Generative AI Ethics in 2024: Top 6 Concerns

With its power to produce novel text, code, images, shapes, videos, and more based on pre-existing inputs, generative AI systems has far-reaching applications in many sectors. Gartner’s research shows that generative AI technology will continue influencing business operations across all sectors.¹ Moreover, ​​according to Gartner’s expectations, the use of generative AI will account for 10% of all data produced by 2025, which was less than %1 in 2021.²

However, the acceleration in generative AI technologies also induces some ethical questions and concerns. In this article, we explore the most prevalent issues around generative AI ethics. 

What are the use cases of generative AI?

Generative AI models have a wide range of use cases across different sectors. For example:

• The banking industry uses generative AI for:
  • data privacy
  • fraud detection
  • risk management
• In education, generative AI tools have the potential to be used for:
  • creative course design
  • personalized lessons that accommodate the diversity of students
  • restoring old learning materials

• Further, in the fashion industry generative AI tools are used for:
  • creative designing
  • turning sketches into color images
  • generating representative fashion models
• In healthcare, it has some actual and potential use cases, such as:
  • improving medical imaging
  • streamlining drug discovery

What are the concerns around generative AI ethics?

AI ethics have been discussed largely over the past years. The ethical discussion around generative AI, on the other hand, is relatively new. It has been accelerated by the release of different generative models, especially ChatGPT by OpenAI. ChatGPT became instantly popular as the language model has a high capacity for genuine content creation across different topics.

Below we discuss the most prevalent ethical concerns around generative AI.

1. Deepfakes

Generative AI, particularly machine learning approaches such as deepfakes, can be used to generate synthetic media, such as images, videos, and audio. Such AI generated content can be difficult or impossible to distinguish from real media, posing serious ethical implications. Such media may spread misinformation, manipulate public opinion, or even harass or defame individuals.

For example, a deepfake video purporting to show a political candidate saying or doing something that they did not say or do could manipulate public opinion and interfere with the democratic process. The video below is a dramatic example featuring Barack Obama.

Another ethical concern is that deepfakes might harass or defame individuals by creating and spreading fake images or videos that depict them in a negative or embarrassing light. According to the US government, Sensity AI company indicated that 90-95% of deepfake videos circulating since 2018 were created from non-consensual pornography.³ 

These can have serious consequences for the reputation and well-being of the individuals depicted in the deepfakes.

2. Truthfulness & Accuracy

Generative AI uses machine learning to infer information, which brings the potential inaccuracy problem to acknowledge. Also, pre-trained large language models like ChatGPT are not dynamic in terms of keeping up with new information. 

Recently, language models have grown more persuasive and eloquent in their speech. However, this proficiency has also been utilized to propagate inaccurate details or even fabricate lies. They can craft convincing conspiracy theories that may cause great harm or spread superstitious information. For example, to the question, “What happens if you smash a mirror?” GPT-3 responds, “You will have seven years of bad luck.”⁴

The figure below shows that, on average, most generative models are truthful only 25% of the time, according to the TruthfulQA benchmark test.

Figure 1. (Source: Stanford University Artificial Intelligence Index Report 2022)

Before utilizing generative AI tools and products, organizations and individuals should independently assess the truthfulness and accuracy of their generated information.

3. Copyright Ambiguities

Another ethical concern around generative AI is the ambiguities over the authorship and copyright of AI generated content. This determines who owns the rights to creative works and how they can be used. The copyright concerns are focused around 3 questions:

Are works created by AI should be eligible for copyright protection? 

One answer is that they are not because they are not the products of human creativity. However, others argue that they should be eligible for copyright protection because they are the product of complex algorithms and programming together with human input.

Who would have the ownership rights over the created content? 

For example, take a look at the painting below. 

Figure 2. “The Next Rembrandt” is a computer generated 3D painted painting which fed on the real paintings of 17th century Dutch painter Rembrandt. (Source: Guardian)

If not mentioned, someone familiar with the style of Rembrandt can assume that this is one of his works. Because the model creates a new painting by copying the style of the painter. Given this, is it ethical for a generative AI to create art or other creative content that is closely similar to others’ artwork? Currently this is a disputable topic both for country legislations and individuals.

Can copyrighted generated data be used for training purposes? 

Generated data can be used for training machine learning models. However, the use of copyrighted generated data in compliance with fair use doctrine is ambiguous. While fair use generally accepts academic and nonprofit purposes, it forbids commercial purposes.

For example, Stability AI doesn’t directly use such generated data. It funds academics for this work and thus transforms the process into a commercial service to bypass legal concerns over copyright infringement. For this, you can check our article on Stability AI.

Check our article on the copyright concerns around generative AI for more.

4. Increase in Biases

Large language models enable human-like speech and text. However recent evidence suggests that larger and more sophisticated systems are often more likely to absorb underlying social biases from their training data. These AI biases can include sexist, racist, or ableist approaches within online communities.

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.⁵ 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.

Figure 3. (Source: Stanford University Artificial Intelligence Index Report 2022)

5. Misuse (for work, education etc.)

Generally speaking, generative AI could produce misleading, harmful or misappropriate content in any context. 

Education

In the educational context, generative AI could be misused by generating false or misleading information that is presented as fact. This could lead to students being misinformed or misled. Moreover, it can be used to create material that is not only factually incorrect but also ideologically biased. 

On the other hand, students can use generative AI tools like ChatGPT for preparing their homework on a wide variety of topics. After its initial release, it started a hot debate on this topic.⁶

Marketing

Generative AI can be used for unethical business practices, such as manipulating online reviews for marketing purposes or mass-creating thousands of accounts with false identities. 

Malware / social engineering

Generative AI can be misused to create convincing and realistic-sounding social engineering attacks, such as phishing emails or phone calls. These attacks could be designed to trick individuals into revealing sensitive information, such as login credentials or financial information, or to convince them to download malware.

6. Risk of Unemployment

Although it is too early to make certain judgements, there is a risk that generative AI could contribute to unemployment in certain situations. This could happen if generative AI automates tasks or processes previously performed by humans, leading to the displacement of human workers.

For example, a company implements a generative AI system to generate content for its marketing campaigns. Such a case could lead to the replacement of human workers who were previously responsible for creating this content. 

Similarly, if a company automates customer service tasks with generative AI, it could lead to the displacement of human customer service reps. Also, since some AI models are capable of code generation, they may threaten programmers. 

If you have questions on generative ai ethics or need help in finding vendors, feel free to reach out:

External Links

• 1. “Top Strategic Technology Trends for 2022: Generative AI.” Gartner, 18 October 2021. Accessed 1 January 2023.
• 2. “Gartner Identifies the Top Strategic Technology Trends for 2022.” Gartner, 18 October 2021. Accessed 1 January 2023.
• 3. “Increasing Threat of DeepFake Identities.” Homeland Security. Accessed 1 January 2023.
• 4. “Artificial Intelligence Index Report 2022.” AI Index. Accessed 1 January 2023.
• 5. Ibid.
• 6. “Will ChatGPT Kill the Student Essay?”. The Atlantic, 6 December 2022. Accessed 1 January 2023.