Are we obsessed with the negatives of Generative AI? (Part 01)

Anand Jagadeesh
4 min readFeb 2, 2024


Why don’t generative AI go to parties? Because they heard people can’t stop talking about their “negative” side!

Generative Artificial Intelligence (GenAI)

Generative AI, a subset of artificial intelligence, has been making waves in the tech world and beyond. It’s a technology that leverages machine learning techniques to generate information similar to what it was trained on. This could be anything from a piece of music, a poem, or even a scientific article. The possibilities are endless and the results, are often astonishing.

However, like any powerful tool, Generative AI comes with its own set of challenges and concerns. While it holds the potential to revolutionize numerous fields, from art to healthcare, from entertainment to academia, and whatnot, it also raises important questions about authenticity, security, and ethics.

Let us try to delve deeper into these issues, examining whether our focus has become disproportionately skewed towards the negatives of Generative AI, with some criticisms, analyze why society might be obsessed with these negatives, and argue for more balanced narratives.

The Negative Pole

Let us explore some common criticisms and issues raised by recent discussions once ChatGPT and other GenAI models were released:

Misinformation and Manipulation

Generative AI, particularly in the realm of deepfakes, are used to generate highly convincing fabricated content, leading to the dissemination of misinformation, defamation, and manipulation of public perception. This is not in the future! It’s already here! Why is this a problem? Though companies like Google introduce systems that can intelligently replace faces in pictures and videos, and claim to self-regulate, not every player is so ethical! These threaten the reliability of information and challenge the authenticity of digital content.

Bias Reinforcement

Generative models are trained on extensive datasets that may inherently contain societal biases. The risk of perpetuating and amplifying these biases in various applications, such as hiring processes and content recommendations, raises concerns about fairness and equity. I tried to explain this problem simply (I guess! Not sure if I succeeded).

Ethical Concerns

The potential for the unethical use of generative AI, such as creating fraudulent content for malicious purposes, raises ethical dilemmas. Striking a balance between technological advancement and responsible use becomes a critical challenge.

Cybersecurity Risks

The technology may be exploited by malicious actors for cyberattacks, including the creation of convincing phishing schemes and manipulation of authentication systems. The continuous development of countermeasures is crucial to stay ahead of potential threats in the cybersecurity landscape. Here is a recent NCSC article on this topic that would provide quite a bunch of information.

Economic Disruption

The widespread adoption of generative AI, especially in creative fields, could lead to economic disruptions and job market changes. Everything from simple website designs to copyrighting could get disrupted. Concerns about unemployment and the need to adapt the workforce to evolving skill demands become significant considerations soon.

Privacy Issues

Generative AI’s ability to generate realistic and sensitive content raises privacy concerns, as individuals may become victims of identity theft or unauthorized use of personal information. Striking a balance between innovation and protecting individual privacy is crucial for responsible deployment.

Legal and Regulatory Challenges

The rapid evolution of generative AI outpaces the development of comprehensive legal frameworks and regulations. The absence of clear guidelines may hinder effective governance and increase the risk of misuse or unintended consequences. We see these concerns raised across the world. A good recent example would be the discussions around the EU AI Act.

Algorithmic Accountability

Generative AI systems often operate as black boxes, making it challenging to trace decision-making processes and hold algorithms accountable for their outputs. The need for transparency and accountability in algorithmic decision-making becomes a pressing issue.

Environmental Impact

Training sophisticated generative models requires significant computational power, contributing to the carbon footprint of AI development. Exploring sustainable approaches and optimizing energy consumption is crucial to mitigate the environmental impact of generative AI.

Are we obsessed with the negatives?

The preoccupation with the downsides of Artificial Intelligence (AI) within society is rooted in a combination of psychological predispositions and the influence of media and popular culture. Human instinct tends to prioritize potential threats, making negative aspects of AI more salient and memorable.

Media outlets, aiming for attention-grabbing narratives, often emphasize the risks and potential harms associated with AI, contributing to a perception that leans towards apprehension rather than appreciation of its benefits. Dystopian depictions of AI in popular culture, often characterized by scenarios of rogue machines and loss of control, further shape public sentiment.

This portrayal reinforces negative stereotypes and fuels anxieties surrounding the technology. In navigating the discourse around AI, it is essential to balance caution with informed optimism, recognizing the potential for positive advancements while addressing legitimate concerns and fostering a more nuanced understanding within society.

Let us explore the other side in part 02!



Anand Jagadeesh

⌨ Writes about: ⎇DevOps, 🧠ML/AI, 🗣️XAI & 💆Interpretable AI, 🕸️Edge Computing, 🌱Sustainable AI | 💼Global Grad. @ VCC | 🎓MSc AI @ UoStA '22 |