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5 Risks of Generative AI & How to Mitigate Them in 2024

Updated on Jan 2
6 min read
Written by
Cem Dilmegani
Cem Dilmegani
Cem Dilmegani

Cem is the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per Similarweb) including 60% of Fortune 500 every month.

Cem's work focuses on how enterprises can leverage new technologies in AI, automation, cybersecurity(including network security, application security), data collection including web data collection and process intelligence.

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As the world increasingly leans towards technological innovation, prioritizing generative AI has become a focal point for many industries, driving creativity and automation to next levels. However, there are serious potential risks of generative artificial intelligence, including its accuracy and ethical use. Therefore, it’s crucial to recognize and navigate these challenges, ensuring a future where technology serves humanity’s best interests.

In this article, we will explain the top 5 risks of generative AI and will provide steps to mitigate them.

1- Accuracy risks of generative AI

Generative AI tools like ChatGPT rely on large language models that are trained on massive data. To answer a question or to create a response to a certain prompt, these models interpret the prompt and induce a response based on their training data. Although their training data sets consist of billions of parameters, they are finite pools and the generative models may “make up” responses from time to time.

There can be many potential accuracy risks caused by generative AI models:

  • Generalization over specificity: Since generative models are designed to generalize across the data they’re trained on, they may not always produce accurate information for specific, nuanced, or out-of-sample queries.
  • Lack of verification: Generative models can produce information that sounds plausible but is inaccurate or false. Without external verification or fact-checking, users might be misled.
  • No source of truth: Generative AI doesn’t have an inherent “source of truth”. It doesn’t “know” things in the way humans do, with context, ethics, or discernment. It’s generating outputs based on patterns in data, not a foundational understanding.

How to Mitigate:

Mitigating the accuracy risks of generative AI requires a combination of technical and procedural strategies. Here are some ways to address those risks:

  • Data quality and diversity: Ensure that the AI is trained on high-quality, diverse, and representative data. By doing this, the likelihood of the AI producing accurate results across a broad range of queries increases.
  • Regular model updates: Continually update the AI model with new data to improve its accuracy and adapt to changing information landscapes.
  • External verification: Always corroborate the outputs of generative AI with other trusted sources, especially for critical applications. Fact-checking and domain-specific validation are essential.
  • User training: Educate users about the strengths and limitations of the AI. Users should understand when to rely on the AI’s outputs and when to seek additional verification.

2- Bias risks of generative AI

Generative AI’s potential for perpetuating or even amplifying biases is another significant concern. Similar to accuracy risks, as generative models are trained on a certain dataset, the biases in this set can cause the model to also generate biased content. AI biases can include sexist, racist, or ableist approaches within online communities.

Some bias risks of generative AI are:

  • Representation bias: If minority groups or viewpoints are underrepresented in the training data, the model may not produce outputs that are reflective of those groups or may misrepresent them.
  • Amplification of existing biases: Even if an initial bias in the training data is minor, the AI can sometimes amplify it because of the way it optimizes for patterns and popular trends.

For example, compared to a 117 million parameter model developed in 2018, a 280 billion parameter model created lately demonstrated an enormous 29% increase in toxicity levels. As these systems evolve into even greater powerhouses for AI research and development there is the potential for increased bias risks as well. You can see this trend in the figure below, where the toxicity level of generated responses increases with the increase in the parameter size.

The toxicity-related risks of generative AI is increasing with the model size.

Source: Stanford AI Index Report 20221

How to Mitigate:

  • Diverse training data can help reduce representation bias.
  • Continuous monitoring and evaluation of model outputs can help identify and correct biases.
  • Ethical guidelines and oversight for AI development and deployment can help in keeping biases in check.

3- Data privacy & security risks of generative AI

Generative AI technology, especially models trained on vast amounts of data, poses distinct risks concerning the privacy of sensitive data. Here are some of the primary concerns:

  1. Data leakage: Even if an AI is designed to generate new content, there’s a possibility that it could inadvertently reproduce snippets of training data. If the training data contained sensitive information, there’s a risk of it being exposed.
  2. Personal data misuse: If generative AI is trained on personal customer data without proper anonymization or without obtaining the necessary permissions, it can violate data privacy regulations and ethical standards.
  3. Data provenance issues: Given that generative models can produce vast amounts of content, it might be challenging to trace the origin of any specific piece of data. This can lead to difficulties in ascertaining data rights and provenance.

How to Mitigate:

Nonetheless, using generative models to create synthetic data is a good way of protecting sensitive data. Some steps to mitigate data security threats can be:

  • Differential privacy: Techniques like differential privacy can be employed during the training process to ensure that outputs of the model aren’t closely tied to any single input. This helps in protecting individual data points in the training dataset.
  • Synthetic training datasets: To mitigate the data security risks, generative models can be trained on synthetic data that are previously generated by AI models.
  • Data masking: Before training AI models, datasets can be processed to remove or alter personally identifiable information.
  • Regular audits and scrutiny: Regularly auditing AI outputs for potential data leakages or violations can help in early detection and rectification.

4- Intellectual property risks of generative AI

Generative AI poses various challenges to traditional intellectual property (IP) norms and regulations. Also, there are concerns around the eligibility of the AI generated content for copyright protection and infringement, which we discuss in our article in detail

These IP concerns are hard to address given the complex nature of AI generated content. For example, look at the Next Rembrandt painting in the figure below. It is hard to differentiate from an original Rembrandt painting.

"The Next Rembrandt" is a computer generated 3D painted painting which fed on the real paintings of 17th century Dutch painter Rembrandt. (Source: Guardian)

Source: Guardian2

Some of the primary risks and concerns of generative AI around intellectual property are:

  • Originality and ownership: If a generative AI creates a piece of music, art, or writing, who owns the copyright? Is it the developer of the AI, the user who operated it, or can it be said that no human directly created it and thus it’s not eligible for copyright? These are problematic concepts when talking about AI generation.
  • Licensing and usage rights: Similarly, how should content generated by AI be licensed? If an AI creates content based on training data that was licensed under certain terms (like Creative Commons), what rights apply to the new content?
  • Infringement: Generative models could unintentionally produce outputs that resemble copyrighted works. Since they’re trained on vast amounts of data, they might inadvertently recreate sequences or patterns that are proprietary.
  • Plagiarism detection: The proliferation of AI-generated content can make it more challenging to detect plagiarism. If two AI models trained on similar datasets produce similar outputs, distinguishing between original content and plagiarized material becomes complex.

How to Mitigate:

  • Clear guidelines and policies: Establishing clear guidelines on the use of AI for content creation and IP-related matters can help navigate this complex landscape.
  • Collaborative efforts: Industry bodies, legal experts, and technologists should collaborate to redefine IP norms in the context of AI.
  • Technological solutions: Blockchain and other technologies can be employed to track and verify the provenance and authenticity of AI-generated content.

5- Ethical risks of generative AI

Over the years, there has been a significant discourse on AI ethics. However, the ethical debate specifically surrounding generative AI is comparatively recent. This conversation has gained momentum with the introduction of various generative models, notably ChatGPT and DALL-E from OpenAI.

  • Deepfakes: The biggest ethical concern around generative AI is deepfakes. Generative models can now generate photorealistic images, videos and even sounds of persons. Such AI generated content can be difficult or impossible to distinguish from real media, posing serious ethical implications. These generations may spread misinformation, manipulate public opinion, or even harass or defame individuals. For example, the video below is a dramatic deepfake featuring Barack Obama.
  • Erosion of human creativity: Over-reliance on AI for creative tasks could potentially diminish the value of human creativity and originality. If AI-generated content becomes the norm, it could lead to homogenization of cultural and creative works.
  • Unemployment impact: If industries heavily adopt generative AI for content creation, it might displace human jobs in areas like writing, design, music, and more. This can lead to job losses and economic shifts that have ethical implications.
  • Environmental concerns: Training large generative models requires significant computational resources, which can have a substantial carbon footprint. This raises ethical questions about the environmental impact of developing and using such models.

How to Mitigate:

  • Stakeholder engagement: Engage with diverse stakeholders, including ethicists, community representatives, and users, to understand potential ethical pitfalls and seek solutions.
  • Transparency initiatives: Efforts should be made to make AI processes and intentions transparent to users and stakeholders. This includes watermarking or labeling AI-generated content.
  • Ethical guidelines: Organizations can develop and adhere to ethical guidelines that specifically address the challenges posed by generative AI.

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Cem Dilmegani
Principal Analyst

Cem is the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per Similarweb) including 60% of Fortune 500 every month.

Cem's work focuses on how enterprises can leverage new technologies in AI, automation, cybersecurity(including network security, application security), data collection including web data collection and process intelligence.

Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.

Cem's hands-on enterprise software experience contributes to the insights that he generates. He oversees AIMultiple benchmarks in dynamic application security testing (DAST), data loss prevention (DLP), email marketing and web data collection. Other AIMultiple industry analysts and tech team support Cem in designing, running and evaluating benchmarks.

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.

Sources: Traffic Analytics, Ranking & Audience, Similarweb.
Why Microsoft, IBM, and Google Are Ramping up Efforts on AI Ethics, Business Insider.
Microsoft invests $1 billion in OpenAI to pursue artificial intelligence that’s smarter than we are, Washington Post.
Data management barriers to AI success, Deloitte.
Empowering AI Leadership: AI C-Suite Toolkit, World Economic Forum.
Science, Research and Innovation Performance of the EU, European Commission.
Public-sector digitization: The trillion-dollar challenge, McKinsey & Company.
Hypatos gets $11.8M for a deep learning approach to document processing, TechCrunch.
We got an exclusive look at the pitch deck AI startup Hypatos used to raise $11 million, Business Insider.

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