory outcomes. For example, a job interview screening system may unfairly exclude certain races or genders.
2. Lack of transparency: If it is not clear how an AI model makes its decisions, people will have less confidence in the results. This is especially problematic for important decisions like legal decisions or medical diagnoses.
3. Difficulty of correction: Correcting a model once bias has been built in can be technically difficult, especially in complex neural network models.

Social risks

The spread of false information and the decline of credibility

One of the most serious societal risks posed by generative AI is the spread of disinformation. Advances in deepfake technology mean that fake video and audio that are indistinguishable from reality can be easily created and spread far and wide. This could have the following effects:

1. Social unrest: Disinformation can spread quickly and cause social unrest. For example, fake news footage can stoke political unrest or affect financial markets.
2. Decreased credibility: The rise of disinformation reduces trust in media and information sources. This makes it harder to distinguish true information and risks undermining public trust.
3. Individual Impact: Individuals can be targeted. Fake video or audio can be used to discredit or tarnish an individual, causing harm.

Employment impacts and automation risks

With the advancement of generative AI and automation technologies, there is a risk that certain jobs and tasks will be replaced by AI. This could have the following impacts:

1. Increased unemployment: As automation advances, certain jobs will become unnecessary, which could lead to an increase in unemployment. For example, repetitive tasks in the manufacturing and service industries are easily replaced by AI and robots.
2. Skills mismatch: The introduction of new technologies will make old skills obsolete and create a demand for new skills, which creates a risk of creating a skills mismatch in the labour market.
3. Rising economic inequality: Automation could lead to rising economic inequality between businesses and individuals who benefit from automation and those who do not, particularly as the income gap widens between high-skilled and low-skilled workers.

Ethical issues and ethical concerns

The use of generative AI also raises ethical concerns, including issues such as:

1. Privacy violation: Content created by generative AI using personal data may pose a risk of violating privacy. For example, it may be problematic to create fake content using an individual’s face image or voice without their permission.
2. Lack of ethical judgment: AI cannot make ethical judgments. Therefore, the content and actions generated by AI may be ethically questionable. For example, AI may generate content that contains discriminatory content.
3. Unclear responsibility: There may be a lack of clarity about who is responsible for the content and actions created by generative AI. This creates the risk that it will be unclear who is responsible when something goes wrong.

Legal risks

Copyright and Intellectual Property Infringement

The risk that generative AI will imitate or plagiarize existing works leads to copyright and intellectual property infringement, which is particularly prevalent in creative fields.

1. Copyright Infringement:

• Clarification: Generative AI often generates new content based on copyrighted works used as training data. If this resembles copyrighted works, it may be copyright infringement.
• Example: For example, if AI-generated music is very similar to existing songs, there is a risk of copyright infringement lawsuits. This problem is already a reality in the music industry, and legal action is being called for.

1. Intellectual Property Rights Infringement:

• Description: AI-generated content may infringe others’ patents or trademarks. In particular, there are concerns about infringement of intellectual property rights if technical ideas or designs are imitated by generative AI.
• Case Study: If a product design using patented technology is generated by AI, it may be considered patent infringement if the design is similar to an existing patent.

Regulatory uncertainty and compliance

Regulations regarding the use of generative AI vary by country and region, and often lack a legal framework. This regulatory uncertainty makes it difficult for companies to ensure proper compliance.

1. Regulatory Uncertainty:

• Description: AI regulations are changing rapidly, making it difficult for companies to keep up with the latest regulations. In particular, in the absence of specific laws regarding generative AI, it is unclear how to respond.
• Impact: Regulatory uncertainty may make it harder for companies to predict legal risks and, as a result, may find themselves in legal trouble.

1. Compliance difficulties:

• Description: Due to the different regulations in each country, it is difficult for companies operating globally to comply with all the laws and regulations in each region. In addition, companies need to review their compliance system every time a new regulation is introduced.
• Countermeasures: It is important to appoint a legal department or compliance officer and regularly audit the status of compliance with laws and regulations.

International regulations and standardization challenges

As generative AI technology is used globally, differences in regulations and standardization between countries will pose challenges.

1. International regulatory differences:

• Description: Companies must navigate a complex legal environment when doing business internationally, as each country has its own regulations. For example, there are regional data protection regulations, such as the EU’s GDPR (General Data Protection Regulation) and the US’s CCPA (California Consumer Privacy Act).
• Impact: This will require companies to meet multiple legal standards simultaneously, increasing compliance costs.

1. Standardization Challenges:

• Description: Because there are no established technical standards for generative AI, companies and research institutes may take different approaches, making it difficult to ensure interoperability and reliability.
• Impact: Lack of standardization can hinder widespread adoption and commercial use of a technology. Establishing common technology standards can encourage collaboration between companies and accelerate technological advancements.

Would you like to proceed to the next section, “Country and company measures”?

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: https://www.bbc.com/news/technology-57962636
: https://www.theverge.com/2020/8/3/21352599/deepfake-music-copyright-law-tiktok
: https://www.forbes.com/sites/legalentertainment/2021/06/10/how-generative-ai-is-disrupting-copyright-law/
: https://www.jdsupra.com/legalnews/navigating-compliance-in-the-age-of-ai-21495/
: https://gdpr.eu/what-is-gdpr/
: https://www.iso.org/news/ref2739.html

Measures taken by each country and company

The US response: policy and legal framework

In the United States, both the government and private companies are taking proactive measures as technology related to generative AI advances.

Government measures

• National Artificial Intelligence Initiative (NAII): The U.S. government has developed the National Artificial Intelligence Initiative to advance research and development of AI technologies, which provides a comprehensive framework for fostering technological innovation while ensuring the ethical use of AI.
• NIST AI Risk Management Framework: The National Institute of Standards and Technology (NIST) is developing a framework for managing risks in AI systems, which provides guidelines for minimizing risks in the design, development, and operation of AI systems.

Measures for private companies

• Google: Google has established guidelines to ensure the ethical use of AI technology, including policies to ensure AI is fair, transparent, and safe, and has established an AI Ethics Committee to evaluate ethical issues surrounding the development and operation of AI technology.
• Microsoft: Microsoft is introducing the Responsible AI Standard to promote the responsible use of AI technology. The standard aims to meet ethical and legal standards in the design and operation of AI systems.

European Measures: GDPR and AI Bill

In Europe, there is a strong focus on protecting personal data and regulating AI technologies.

GDPR (General Data Protection Regulation)

• Description: The GDPR puts strict regulations on the collection, processing and storage of personal data, strengthening the rights of data subjects and requiring companies to adhere to high standards on data protection.
• Impact: If generative AI uses personal data, it will be required to obtain proper consent under GDPR and will need to adhere to the principles of data transparency and purpose limitation.

AI Bill

• Description: The EU is proposing an AI bill to introduce comprehensive regulation of AI technology. The bill aims to set regulations according to the risks of AI systems and impose strict requirements on high-risk AI systems.
• Impact: AI systems considered high risk (e.g. biometric systems) will require rigorous certification processes and oversight to ensure they are secure and trustworthy.

Japan’s measures: Government guidelines and industry initiatives

In Japan, the government and businesses are working together to advance risk management and technological development related to generative AI.

Government guidelines

• AI Technology Strategy: The Japanese government has formulated the “AI Technology Strategy” to promote the development and social implementation of AI technology, which includes guidelines for ensuring the ethical use of AI.
• Act on the Protection of Personal Information (APPI): Japan’s Act on the Protection of Personal Information (APPI) provides similar regulations to the GDPR and strengthens the protection of personal data. This ensures transparency and accountability when generative AI handles personal data.

Industry Efforts

• Fujitsu: Fujitsu has formulated AI ethics guidelines and aims to meet ethical standards in the development and use of AI technology. It also places emphasis on transparency and accountability in the social implementation of AI technology.
• NEC: NEC is working to ensure the fairness and transparency of AI in order to increase social acceptance of AI technology. Specifically, it is developing technology to remove bias and protect data privacy.

Would you like to proceed to the next section, “Corporate Initiatives”?

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https://www.ai.gov/
https://www.whitehouse.gov/ostp/ai/
https://www.nist.gov/programs-projects/nist-artificial-intelligence-risk-management-framework
https://ai.google/responsibility/
https://www.microsoft.com/en-us/ai/responsible-ai
https://gdpr.eu/
https://ec.europa.eu/info/law/law-topic/data-protection/data-protection-eu_en
https://ec.europa.eu/digital-strategy/our-policies/european-approach-artificial-intelligence_en
https://www.europarl.europa.eu/doceo/document/TA-9-2021-0274_EN.html
https://www.meti.go.jp/policy/it_policy/ai/index.html
https://www.ppc.go.jp/en/
https://www.fujitsu.com/global/about/csr/vision/ai-ethics/
https://www.nec.com/en/global/solutions/ai/ethics/index.html

Corporate Initiatives

Examples of countermeasures taken by technology companies

Many companies are taking their own initiatives to address the risks of generative AI. Here are some notable examples.

Google’s efforts

• Establishment of AI Ethics Guidelines: Google is developing guidelines to ensure the ethical use of AI technology. These guidelines aim to ensure that AI is fair, transparent, and safe. Contains the policy.
• Establishment of an AI Ethics Committee: Google is establishing an Ethics Committee to evaluate ethical issues related to the development and operation of AI technologies. The committee will oversee whether AI projects meet ethical standards.

Microsoft’s efforts

• Responsible AI Standard: Microsoft is introducing the “Responsible AI Standard” to advance the responsible use of AI technology. The standard aims to meet ethical and legal standards in the design and operation of AI systems.
• Office of AI Ethics: Microsoft is creating a dedicated office to oversee the ethical use of AI technologies and is working to ensure AI projects are developed in a responsible manner.

IBM’s efforts

• AI Ethics and Trustworthiness Framework: IBM is developing a framework to ensure the ethical use and trust of AI technologies. The framework includes guidelines to ensure transparency, accountability and fairness.
• AI Debiasing: IBM is developing tools to detect and correct bias in AI models, helping ensure that AI systems deliver unbiased results.

Risk management and countermeasures by industry

Managing the risks of generative AI requires a different approach for each industry. Here are some key industry strategies:

Finance industry

• Adoption of risk management systems: Financial institutions are adopting risk management systems that use generative AI, which is improving the accuracy of fraud detection and risk assessment.
• Strengthening regulatory compliance: The financial industry operates under strict regulations, and compliance is also required for the use of generative AI. Frameworks are being created to comply with regulations in each country [17†source].

Medical Industry

• Protecting data privacy: In the medical field, protecting the privacy of patient data is important. When using generative AI, data is anonymized and encrypted.
• Improved diagnostic accuracy: Generative AI is used in image analysis and diagnostic support systems, which contributes to improving diagnostic accuracy, for example in early cancer detection.

Manufacturing Industry

• Automated Quality Control: Using generative AI to automate manufacturing process monitoring and quality control reduces the incidence of defects.
• Improved Productivity: AI-based predictive maintenance and production scheduling improve productivity and reduce costs.

Introduction of ethical AI and sustainable development

The use of generative AI requires addressing ethical issues and ensuring sustainable technological development.

Introduction of Ethical AI

• Explainable AI (XAI): Explainable AI (XAI) is a technology that explains AI’s decision-making process in a way that humans can understand, making AI more transparent and trustworthy.
• AI ethics education: Companies are conducting AI ethics education for their employees and promoting ethical use of AI.

Sustainable development

• Improve energy efficiency: Training generative AI requires a lot of energy. Companies are working to make technology more sustainable by developing more energy-efficient hardware and algorithms.
• Environmental Consideration: Measures are taken to minimize the environmental impact in the development and operation of generative AI technology, for example by using renewable energy and implementing carbon offsets.

Would you like to continue to the next section, “Assessing and Managing Generative AI Risks”?

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Sources:

9. https://ai.google/responsibility/
10. https://www.microsoft.com/en-us/ai/responsible-ai
11. https://ec.europa.eu/info/law/law-topic/data-protection/data-protection-eu_en
12. https://www.ibm.com/watson/ai-ethics/
13. https://www.forbes.com/sites/forbestechcouncil/2021/10/13/ethical-ai-why-its-the-key-to-building-trust-and-transparency-in-business/
14. https://www.nature.com/articles/d41586-019-01478-8
15. https://www.nist.gov/programs-projects/nist-artificial-intelligence-risk-management-framework
16. https://www.americanbanker.com/news/ai-and-fraud-detection-a-match-made-in-data-heaven
17. https://www.healthit.gov/topic/privacy-security-and-hipaa/health-privacy
18. https://jamanetwork.com/journals/jama/fullarticle/2778930
19. https://www.ibm.com/blogs/research/2020/09/ai-in-manufacturing/
20. https://www.forbes.com/sites/forbestechcouncil/2021/07/21/ai-in-manufacturing-predictive-maintenance-and-productivity/
21. https://arxiv.org/abs/2002.01266
22. https://www.forbes.com/sites/forbestechcouncil/2020/12/14/how-to-teach-ai-ethics/
23. https://arxiv.org/abs/2107.01101
24. https://www.nature.com/articles/d41586-021-01742-1

Assessing and Managing Generative AI Risks

We present a methodology and framework for assessing and effectively managing the risks of generative AI, thereby enabling organizations to minimize the risks associated with its use.

Risk assessment methods and frameworks

There are various methodologies and frameworks used to assess the risks of generative AI. These methodologies identify, analyze, and evaluate the risks.

Risk assessment methodology

1. Qualitative Risk Assessment:

• Description: A method for qualitatively assessing risk, subjectively assessing the impact and probability of risk occurrence. It is typically visualized using a risk matrix or heat map.
• Application example: It is useful for assessing the ethical and societal risks associated with the use of generative AI.

1. Quantitative Risk Assessment:

• Description: A method for quantitatively assessing risk, which numerically evaluates the impact and probability of risk occurrence. Statistical models and simulations are generally used.
• Application example: Useful when assessing the technical and economic risks of generative AI.

Risk Assessment Framework

1. NIST AI Risk Management Framework:

• Description: The AI Risk Management Framework provided by the National Institute of Standards and Technology (NIST) provides guidelines for systematically assessing and managing the risks of AI systems. The framework covers risk assessment at each stage of the design, development, and operation of AI systems.
• Application example: Can be applied to risk assessment of a wide range of AI systems, including generative AI.

1. ISO/IEC 38500:2015:

• Description: This standard, from the International Organization for Standardization (ISO) and the International Electrotechnical Commission (IEC), provides a framework for the governance of information technology and can be applied to risk management of AI systems.
• Application example: Used to enhance governance and risk management of AI technologies.

Risk Management Best Practices

To effectively manage the risks of generative AI, we recommend adopting the following best practices:

1. Ensuring transparency:

• Explanation: Making the decision-making process of generative AI systems transparent increases trust for users and stakeholders. Explainable AI (XAI) techniques are encouraged.
• Application Example: Create documentation and reports that clearly show how Generative AI processes data and produces results.

1. Establishment of ethical guidelines:

• Description: Prevent ethical issues by formulating ethical guidelines for the use of generative AI and educating employees about them.
• Application example: Implement a program to train AI development teams on how to recognize and deal with ethical issues.

1. Risk Monitoring and Assessment:

• Description: By continuously monitoring and regularly evaluating risks during the operation of the generative AI system, new risks can be discovered early and measures can be taken.
• Application example: By regularly analyzing operational data from AI systems and introducing an anomaly detection system, risk occurrence can be monitored in real time.

Continuous monitoring and risk mitigation

Continuous monitoring and mitigation efforts are crucial in managing the risks of generative AI.

1. Introduction of anomaly detection system:

• Description: A system to detect anomalies during operation of the generative AI system will be introduced, allowing for rapid response when problems occur.
• Application example: Build a mechanism to monitor system performance and output in real time and issue an alert if an abnormality is detected.

1. Perform regular risk assessments:

• Description: Conduct regular risk assessments of generative AI systems to address new risks and changes to existing risks.
• Application example: Prepare a quarterly risk assessment report and report it to management to continuously improve the effectiveness of risk management.

Would you like to proceed to the next section, “The Future of Generative AI and the Importance of Risk Management”?

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Sources:

24. https://www.nist.gov/programs-projects/nist-artificial-intelligence-risk-management-framework
25. https://www.forbes.com/sites/forbestechcouncil/2021/10/13/ethical-ai-why-its-the-key-to-building-trust-and-transparency-in-business/
26. https://arxiv.org/abs/2107.01101
27. https://arxiv.org/abs/2002.01266

The future of generative AI and the importance of risk management

Evolution of generative AI and future prospects

Generative AI is evolving rapidly, and its future holds many possibilities. Here are some key points about the outlook for generative AI:

Advanced generation capabilities

• Description: Generative AI technology is becoming increasingly sophisticated and capable of generating more realistic and complex content. This includes areas such as natural language processing, image generation, and speech synthesis.
• Impact: This advancement will enable generative AI to find widespread applications not only in creative projects and the entertainment industry, but also in business, education, healthcare, and other fields.

Interactive AI

• Description: In the future, it is expected that generative AI will become more interactive and able to naturally interact with users, which will greatly improve the user experience.
• Impact: Virtual assistants and customer service using generative AI will become more prevalent, providing more personalized support.

Increased automation and efficiency

• Description: Advances in generative AI will automate a variety of business processes and improve productivity, for example by streamlining tasks such as content generation, data analysis, and predictive modeling.
• Impact: This allows companies to reduce costs, improve operational efficiency, and become more competitive.

The importance of risk management for sustainable technological development

To realize the future of generative AI, sustainable technological development is essential, and the following risk countermeasures are important to achieve this:

Ethical AI development

• Description: Ethical AI development is essential to increasing the trust and societal acceptability of generative AI. This includes ensuring fairness, transparency, and accountability.
• Impact: Ethical AI development will increase the acceptance of AI technology throughout society, promoting the spread and development of the technology.

Sustainable energy use

• Description: Training and running generative AI requires a lot of energy. Energy-efficient technologies and the use of renewable energy are key.
• Impact: Sustainable energy use minimizes environmental impact and ensures long-term use of Generative AI.

Ongoing risk management

• Description: The risks of generative AI are constantly changing and require ongoing risk management, which includes regular risk assessment and monitoring.
• Impact: Continuous risk management allows for early detection of emerging risks and the taking of appropriate measures.

Summary | Find the right generative AI tool for you

By understanding the risks and countermeasures of generative AI, you can use it safely and effectively. Below are the key points of this article.

1. Diverse risks of generative AI: There are technical risks, social risks, and legal risks, each of which requires appropriate measures.
2. Efforts by countries and companies: Governments and companies around the world are taking various measures, and by referring to these, you can strengthen your own company’s risk management.
3. Risk assessment and management are important: Systematic risk assessment and ongoing risk management will support the safe use of generative AI.
4. Sustainable Technological Development: By pursuing ethical and sustainable technological development, we can realize and maximize the benefits of a generative AI future.

Finally, risk management and sustainable technological development are essential to ensure the safe and effective use of generative AI. By taking these measures, we can maximize the potential of generative AI and lead the way in future technological innovation.

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