An Expert Identifies the Top 5 Risks of Generative AI

Generative AIs, like ChatGPT, have brought about a revolution in the way we interact with and perceive artificial intelligence. Tasks such as writing, coding, and job applications have become much simpler and faster due to the capabilities of generative AIs. However, despite all the advantages, there are some significant risks that need to be taken into consideration.

One of the most significant issues with AI is trust and security, and it has led some nations to ban ChatGPT altogether or to review their AI policies to safeguard users from harm.

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Top 5 Risks of Generative AI

Gartner analyst Avivah Litan has identified the most significant risks of generative AI, which include trust and security concerns such as hallucinations, deep fakes, data privacy, copyright issues, and cybersecurity problems as well.

1. Data Privacy

Generative AI poses a significant privacy concern since user data is typically stored for model training. This issue was the primary reason why Italy banned ChatGPT, as they alleged that OpenAI did not have legal permission to collect user data.

As per Litan, “Employees can inadvertently expose sensitive and confidential company information while engaging with generative AI chatbot solutions. These applications can store data indefinitely, captured through user inputs, and potentially use it to train other models, thereby risking the confidentiality of the information.”

2. Cybersecurity Vulnabriites

The refined abilities of generative AI models, such as coding, can also fall into the wrong hands, which raises concerns about cybersecurity.

Litan states that “Apart from sophisticated social engineering and phishing attacks, malicious programmers could exploit generative AI tools to create malicious code more easily.”

Litan highlights that even though traders of generative AI solutions may assure their customers that their models are trained to detect and reject malicious cybersecurity requests, end-users are often not provided with the means to verify all the security measures implemented.

3. Hallucinations

The term “hallucinations” refers to the errors that AI models make because they are not human and they depend on training and data to generate responses.

If you have used an AI chatbot, you may have experienced these “hallucinations” in the form of a response that misunderstands your prompt or provides an outright incorrect answer to your question.

Litan explains that biased or factually incorrect responses can result from the training data, and this can be a significant issue when people are relying on these bots for information.

According to Litan, “Training data can result in biased, inaccurate, or erroneous responses. However, detecting these issues can be challenging, especially as solutions become more believable and are increasingly relied upon.”

4. Deepfakes

A type of fraud known as deepfake involves the use of generative AI to produce faked videos, images, and audio recordings that bear a similarity to a real person.

Flawless instances have the AI-created viral picture of Pope Francis wearing a puffer jacket or the AI-generated song by Drake and the Weeknd, which crossed hundreds of thousands of streams.

According to Litan, these faked pictures, videos, and voice recordings have been used to target famous personalities and politicians, share misleading content, and even create fake accounts or gain unauthorized access to real ones.

Similar to Hallucinations, deepfakes can play a role in the general circulation of false information, resulting in the propagation of misinformation, a significant societal issue.

5. Copyright Infringement

The use of generative AI models in creating outputs is a reason for worry about copyright Infringement, owing to their training on vast amounts of internet data.

When generative AI models are being trained, they use a lot of data from the internet to learn and generate new outputs. This means that the AI can use material that was never meant to be shared or used to create new content. This increases worries about who owns the rights to the new content, as it may contain elements that were not licensed for use.

AI-powered tools like DALL-E use a vast library of photos they were trained on to generate an image based on a given Command.

However, this process might lead to the inclusion of certain elements or style aspects that belong to an artist but are not explicitly credited to them in the final output.

It is challenging to address copyright concerns because we are not always informed of the specific works that are used to train generative AI models. As a result, it is difficult to determine whether the generated output infringes on someone’s rights.

the emergence of generative AI has brought about several problems related to copyright, especially in the areas of art and content creation. With the potential to use vast quantities of data from the internet to train AI models, it can be difficult to manage copyright issues in a significant way. As technology continues to advance, it is important to think about these concerns and create plans that protect the rights of content creators while allowing for the creation in AI-generated art and content.

Conclusion

However, generative AI has huge potential to transform various industries, it also poses significant risks and challenges. As identified by the expert, the top five risks of generative AI include the potential for bias, deepfakes, copyright issues, security concerns, and Privacy concerns.

It is crucial for stakeholders to be aware of these risks and work towards developing responsible practices that promote the ethical and equitable use of generative AI. By managing these risks proactively, we can unlock the full potential of generative AI while reducing its negative impacts.

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