The Battle for Authenticity: The Importance of Detecting Human vs. AI-Generated Content by Intetics

Intetics, a leading global technology company, published an article on the burning challenges of generative AI, existing AI-generated content detection tools, their current limitations and prospects, and the possible effect of modern sophisticated AI tools on human work.

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AI has been around for longer than one might think, without humans even noticing it. If someone doubt that they should probably think about Netflix’s recommendations for TV shows and series, Instagram’s feed personalization, Amazon’s suggestion for accompanying purchases, and text editors that complete phrases.

All of those are examples of AI-powered features. The community didn’t care if it was AI prediction, human recommendation, or a remarkable coincidence until the recent quantum leap in generative AI. The spark turned into a flame after the public release of ChatGPT.

The astonishingly skilled bot, as well as the recently revealed GPT-4, can mimic authentic writing, images, and coding, providing compelling content and significantly lowering marketing costs for businesses. While the tech revolutionized the way businesses operate within a couple of months, it raised concerns about copyright, authenticity, privacy, and ethics. Therefore, there is now a need to detect AI-generated content, in order to control smart machines.

Generative AI as a Threat to Authenticity, Security, and Ethics

As a result of the huge public exposure around generative AI, its potential risks have been raised, especially copyright issues. Identifying AI-generated content has become a burning discussion point among educators, employers, researchers, and parents.

While researchers and programmers have actively taken on the task of developing tools to help differentiate between human-created and machine-generated content, even the most advanced solutions struggle to keep pace with this breakneck technology’s progress.

This urged some organizations to prohibit AI-generated content. For instance, a world-famous supplier of stock images, Getty Images, some art communities, and even New York City’s Department of Education have banned ChatGPT in public schools.

The questionable positive impact of generative AI, together with increased privacy risks and ethical considerations, turned out to be much more severe than expected even by the tech community. On March 29, Elon Musk, Steve Wozniak, and 1000+ other tech industry leaders signed an open letter to pause AI experiments. The petition seeks to protect humans from the “profound risks to society and humanity” that AI systems with human-competitive intelligence can provoke — from flooding the internet with disinformation and automating away jobs to more catastrophic future risks emphasized in science fiction.

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To be more precise, the potential AI-related hazards are the following:

• Bias and discrimination: Chatbots may reinforce and amplify existing prejudices and discrimination if they are trained on biased data.
• Misinformation: AI language models can produce inaccurate or misleading information if not adequately trained or supervised.
• Privacy and security: AI tools can access and analyze vast amounts of sensitive personal information, raising concerns about privacy and security.
• Job displacement: Being able to automate simple repetitive tasks, AI can replace human workers, potentially leading to job displacement.
• Lack of accountability: AI language models can generate inappropriate or harmful content, but holding individuals or organizations responsible for their actions can be challenging. Furthermore, AI can impersonate people for more effective social engineering cyber-attacks.

Given that, risks associated with generative AI underscore the significance of responsible AI development and deployment — and the need for ethical guidelines and regulations to mitigate the potential adverse effects of AI language models.

Some issues are already addressed by legislative authorities. For example, one California bill requires companies and governments to inform affected persons in order to increase AI usage transparency. Such a bill aims to prevent non-compliant data practices—such as those applied by facial recognition company Clearview AI, which was fined for breaching British privacy laws. The company had processed personal data without permission while training its models on billions of photos scraped from social media profiles. In this scenario, evaluating the ethics of data collection and storage practices could have highlighted a lack of privacy safeguards and averted the resulting poor publicity.

One Pennsylvania bill would require organizations to register systems that use AI in important decision-making, as well as to provide info on how algorithms were used. This information will help objectively evaluate AI algorithms and assess bias. In the future, such initiatives will help to eliminate racial bias. One such example of AI-powered racial bias was when Black patients were assigned a lower level of risk than equally sick White patients. This happened because the algorithm used health costs as a proxy for health needs, leading to less money being spent on Black patients who have the same level of need as White patients.

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