Best Practices for Ethical Use of Generative AI Technology

We live in an era where machines can do everything! They can produce art, codes, craft stories, and even break complex data and information in the simplest of language with just a prompt! Speak the right words, and voila, you have the desired output! However, there’s a condition! The machines work on already existing data of any field. They interpret that data; extract required information and present it to the command giver as their own. Oops, but that’s wrong, isn’t? This downright falls into the category of plagiarism. This can happen if there is an intellectual theft. And unfortunately, with the coming of generative AI, it already is happening!

With a complete revolutionizing of digital world, technology, and how we receive and process information, ethical use of generative AI technology and stringent laws to make sure this practise is followed everywhere is the need of the hour. To those wondering what generative AI is, it's scope, and AI ethical issue, scroll down for more information.

What is Generative AI?

It is a new model of technology that generates new content based on existing content. For example, if you give the prompt to Meta AI, or Chatgpt to draw the picture of a flower, the result produced will be based on pictures of flowers taken and stored till date on the internet. Unlike traditional AI systems that analyze data or make predictions, Generative AI can produce original outputs based on patterns it has learned from large datasets.

Being extremely fast-paced and quite accurate, the popularity of usage of generative AI has boomed, giving rise to many innovative applications transforming almost every industry. But, with great power, comes great responsibility! Dangers lurk nearby, and hence the need for protection, precaution, and prevention.

Scope of AI in the Modern World

Artificial Intelligence (AI) is transforming every major industry—from healthcare and education to finance and creative arts. Its ability to process vast data sets, recognize patterns, and make decisions is enabling innovations like self-driving cars, personalized medicine, intelligent virtual assistants, and predictive analytics. As AI continues to evolve, its role in enhancing productivity, automating repetitive tasks, and even supporting human creativity is only expected to grow. The scope of AI spans across future jobs, research, ethical governance, and cross-industry applications—making it one of the most promising technologies of the 21st century.

The Ethical Concerns of Generative AI

You may choose to ignore your parents or teachers when they warn you about the dangers of technology, but when the CEO of OpenAI, Sam Altman, himself sheds grave concerns over misuse of generative AI and the maddening usage of it, we all need to lend our ears. But what are these ethical concerns and how can they impact us? Are they already impacting us? What can we do to ensure full safety?

1. Distribution of Harmful Information and Content

The structures of our society have been built on stones of biasness, prejudice, discrimination, domination, and violence. Naturally, the training data used for generative AI is also infringed with above mentioned barriers. Harmful contents like DeepFakes can ruin anybody’s life within seconds with fake images, text, videos, voice messages, and more. Through usage of your personal information that’s stored anywhere on the internet, the scammers can use generative AI to create deeply disturbing, and harmful content to exploit you mentally and financially.

2. Copyright and Legal Exposure

We all are aware of the Ghibli art controversy where Open AI’s recent 4o Image generation feature transformed real life pictures of men, women, pets, landscapes into Ghibli art pictures, based on existing data of Ghibli art. But what went wrong was the Open AI company never took permission from Ghibli art studio and its maker, making the entire episode infamous for copyright infringement and intellectual property theft.

Sure, 150 million people flocked to Chatgpt to generate Ghibli-art style pictures of them to post and show around, but at what cost?

3. Data Privacy Violations

The moment we upload our pictures, phone numbers, videos, documents, signatures on the internet, we lose complete control over it. Any person adept in theft and data privacy violations can illegally access and use them.  
Another controversy surrounding Ghibli art and Open AI legal battle, is the concern that Open AI can misuse the voluntarily uploaded pictures of people for training purposes, and other activities. These uploads contain never-before-seen images, such as intimate pictures, family portraits, and private moments that might not have been posted online. Because of this, OpenAI has exclusive access to the original photos, whilst rivals can only view the modified versions.

4. Sensitive Information Disclosure

Remember the time, when there was a cyber security attack on Dominos, and the data of all its customers (their phone numbers, address, order history, and more) was leaked online? Breach of data and leak of sensitive information is now an often-recurring event.

Many a time, we ourselves give the consent for misuse by uploading sensitive information on generative AI platforms. If an employee uploads confidential information, such as a contract, software product source code, or private data, among other things, the risks are greatly increased. The consequences can be serious, harming the organization's finances, reputation, or legal standing; for this reason, it is imperative that a clear data security strategy be in place. 

5. Amplification of Existing Bias

If a model is based on the training data that’s prejudiced against a particular community, gender, sexuality, religion, nationality, or anything, the outcome of that technological model will always be fraught with amplified biases.  
For examples, in recent times, a model used by US Police department was heavily criticised because the generative images of criminals or individuals it thought of being more prone to committing crimes was prejudiced against the black community, the Latin community, and other minorities.

This heavily questions what information we are getting, how that information is being produced, and what consequences there can be of usage of that information.

Famous Examples of AI-Generated Misinformation

AI tools have also been misused to spread misinformation. For instance:

• Pope in a puffer jacket: A hyperrealistic image of Pope Francis in a Balenciaga-style coat, created by AI, went viral on social media—many believed it was real.
• Fake arrest of Donald Trump: AI-generated images falsely showing Donald Trump being arrested sparked confusion before being debunked.
• Deepfake videos of Politicians: Videos showing fabricated speeches or altered facial movements of leaders like Barack Obama and Vladimir Putin have been created using AI-powered deepfake technology.

These incidents highlight the ethical challenges and the urgent need for robust fact-checking and AI governance. 

What are some ethical considerations when using Generative AI?

There is no doubt of the immense capabilities of generative AI when used ethically and for the betterment of society. It is fast paced, effective, expansive, and revolutionary. When used in legal, controlled, and regulated manner, it is a brilliant human tool to minimise the harmful impacts of it and help improve results and outcomes.

As generative AI technologies become more powerful and widespread, it is essential to ensure their development and deployment align with responsible, ethical standards.

1. Transparency and explainability:

Developers and organizations must indicate when content is AI-generated—be it text, imagery, audio, or video. Where feasible, AI systems should also provide traceable logic or justifications, especially in high-stakes decision-making contexts. This helps users understand the capabilities, limitations, and intended use of the system.

2. Fairness and non-discrimination:

Generative AI models must be trained on diverse and representative datasets to avoid perpetuating harmful biases related to race, gender, culture, or socio-economic status. Regular auditing and testing should be conducted to detect and mitigate algorithmic bias, ensuring equitable outcomes and avoiding the marginalization of vulnerable groups.

3. Accountability and human oversight

AI systems, particularly in sensitive domains like healthcare, education, and finance, must include human-in-the-loop mechanisms. Clear documentation of training data, model architecture, and deployment decisions is important, and institutions must designate responsibility for managing AI outcomes.

4. Data privacy and informed consent

Generative AI should only use data that has been ethically sourced with explicit consent, and the systems must not produce outputs that compromise individual privacy. Implementing techniques like differential privacy and federated learning can further enhance data protection.

5. Strong safeguards into generative AI

Rigorous testing for vulnerabilities—such as adversarial prompts, phishing potential, or disinformation generation—is essential. Content moderation, prompt filtering, and user feedback loops are key components of a secure deployment.

6. Consideration of environmental impact

Training large models consumes significant energy. Developers should aim for energy-efficient architectures, use green data centres, and adopt practices like model distillation to reduce carbon footprints.

Ethical use cases should be prioritized. Generative AI should not be deployed in contexts that pose high risks to public safety, civil liberties, or democratic processes. Instead, its application should focus on social good—such as enhancing accessibility, supporting mental health, combating climate change, or advancing education on AI.

Finally, users should have informed consent and control when engaging with generative AI. This includes the ability to opt out, correct or delete personal data, and report problematic outputs. User empowerment through choice and transparency helps build trust and aligns AI with human-centric values.

By following these best practices, we can ensure that generative AI technologies are used responsibly, ethically, and for the benefit of all.

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Conclusion

Generative AI is already here and is here to stay! With immense possibilities, comes the need to be extra cautious, both from individual and organizational point of view. It is imperative to be aware of what are some ethical considerations when using generative AI and adopt ethical practices to make sure the journey to technological advancement is smooth and trustworthy.