With the rapid development of artificial intelligence (AI) technology and its deep penetration into our daily lives, the issue of AI ethics and bias has emerged as a pressing issue. This article takes an in-depth look at the ethical challenges and bias issues posed by AI technology and makes seven key recommendations for a future society.
AI Ethics: Key to Coexistence of Technology and Humans

What is the nature of AI ethics?
The essence of AI ethics is to harmonize artificial intelligence technology with human society. Specifically, it aims to maximize the benefits of AI while protecting human dignity and rights. This includes principles such as fairness, transparency, privacy protection, safety, and accountability; AI ethics considers not only the development of the technology but also its use and impact broadly, putting the interests of society as a whole first.
For example, AI applications in the medical field must strike a balance between improving diagnostic accuracy and protecting patient privacy; AI ethics is becoming increasingly important as a guiding principle for achieving coexistence between technology and humans.
Why AI Ethics Matters Now
With the rapid advancement and spread of AI technology, its influence is expanding exponentially. As AI is used in all aspects of our lives, including medicine, finance, and education, its decisions are increasingly influencing people's lives. However, the risk of bias and error exists in AI decisions, and inappropriate use can lead to discrimination and rights violations.
In addition, laws and regulations have not kept pace with the rapid progress of AI technology. Against this backdrop, AI ethics has become an important guideline for the sound development of technology and its harmony with society, and efforts to address AI ethics are essential for gaining society's trust and for the sustainable development of technology.

AI Bias: The Hidden Roots of Discrimination
Where does bias come from?
Bias in AI systems arises from three main factors. First is bias in the training data. If data is biased toward a particular attribute (gender, race, etc.), AI will learn that bias. Second, algorithm design errors. The developer's unconscious bias may be reflected in the algorithm. Finally, it is a reflection of social bias. Existing social biases and stereotypes may be learned by the AI.
For example, there have been cases where AI, trained on historical hiring data, has favored certain genders or races. These biases could undermine the fairness of AI systems and promote social inequality.
Path to Bias Elimination
AI bias elimination requires a multifaceted approach. First, it is important to build a data set that accounts for diversity. Collect data that is fairly representative of a variety of attributes and audit and correct it regularly. Second, a diverse team, including ethics experts, is required to design algorithms. It is also important to establish a process to regularly audit the output of the AI system to detect and correct biases. In addition, ongoing education and awareness of developers and operators, as well as organization-wide awareness of bias, is also essential. Transparency is also important, and the AI decision-making process must be made as accountable and informative as possible to users. By implementing these initiatives in a comprehensive manner, we can move closer to eliminating bias.

AI Transparency: Building a Foundation of Trust
Why is transparency important?
AI transparency is the foundation for gaining society's trust and for responsible AI development: by clarifying the operating principles and decision-making processes of AI systems, users' understanding and trust can be deepened. Transparency is also essential for accountability: when problems arise with AI decisions, it enables us to identify the causes and respond appropriately. In addition, transparency helps detect and correct bias and unfairness. Understanding the internal workings of the system can help identify potential problems and lead to continuous improvement. Transparency also plays an important role in promoting legal compliance and ethical development; transparency is essential for AI to gain widespread acceptance and sustainable development in society.
Specific ways to increase transparency
To increase AI transparency, a combination of multiple methods can be effective. First, it is important to employ explainable AI (XAI) techniques, such as LIME and SHAP, to explain the AI decision process in a way that humans can understand. Second, whenever possible, code and models should be open-sourced to allow for third-party validation. Strengthening data governance is also important, clearly managing and documenting the source of the data used and how it is processed.
Creating and publishing model cards with detailed specifications and performance indicators for AI models would also be effective. The user interface also needs to be improved, displaying the basis for AI decisions and confidence levels in an easy-to-understand manner. Regular third-party audits and ongoing dialogue with stakeholders will also contribute to improved transparency.
The comprehensive implementation of these methods can greatly increase the transparency of AI systems.

AI Ethics and Legal Liability: Challenges in the Gray Zone
How to define responsibility
Defining where responsibility lies for AI systems is a complex issue. The question is how to distribute responsibility among developers, users, and the AI system itself. For example, in the case of a self-driving car accident, it must be determined where the responsibility lies: with the manufacturer, with the software developer, with the vehicle owner, or with the AI system itself.
One solution proposed is a tiered responsibility model. This is a method of distributing liability according to the level of autonomy of the AI. The introduction of AI insurance and the incorporation of AI as a legal entity are also being considered. Clarifying where responsibility lies is essential for the social implementation of AI, and it is necessary to build a social consensus through legal and ethical discussions.
Current status of international legislation
With the rapid development of AI technology, legislation is being developed in many countries, but the content and progress varies: in the EU, an AI regulatory bill has been proposed and a risk-based approach to AI regulation is being considered. In the U.S., there is no comprehensive AI law at the federal level, but legislation is being developed at the state level and guidelines are being formulated in specific areas. In China, ethical guidelines for AI have been published and AI development is being promoted as a national strategy. In Japan, AI social principles have been formulated and discussions are underway to develop legislation.
However, given the transnational nature of AI technology, international coordination is essential; international organizations such as the OECD and UNESCO have issued principles and recommendations on AI, and a global framework is being developed.
Seven Recommendations for a Future Society
With the rapid development of AI technology and its penetration into society, we are faced with new challenges. A comprehensive and proactive approach is essential to achieve an ethical and sustainable AI society. Below are seven key recommendations for the society of the future from the perspective of AI ethics. These recommendations aim to harmonize technology and humans, maximizing the benefits of AI while minimizing potential risks.
- Strengthening International Cooperation: It is essential to establish an international framework for AI ethics. Harmonization of global AI development and use will be achieved by establishing common standards, while taking into account differences in national legal systems and cultures.
- Enhancement of Education: Enhance AI ethics education for both engineers and general users. We will improve AI literacy in society as a whole by communicating the importance of AI ethics to a wide range of audiences, from school education to recurrent education.
- Developing a long-term vision: Organizations should develop a long-term strategy for AI ethics and continuous improvement. It is important to aim for sustainable AI use, not just short-term gains.
- Ensuring Diversity: Promote diversity in your AI development team. Members with different backgrounds can collaborate to evaluate and improve AI systems from multiple perspectives.
- Ongoing Audits: We conduct periodic ethical reviews of AI systems. Audits, which also include outside experts, will detect and correct biases and ethical issues as early as possible.
- Increased transparency: We will enhance the visibility and accountability of the AI decision-making process. Develop a mechanism for users to understand and, if necessary, challenge AI decisions.
- Promoting Social Dialogue: Promoting public debate and citizen participation on AI ethics. We aim for the harmonious development of AI and society through dialogue among technologists, policy makers, and citizens.
By putting these recommendations into practice, we can develop ethical and trustworthy AI technologies and build a society that embraces them. Our challenge is to properly define the role of AI in the future society and to realize the symbiosis between humans and AI.

Summary: AI Ethics Builds the Society of the Future
AI technology has the potential to enrich our lives. However, in order to reap its full benefits, ethical issues must be seriously addressed and appropriate measures taken. The seven recommendations presented in this article will serve as important guidelines for harmonizing AI technology with human society. Each of us must think about AI ethics and act accordingly to realize a better future society.
[Ref.]
1 UNESCO.(2021). Recommendation on the Ethics of Artificial Intelligence. https://www.unesco.org/en/artificial-intelligence/recommendation-ethics
2 World Economic Forum.(2021). 9 ethical AI principles for organizations to follow. https://www.weforum.org/agenda/2021/06/ethical-principles-for-ai/
3 Prolific.(2023). AI Bias: 8 Shocking Examples and How to Avoid Them. https://www.prolific.com/resources/shocking-ai-bias
4 IMD.(2023). How organizations navigate AI ethics. https://www.imd.org/ibyimd/technology/how-organizations-navigate-ai-ethics/
5 CompTIA.(2023). 11 Common Ethical Issues in Artificial Intelligence. https://connect.comptia.org/blog/common-ethical-issues-in-artificial-intelligence
6 European Commission.(2023). Artificial Intelligence Act. https://digital-strategy.ec.europa.eu/en/policies/regulatory-framework-ai
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