Generative AI Ethics: Concerns and How to Manage Them?
Generative AI raises important concerns about how knowledge is shared and trusted. Britannica, for instance, has accused Perplexity of reusing its content without consent and even attaching its name to inaccurate answers, showing how these technologies can blur the lines between reliable sources and AI-generated text.1
Explore what generative AI ethics concerns are and best practices for managing them.
1. Bias in outputs
AI models learn patterns from vast amounts of training data that may include stereotypes, incomplete information, or skewed representations. This bias can appear in AI-generated outputs in multiple ways, such as misrepresenting certain groups in hiring systems or reinforcing unfair assumptions in healthcare decision-making.
In business contexts, biased models raise ethical concerns when customers or employees are treated unequally due to patterns embedded in data sets.
Real-life example:
The European Commission guidelines emphasize that researchers must be aware of these biases, as they can compromise research integrity and scientific fairness.2
Real-life example:
AI Fairness 360 is an open-source toolkit under The Linux Foundation designed to identify and reduce bias in machine learning models.
Originally developed by IBM, it provides ten leading bias mitigation algorithms and over seventy fairness metrics to assess both individual and group fairness.
Available in Python and R, the toolkit supports practical applications across sectors such as finance, healthcare, and education. It includes tutorials demonstrating real-world use cases like credit scoring and medical expenditure analysis.
AI Fairness 360 encourages community collaboration through GitHub and dedicated mailing lists for announcements, technical discussions, and governance.3
2. Misinformation and hallucinations
Generative AI models can produce false or misleading content, also known as hallucinations. These hallucinations often sound confident and authoritative, which increases the risk that users trust them as reliable information sources.
One example is when generative AI creates fabricated citations in academic writing, leading to unverifiable references in higher education research. In business, hallucinated product information can damage customer trust if AI systems present inaccurate details.
Real-life example:
The Indian government has introduced draft regulations requiring artificial intelligence and social media platforms to label AI-generated content to combat misinformation and deepfakes.
The proposal mandates that visual media include visible markers covering at least 10% of the display area and that audio clips disclose AI generation during the first 10 percent of playback. Platforms such as OpenAI, Meta, Google, and X would also need to obtain user declarations and implement technical measures to verify compliance.
The Ministry of Information Technology stated that these measures aim to enhance transparency, traceability, and accountability in public-facing AI content.
The rules respond to growing concerns over the misuse of generative AI to spread misinformation, manipulate elections, and infringe intellectual property rights. Ongoing court cases involving Bollywood actors underscore the urgency of regulation.4
3. Intellectual property and copyright
Generative AI technology raises questions about copyright protection and intellectual property.
AI-generated works may reproduce copyrighted material without acknowledgment. Training data often includes copyrighted material scraped from the internet, which can lead to copyright infringement when the system reuses elements in generated content.
For researchers, there is an ethical concern when AI tools produce text or images based on existing copyrighted publications, as it undermines academic integrity. Businesses also face legal risks if AI-generated outputs resemble copyrighted logos, articles, or designs.
Discover the AI image detector benchmark to see which tools are most effective at detecting AI-generated content.
Real-life example:
The Microsoft Responsible AI Transparency Report outlines the company’s approach to developing, deploying, and governing artificial intelligence responsibly.
Guided by its core principles of fairness, reliability, safety, privacy, inclusiveness, transparency, and accountability, Microsoft applies the NIST AI Risk Management Framework to ensure adequate oversight across the AI lifecycle.
The report highlights advancements in responsible AI tools, compliance with regulations such as the EU AI Act, and implementation of the Frontier Governance Framework to manage high-impact model risks. It details progress in content safety, transparency initiatives such as LinkedIn’s content credentials and Copilot query visibility, and global collaborations to combat deceptive AI use.
The company also invests in AI literacy programs and research efforts, including the EUREKA evaluation framework and studies from the Sociotechnical Alignment Center, to improve AI risk measurement and governance across industries.5
Real-life example:
The Deepfake-Eval-2024 benchmark was created to reflect current conditions by including 45 hours of manipulated video, 56.5 hours of audio, and nearly 2,000 images collected from social media and user platforms across 88 websites and 52 languages.
When open-source detection models were tested on this dataset, their accuracy dropped significantly, with performance reductions of about 50% for video, 48% for audio, and 45% for image detection.
Commercial systems and models fine-tuned on the new benchmark performed better, but still fell short of the precision achieved by trained forensic experts. This highlights both the urgency of advancing detection tools and the ongoing importance of human expertise in safeguarding against AI-generated disinformation.
Figure 1: The image, which shows Deepfake-Eval-2024 video and audio examples in the first two rows and image samples in the third and fourth rows, illustrates the wide range of content styles and generation methods, such as lip-sync, face-swapping, and diffusion.6
4. Privacy and sensitive information
The use of generative AI tools often requires inputting data into external systems. If sensitive information, such as unpublished research, patient records, or business documents, is uploaded, there is a risk that it will be stored, reused, or exposed without consent.
Real-life example:
South Korea’s Personal Information Protection Commission suspended new downloads of the Chinese AI app DeepSeek after the company admitted it had not fully complied with the country’s privacy rules.
The suspension, which began in mid-February 2025, will remain in effect until DeepSeek adjusts its practices to comply with local data protection laws, although its web service remains accessible. The startup has recently appointed legal representatives in South Korea and acknowledged its shortcomings in handling personal data. This move follows similar action in Italy, where regulators blocked DeepSeek’s chatbot due to concerns about its privacy policy.7
5. Accountability and authorship
AI systems cannot be authors because authorship implies responsibility and agency.
Ethical AI practice requires that humans remain fully accountable for AI-generated works. Researchers cannot attribute authorship to generative AI models, as only humans can ensure accuracy, fairness, and respect for intellectual property.
In business, companies must ensure that employees are responsible for generating content and that there is a transparent chain of accountability.
Real-life example:
Arthur’s Shield platform provides real-time protection for large language models by monitoring both user prompts and model responses to prevent harmful or unsafe outputs.
Positioned between the application and deployment layers, it integrates with any LLM, including OpenAI models, and can be deployed across cloud, SaaS, or on-premises environments.
Shield safeguards against data leakage, hallucinations, toxicity, and prompt injection attacks while ensuring alignment with organizational values.
Its model- and platform-agnostic design allows compatibility with major providers such as AWS and Azure, enabling enterprises to deploy generative AI systems securely.8
Real-life example:
The University of Alberta emphasizes that academic integrity depends on transparent disclosure when using generative AI.9
6. Job displacement
Generative AI tools automate structured tasks in areas such as content writing, customer services, and design, raising significant concerns about workforce disruption.
Real-life example:
Expert forecasts that as many as 50% of entry-level white-collar jobs could disappear by 2027, with clerical, administrative, and customer service roles at the highest risk. The International Monetary Fund estimates that 300 million jobs globally may be affected, primarily through task-level automation rather than complete elimination, but this still pressures workers to adapt quickly.
A particular ethical concern is the loss of junior positions, which undermines mentorship and long-term workforce development, creating what researchers describe as an “exponentially bad move” for companies.
These disruptions are not just economic; they also carry societal and political risks, as concentrated job losses could exacerbate inequality, undermine social stability, and heighten public anxiety about the future of work. Read AI job loss to learn more about the economic and social implications.
7. Environmental impact
Generative AI models raise ethical concerns due to their high energy use, water demands, and hardware costs. Training large language models with billions of parameters can generate hundreds of metric tons of CO₂, while inference adds a continuous burden as AI systems scale.10
The footprint varies by geography, as energy sources and cooling needs significantly impact emissions and water consumption. In some cases, training a single model has required nearly a million liters of water, and even everyday use consumes measurable amounts.
Hardware production adds further impact through rare earth mining and energy-intensive fabrication, with rapid model turnover multiplying these costs.11
While generative AI can support sustainability goals, such as optimizing transport or predicting environmental risks, its own resource demands create a serious ethical issue.
Real-life example:
The Generative AI Ethics Playbook emphasizes the need to address the environmental consequences of artificial intelligence, particularly the high energy consumption and carbon emissions linked to training and deploying large models.
It cautions that the rapid expansion of computationally intensive AI systems contributes significantly to greenhouse gas emissions and resource depletion, especially when powered by non-renewable energy sources. Practitioners are advised to record and disclose the carbon footprint of their models to ensure accountability and informed decision-making about sustainability.
To mitigate these effects, several strategies are proposed. Developers are encouraged to:
- Use measurement tools to monitor energy and resource usage.
- Adopt smaller or modular architectures that reduce computational demand.
- Implement optimization methods such as pruning, model distillation, and low-precision computation to improve efficiency.
The article also advocates selecting data centers that rely on renewable energy and supporting organizational transitions toward sustainable energy providers. These practices aim to promote environmentally responsible AI development that balances innovation with ecological stewardship.12
8. Security and misuse
Generative AI systems can be exploited in harmful ways, such as through “prompt injection attacks” that override safety mechanisms or by creating malicious code. These risks include spreading disinformation, producing toxic content, or enabling cyberattacks.
In the political sphere, AI-generated deepfakes and manipulated outputs have the potential to influence elections and damage public trust. Businesses must recognize that AI technology can be used to create content with unintended or potentially dangerous consequences if not carefully monitored.
Real-life example:
Generative AI applications had varying impacts on the 2024 European, French, and British elections. Deepfakes targeted leaders like Olaf Scholz, Keir Starmer, and Marine Le Pen. At the same time, right-wing parties in Germany and France used AI personas and undisclosed content, and Russian groups deployed large language models for pro-Russia narratives, showing how AI can spread disinformation and foreign influence.
Chatbots such as ChatGPT, Gemini, and Copilot have proven unreliable, often providing incomplete or inaccurate election details, which raises ethical concerns about their role in democratic processes.13
Best practices to manage generative AI ethics concerns
Maintain human oversight
AI should not replace human judgment in high-stakes contexts. Instead, humans must stay informed to verify the accuracy of AI-generated outputs.
For example, in healthcare, doctors should utilize generative AI models as assistants rather than decision-makers. Generative AI ethics guidelines emphasize that researchers remain accountable for their outputs and stress the importance of integrating human-in-the-loop processes to ensure accuracy and ethical use.
Disclose AI use transparently
Transparency about the use of generative AI tools builds trust and ensures accountability. Researchers should state which tools were used, their version, and how they influenced the generated content. Businesses can apply watermarks or in-app labels to clarify when content is AI-generated.
Transparency also prevents ethical issues where AI-generated works are presented as entirely human, which could mislead customers.
Protect sensitive data
Responsible use of AI requires careful handling of sensitive information. Researchers must not upload unpublished data or personal information into external AI tools unless they are assured of having adequate privacy protections.
Companies should prioritize using first-party or zero-party data when training AI models, reducing risks associated with unreliable third-party sources. Protecting sensitive data prevents misuse, respects privacy laws, and avoids exposing information that could damage trust.
Address bias and fairness
Bias in training data directly affects AI-generated outputs. Organizations must test for bias and evaluate models before deployment to ensure fairness. Researchers should disclose the limitations of generative AI systems, including their potential for bias, and adopt mitigation strategies accordingly.
In businesses, testing AI-generated outputs across different demographics can prevent discriminatory effects.
Ensure copyright protection
To prevent copyright infringement, users should respect intellectual property rights and properly cite sources when using AI-generated content. Researchers should not pass off AI-generated works as original if they are derived from copyrighted material. Businesses must avoid deploying generative AI systems that reproduce copyrighted logos or text without obtaining the necessary permission.
Promote sustainable practices
Since environmental impact is a recognized ethical issue, organizations should choose AI tools with lower energy usage where possible.
Efficient prompting, smaller AI models, and optimized infrastructure can reduce the environmental footprint. Researchers should also assess the environmental implications of using large language models and disclose them where relevant, aligning with sustainability goals.
Continuous monitoring and testing
Generative AI models require constant oversight. Organizations should not treat AI as static tools; instead, they should conduct regular audits of generated data to ensure accuracy, identify potential biases, and assess security risks. Continuous monitoring helps ensure that generative AI tools are used responsibly in both research and business.
Education and training
Training users on ethical considerations is critical for the responsible use of AI. Businesses should educate their employees on the risks and limitations of AI-generated content, ensuring they can verify the outputs and maintain professional integrity.
Encourage feedback and dialogue
Creating open feedback mechanisms helps organizations detect risks early. Employees, researchers, and communities should be encouraged to report concerns about AI-generated outputs. Companies can establish anonymous reporting systems or ethics councils to oversee the adoption of AI. Dialogue between subject matter experts, developers, and users ensures that ethical issues are addressed in multiple ways and that practices evolve in response to technological change.
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Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE and NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and resources that referenced AIMultiple.
Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised enterprises on their technology decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.
He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.
Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.
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