Generative AI in Focus: Ethical Considerations and Unethical Practices

Generative AI is the new topic in town, you can think of it as a digital wizard that can create text, images, music and more. It operates using machine learning models, in particular deep learning algorithms. These algorithms analyze vast amounts of data to identify patterns and relationships. So, we can say that the applications of generative AI are diverse. With such diverse applications there comes certain boundaries , which has to be labelled  as ethical and unethical. Let’s dive deep into the entire concept of Generative AI and understand what’s ethical, what’s not.

The Ethical Gains From Generative AI

Gen AI has without a doubt emerged as a transformative technology across various sectors. These includes business, social networking, healthcare, education and art. Let’s look at some ethical implications of Gen AI closely and analyze how Gen AI is used with real-life examples.

Boosting Organisational Productivity 

Generative AI can be tailored to suit different domains, allowing it to be utilized for a diverse array of tasks. Some notable applications include chatbots, content generation, and language translation, all of which contribute to increased organizational productivity.

 Gen AI can engage in human-like conversations, providing answers to questions in a natural and intuitive manner. This capability significantly reduces the effort spent on repetitive tasks, freeing up employees to focus on more strategic initiatives. Additionally, it can distill complex topics into accessible summaries, making information easier to digest for various audiences.

For instance, a global marketing team can use Vertex AI to localize their campaigns for different regions without losing their brand voice. By automating the translation process, organizations can enhance communication with international audiences.

Enhanced Content Creation

Generative AI allows various businesses to produce high quality materials quickly and efficiently. This not only allows us to utilize time and manage tasks effectively, but also opens the door for creativity in the content. For example, if there is a task to prepare a pitching presentation for your investors. Now, you can either choose the traditional way and start gathering content, choosing templates , or you can use Gen AI like Tome.

Tome will create engaging presentations with minimal effort. You just have to add your prompt where you can guide Tome on how you want your presentation to look. This capability will also allow you to modify and create creative presentation designs.

Personalised Interaction

Generative AI models have the ability to remember previous interactions, which enhances the consistency and relevance of conversations for users. This capability allows users to customize their experience by asking models to recall their writing style, questions they asked before etc. 

For example, while solving a question using AI, the AI will automatically suggest relevant answers based on previous responses after one or two questions. Check out our other informative articles on Generative AI and it’s techniques (From LLM’s to Hugging Face) – Click here

Advantage for Social Media

Businesses that use generative AI tools like Jasper have seen a significant boost in social media engagement. When we talk about social media, with 78% of Australian firms noting improved impressions and interactions after adopting AI-generated content. This shows how generative AI not only enhances productivity by reducing the time spent on content creation but also drives better results in terms of audience engagement across various social media channels.

The Unethical Side of Generative AI

Generative AI poses a range of ethical concerns and risks of misuse. This can lead to solid challenges for society and organisations. Here are some of the primary unethical applications of generative AI

Trust and Authenticity Challenges

Generative AI can produce various information that seems to be legit but is often misleading or false. In technical terms, we call this AI hallucinations. Therefore, even if these models appear to comprehend the data they process and create, they lack actual understanding.

Moreover, we know that these models train on vast datasets, and datasets often contain biases. This in turn affects the reliability of the outputs. If Gen AI can create creative content, then it can surely create destructive content that includes fake news, misinformation, and deep fakes.

Copyright Infringement

• Inadvertent Reproduction: Generative AI may reproduce copyrighted material when generating new content, risking copyright infringement.

• Ownership Ambiguity: Questions arise about who owns the rights to AI-generated works—users, developers, or the AI itself—complicating traditional copyright frameworks.

• Need for Legislative Reform: There is a growing demand for updates to copyright laws to address the challenges posed by generative AI and clarify fair use provisions.

• Impact on Businesses: Companies using generative AI outputs risk legal repercussions. If the generated content looks like protected works without permission then there may be a lawsuit.

Risks of Feedback Loops in Generative AI

The widespread use of generative AI outputs on the internet can create problematic feedback loops that threaten the integrity of future models. When new AI models train themselves on content produced by their previous versions, they often absorb the biases and inaccuracies found in that earlier output. This recursive process can result in a phenomenon known as model collapse. Where newer models become increasingly detached from the foundational data they were originally trained on.

As this cycle continues, the quality of AI-generated content may decline, leading to effect where errors and biases become more pronounced. This not only undermines the reliability of the models but also raises concerns about the spread of misinformation. Ultimately, without intervention, these feedback loops can diminish the effectiveness of generative AI systems. Which will obstruct their ability to provide accurate and diverse outputs.

Risks of AI Voice Cloning in Scam

Gen AI enables fraudsters to impersonate individuals and scam people online. Fraudsters can mimic an individual’s voice using Voice Cloning, which involves using a small sample available on the internet, such as from social media handles or call recordings. Once obtained, the fraudster may use this sample to extract money or sensitive information.

In an incident involving Florida man Jay Shooter, parents were nearly scammed out of $30,000 when a fraudster used AI voice to mimic his voice. The fraudster tricked Shooter’s parents into believing he was in trouble and needed bail money after a car accident. Therefore, during this peak of AI technology, it is essential to remain vigilant and ask questions.

Developers at the Ethical Forefront

Developers are now starting to prioritize ethical considerations in their work, due to the unethical applications of AI. Although this consideration is still in its early stages, it marks the beginning of a shift towards ethical usage of AI. One of the finest examples is Google’s AI principles, which focus on fairness, accountability, and transparency in the development of new AI technologies.

The evolving practices of ethical usage can be seen in one of the famous AI models – ChatGPT, which avoids generating harmful and unethical content. When users ask for dangerous information or request assistance for some suspicious task, these systems are designed not to provide responses. Visit Open AI’s safety guidelines.

Developers are also looking after tackling abusive information and guiding against any malpractice during exams. However, developers face issues when it comes to achieving fully ethical AI due to bias in data, transparency in decision-making processes, and ensuring accountability.

Conclusion

Generative AI holds immense potential to reform various sectors by enhancing creativity, efficiency, and productivity. However, its rapid adoption also brings significant ethical challenges and risks that must be addressed. Issues such as lack of trust due to misinformation, copyright infringement, environmental impact, and the dangers of feedback loops highlight the complexities involved in deploying this technology responsibly.