AI Ethics Dilemmas with Real-Life Examples
Though artificial intelligence is changing how businesses work, there are concerns about how it may influence our lives. This is not just an academic or societal problem, but a reputational risk for companies; no company wants to be undermined by data or AI ethics scandals that damage its reputation.
Explore insights into ethical issues that arise with the use of AI, examples of misuse, and the key principles to mitigate these problems.
Algorithmic Bias
Algorithms and training data may contain biases, as humans do, since humans also generate those. These biases prevent AI systems from making fair decisions. We encounter biases in AI systems due to two reasons:
- Developers may program biased AI systems without even noticing
- Historical data used to train AI algorithms may not be sufficient to accurately represent the entire population.
Real-life example: Large language models (LLMs) are increasingly used in workplaces to improve efficiency and fairness, but they may also reproduce or amplify social biases. The Silicon Ceiling study examines the impact of LLMs on hiring by auditing race and gender bias in OpenAI’s GPT-3.5, drawing on traditional resume audit methods.
Researchers conduct two studies using names associated with different races and genders: resume evaluation and resume generation. In Study 1, GPT scores resumes with varied names across multiple occupations and evaluation criteria, revealing stereotype-based biases. In Study 2, GPT generates fictitious resumes, showing systematic differences: women’s resumes reflect less experience, while Asian and Hispanic resumes include immigrant markers.
These findings add to evidence of bias in LLMs, particularly in hiring contexts. To build an ethical and responsible AI, eliminating biases in AI systems is necessary. Yet, only 47% of organizations test for bias in data, models, and human use of algorithms.
Autonomous Things
Autonomous Things (AuT) are devices and machines that perform specific tasks without human intervention. These machines include self-driving cars, drones, and robotics. Since robot ethics is a broad topic, we focus on unethical issues arising from the use of self-driving vehicles and drones.
Self-Driving Cars
The autonomous vehicles market was valued at $54 billion in 2019 and is projected to reach $557 billion by 2026. Despite its growing value, autonomous vehicles pose various risks to AI ethics guidelines. The liability and accountability of autonomous vehicles are still a matter of debate.
Real-life example: In 2018, an Uber self-driving car hit a pedestrian who later died at a hospital. The accident was recorded as the first death involving a self-driving car. After the investigation by the Arizona Police Department and the US National Transportation Safety Board (NTSB), prosecutors decided that the company is not criminally liable for the pedestrian’s death due to the safety driver being distracted by her cell phone, labeling the accident as “completely avoidable.”
Lethal Autonomous Weapons (LAWs)
LAWs are AI-powered weapons that can identify and engage targets on their own based on programmed rules. Such systems have existed for decades, particularly in defensive applications like mines, missile defense, and sentry systems.
Real-life example: In the Ukraine-Russia conflict, autonomous weapons are mainly used through AI-enabled drones and loitering munitions rather than fully independent systems. Russia employs loitering munitions, which can autonomously search for and strike predefined military targets with minimal human control once launched.
These systems increase speed and precision on the battlefield but reduce meaningful human oversight, creating legal and ethical challenges under international humanitarian law, particularly regarding the principles of distinction, proportionality, and accountability.
Real-life example: Since 2018, the United Nations has consistently opposed lethal autonomous weapons systems (LAWS). Secretary-General António Guterres has called them politically and morally unacceptable, urging their prohibition.
Unemployment and Income Inequality due to Automation
AI-driven automation is expected to significantly reshape labor markets, contributing to short-term unemployment pressures and widening income inequality if left unmanaged. Current projections suggest that 15-25% of jobs will face significant disruption by 2025-2027, with 5-10% net job displacement after new roles are created.
At the same time, AI complements human labor in areas such as decision-making, reasoning, and creativity, shifting demand toward higher-value skills. With over 40% of workers needing substantial upskilling by 2030, unequal access to retraining risks deepening income inequality between those who can adapt to AI-enabled roles and those who cannot.
Misuses of AI
AI Governance Disputes over Autonomous Weapons
Recent tensions between AI companies and governments illustrate how difficult it is to set limits on the military use of AI. In early 2026, AI company Anthropic refused to sign a U.S. Department of Defense contract that would allow the government “unrestricted access” to its models for “all lawful purposes.”
Anthropic CEO Dario Amodei stated that the company would only participate if two safeguards were included: prohibiting mass domestic surveillance and preventing the development of fully autonomous weapons without human oversight.
This disagreement highlights broader concerns about the role of advanced AI systems in warfare. While large language models are not weapons themselves, they can be integrated into military systems to analyze intelligence and generate lists of potential targets.
Surveillance Practices Limiting Privacy
“Big Brother is watching you.” This famous line from George Orwell’s dystopian novel 1984 was once a work of science fiction. Today, however, it increasingly feels like reality, as governments deploy AI for mass surveillance. In particular, the use of facial recognition technology in surveillance systems has raised serious concerns about privacy rights.
Real-life examples: Some tech giants also state ethical concerns about AI-powered surveillance. For example, Microsoft President Brad Smith published a blog post calling for government regulation of facial recognition. IBM stopped offering the technology for mass surveillance due to its potential for misuse, such as racial profiling, which violates fundamental human rights.
Manipulation of Human Judgment
AI-powered analytics can provide actionable insights into human behavior, yet abusing analytics to manipulate human decisions is ethically wrong.
Real-life example: Cambridge Analytica sold American voters’ data crawled on Facebook to political campaigns and provided assistance and analytics to the 2016 presidential campaigns of Ted Cruz and Donald Trump. Information about the data breach was disclosed in 2018, and the Federal Trade Commission fined Facebook $5 billion due to its privacy violations.
Proliferation of Deepfakes
Deepfakes are synthetically generated images or videos in which a person in a media image or video is replaced with someone else’s likeness. Creating a false narrative using deepfakes can harm people’s trust in the media. This mistrust is dangerous for societies, considering mass media is still the number one option of governments to inform people about emergency events.
Real-life example: The European Commission has opened an investigation into Elon Musk’s platform X over allegations that its AI tool, Grok, was used to generate sexualized deepfake images of real people, following similar action by the UK regulator Ofcom.
Artificial General Intelligence (AGI) / Singularity
The prospect of artificial general intelligence (AGI) or singularity raises ethical concerns about the value of human life as machines surpass human intelligence. Practical dilemmas, such as whether self-driving cars should prioritize the safety of passengers or pedestrians, highlight unresolved moral questions that must be addressed before these technologies are widely deployed.
More broadly, the emergence of superintelligent systems challenges human dominance and raises fundamental questions about the rights, responsibilities, and moral frameworks of artificial beings.
Robot Ethics
Robot ethics, or roboethics, deals with how humans design, use, and treat robots. Debates on this topic have existed since the 1940s, mainly questioning whether robots should have rights comparable to those of humans and animals.
Author Isaac Asimov is the first one to talk about laws for robots in his short story called “Runaround”. He introduced the Three Laws of Robotics:
- A robot may not injure a human being or, through inaction, allow a human being to come to harm.
- A robot must obey the orders given by human beings except where such orders would conflict with the First Law.
- A robot must protect its existence as long as such protection does not conflict with the First or Second Law.
How to Navigate These Dilemmas?
These are hard questions, and innovative, controversial solutions like the universal basic income may be necessary to address them. There are numerous initiatives and organizations aimed at minimizing the potential negative impact of AI.
For instance, the Institute for Ethics in Artificial Intelligence (IEAI) at the Technical University of Munich conducts AI research across various domains such as mobility, employment, healthcare, and sustainability.
Recommendations for Mitigating AI Controversies
Consider UNESCO policies & best practices:
- Data Governance Policy: This policy emphasizes the importance of detailed frameworks for data collection, use, and governance to ensure individual privacy and mitigate risks.
- Ethical AI Governance: Governance mechanisms must be inclusive, multidisciplinary, and multilateral, incorporating diverse stakeholders.
- Education and Research Policy: It promotes AI literacy and ethical awareness by integrating AI and data education into curricula.
- Health and Social Well-Being: This policy encourages the deployment of AI to improve healthcare and advance mental health.
- Gender Equality in AI: Aims to reduce gender disparities in AI by supporting women in STEM fields.
- Environmental Sustainability: This policy focuses on assessing and mitigating the environmental impact of AI.
- Readiness Assessment Methodology (RAM): This technique helps states evaluate their preparedness to implement ethical AI policies.
- Ethical Impact Assessment (EIA): This method assesses the potential social, environmental, and economic impacts of AI projects.
- Global Observatory on AI Ethics: A digital platform that offers analyses of AI’s ethical challenges.
- AI Ethics Training and Public Awareness: Encourages accessible education and civic engagement.
Best Practices Recommended by UNESCO
- Inclusive and Multi-Stakeholder Governance: Involve diverse stakeholders in policy creation and AI governance.
- Transparency and Explainability: Develop AI systems with interpretable decision-making processes.
- Sustainability Assessments: Regularly evaluate AI systems for their environmental impact.
- AI Literacy Programs: Educate the public and policymakers on AI’s ethical implications.
- Ongoing Audits and Accountability Mechanisms: Establish regular audits for AI systems to detect and address biases.
Learn Responsible AI Frameworks
Here are some responsible AI frameworks to overcome ethical dilemmas like AI bias:
- Transparency: AI developers have an ethical obligation to be transparent in a structured, accessible way.
- Explainability: AI developers and businesses need to explain how their algorithms arrive at their predictions.
- Alignment: Modernizing legal frameworks will clarify the path to ethical AI development.
- Use AI Ethics Frameworks and Tools: Organizations are increasingly focusing on ethical frameworks to guide the use of AI technologies.
FAQs
What is AI ethics? AI ethics is the study of the moral principles guiding the design, development, and deployment of artificial intelligence. It addresses issues such as fairness, transparency, privacy, and accountability to ensure AI systems benefit society, avoid harm, and respect human rights.
What is the UNESCO recommendation for the Ethics of AI? The UNESCO Recommendation on the Ethics of AI calls for minimizing discriminatory and biased outcomes in AI systems while promoting fairness, transparency, accountability, and respect for human rights.
