Rapidly advancing AI technology has made our lives and businesses more convenient and prosperous. However, are you aware that this progress also highlights significant ethical challenges in the use of AI?
This article provides a comprehensive overview of ethical issues in AI, including case studies and initiatives by private companies, as well as key points to consider to avoid AI ethics problems. If your company is considering implementing AI, please read on.
What are Ethical Issues in AI Utilization?
Ethical issues in AI utilization refer to the social and moral challenges that arise behind the convenience and efficiency gains brought by AI. For example, bias problems, which occur when AI learns from skewed data, risk leading to unfair judgments based on race or gender.
Furthermore, the use of facial recognition technology and personal data raises concerns about potential privacy violations. Another representative ethical issue is the lack of clarity regarding accountability for decisions and actions made by AI.
Thus, in our modern world where AI is widespread, AI ethics has become a critically important challenge. It is clear that the more AI permeates society, the more essential it becomes to address these ethical issues.
Key Problems in AI Ethics
What specific aspects are considered problematic in AI utilization? This chapter explains the main issues in AI ethics.
Privacy Violations
As AI technology evolves, vast amounts of personal data are collected and analyzed, making privacy violations a major concern. For instance, facial recognition technology and tracking via surveillance cameras can threaten individual freedom and privacy in exchange for convenience. Therefore, there is a growing need to establish clear rules for AI utilization, defining the acceptable limits of data collection and use.
Bias and Discrimination
Since AI systems learn from data provided by humans, they can directly reflect the biases present in that data. This includes cases where AI makes decisions that disadvantage specific genders or races, thereby exacerbating social inequalities. To solve this problem, it is crucial to improve data quality and develop AI with fairness in mind.
Lack of Accountability
The process by which AI arrives at a specific decision is often a “black box,” and this lack of accountability is a significant concern. Especially when AI is involved in critical decisions in fields like medicine or finance, transparency is required so users can trust the outcomes. To ensure this transparency, there is an urgent need to develop technologies that can explain the workings of AI algorithms.
Unclear Allocation of Responsibility
When a decision or action by AI causes a problem, the difficulty in pinpointing responsibility is another classic AI ethics issue. For example, if content generated by AI contains misinformation or copyright infringement, multiple parties are involved—platform operators, AI developers, users—but identifying who is responsible is challenging. This points to the need for measures at the national and governmental level, such as legal frameworks and ethical guidelines, to clarify responsibility in AI utilization.
Three Case Studies of AI Ethics Issues
Recently, AI ethics issues have frequently become major topics of discussion. This chapter introduces three real-world examples of such problems.
Amazon (Gender Discrimination in Recruitment)
Previously, Amazon used an AI-powered recruitment system. This system learned from past hiring data to judge candidates’ suitability. However, it was found to favor male candidates and disadvantage female candidates.
This case demonstrates the risk that AI can perpetuate unfair judgments if its training data contains bias. Although Amazon eventually discontinued the system, it served as a catalyst for renewed focus on the transparency of data and algorithms in AI utilization.
Samsung Electronics (Privacy Violation)
In 2023, an incident occurred at Samsung Electronics where employees used an AI tool to process sensitive information, leading to a data leak. The primary cause was believed to be an engineer using ChatGPT to fix source code bugs. During this process, code containing sensitive information was sent to the AI’s servers, resulting in a partial leak of the data.
This highlights the indispensable need for security measures to protect confidential data when companies utilize AI. Leaking sensitive information can lead to irreparable consequences, such as loss of competitiveness and social trust, making this a critical consideration for any company using AI.
Tokyo 2020 Olympics (Unclear Responsibility in Traffic Accident)
The introduction of autonomous vehicles was a major talking point during the Tokyo 2020 Olympics. However, one of these vehicles was involved in a collision with a visually impaired athlete within the athletes’ village.
Subsequent investigations into the accident revealed an unclear allocation of responsibility among the multiple parties involved, including:
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The vehicle manufacturer
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The Olympic organizing committee
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On-site guides
This case illustrates the necessity of clarifying responsibility sharing when accidents or problems occur, especially as collaboration between AI and humans becomes more prevalent.
Private Sector Initiatives Addressing AI Ethics
Many companies are now undertaking various initiatives to tackle AI ethics issues. This chapter introduces some of these efforts.
In 2018, Google established three AI principles to guide the integration of AI throughout its operations:
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Be bold with innovation.
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Develop and deploy AI responsibly.
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Work together to advance.
These principles articulate the company’s key priorities and philosophies regarding AI use. Based on these principles, Google promotes the beneficial use of AI, explicitly stating its responsibility in AI development and deployment while addressing grand themes like economic development and scientific progress. By publicly committing to such high-level principles, Google solidifies its position as a leading company at the forefront of the AI field.
Microsoft
Based on the philosophy that “AI should be developed and used based on trust, for the benefit of all people,” Microsoft established six core principles for responsible AI development and use in 2018. These principles include:
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Fairness
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Reliability and safety
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Privacy and security
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Inclusiveness
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Transparency
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Accountability
Guided by these principles, Microsoft develops AI systems with a strong emphasis on ethical considerations. Furthermore, the company promotes the socially responsible use of AI by focusing on internal ethics education and collaboration with external experts.
Fujitsu
Fujitsu has established the “Fujitsu Group AI Commitment” to actively promote the ethical development and use of AI. This commitment clearly outlines several key guidelines, including:
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Human-centric AI
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Ensuring accountability and transparency
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Protecting privacy
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Ensuring security
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Maintaining fairness
Based on this policy, Fujitsu employs a consistently ethics-focused approach, from AI research and development to social implementation. Additionally, the company has established an ethics committee comprising internal and external experts to continuously review and address ethical challenges related to AI.
OKI Group
In September 2019, the OKI Group established and published its “OKI Group AI Principles.” These principles define ethical standards for AI development and use, incorporating various elements such as:
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Ensuring transparency
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Ensuring accountability
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Protecting privacy
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Maintaining fairness
The company is also strengthening collaboration with various stakeholders to promote the social implementation of AI, driving actions toward achieving human-centric AI development.
Key Points for Companies to Avoid AI Ethics Issues
When introducing and utilizing AI, companies must be mindful of several points. Finally, based on the discussion so far, this chapter explains key points for companies to avoid AI ethics problems.
Ensuring Transparency and Accountability
A crucial point when using AI is to clarify how its algorithms and decision-making processes function. Establishing a system that can explain to users and stakeholders how AI makes decisions builds trust in the AI. If an AI system becomes a black box, it risks unexpected outcomes or misunderstandings, making accountability a key to ethical AI use.
Eliminating Bias and Ensuring Fairness
Since AI makes decisions based on training data, biases within that data can be directly reflected in the output. Therefore, when developing AI, companies must take sufficient measures to avoid biased data and unfair judgments. Using diverse datasets and implementing mechanisms to monitor and adjust for biased AI decisions contribute to ensuring fairness and building social trust.
Strengthening Privacy and Security
For companies utilizing AI, protecting privacy and strengthening security are indispensable. Especially when handling personal or confidential information, implement robust security measures to protect company data from cyberattacks and unauthorized access. Regularly checking AI systems for vulnerabilities and ensuring data safety can help prevent AI ethics issues before they arise.
Conclusion
This article has explained AI ethics issues through examples and case studies, introduced initiatives by private companies, and discussed key points for avoiding these problems.
AI is an extremely useful tool, but its use requires careful consideration of ethical implications. Revisit this article to understand the main problems and specific examples within AI ethics.
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