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In the rich history of humanity, the advent of artificial intelligence (AI) has added a new, delicate aspect. The aspect of promising technological advancement has the potential to either enrich the nest of our society or destroy it entirely. The latest straw in this complex nest is generative AI, a frontier teeming with both potential and perils. It is a realm where the ethereal concepts of cyber peace and resilience are not just theoretical constructs but tangible necessities.

The spectre of generative AI looms large over the digital landscape, casting a long shadow on the sanctity of data privacy and the integrity of political processes. The seeds of this threat were sown in the fertile soil of the Cambridge Analytica scandal of 2018, a watershed moment that unveiled the extent to which personal data could be harvested and utilized to influence electoral outcomes. However despite the indignation, the scandal resulted in meagre alterations to modus operandi of digital platforms.

Fast forward to the present day, and the spectre has only grown more ominous. A recent report by Human Rights Watch has shed light on the continued exploitation of data-driven campaigning in Hungary's re-election of Viktor Orbán. The report paints a chilling picture of political parties leveraging voter databases for targeted social media advertising, with the ruling Fidesz party even resorting to the unethical use of public service data to bolster its voter database.

The Looming Threat of Disinformation

As we stand on the precipice of 2024, a year that will witness over 50 countries holding elections, the advancements in generative AI could exponentially amplify the ability of political campaigns to manipulate electoral outcomes. This is particularly concerning in countries where information disparities are stark, providing fertile ground for the seeds of disinformation to take root and flourish.

The media, the traditional watchdog of democracy, has already begun to sound the alarm about the potential threats posed by deepfakes and manipulative content in the upcoming elections. The limited use of generative AI in disinformation campaigns has raised concerns about the enforcement of policies against generating targeted political materials, such as those designed to sway specific demographic groups towards a particular candidate.

Yet, while the threat of bad actors using AI to generate and disseminate disinformation is real and present, there is another dimension that has largely remained unexplored: the intimate interactions with chatbots. These digital interlocutors, when armed with advanced generative AI, have the potential to manipulate individuals without any intermediaries. The more data they have about a person, the better they can tailor their manipulations.

Root of the Cause 

To fully grasp the potential risks, we must journey back 30 years to the birth of online banner ads. The success of the first-ever banner ad for AT&T, which boasted an astounding 44% click rate, birthed a new era of digital advertising. This was followed by the advent of mobile advertising in the early 2000s. Since then, companies have been engaged in a perpetual quest to harness technology for manipulation, blurring the lines between commercial and political advertising in cyberspace.

Regrettably, the safeguards currently in place are woefully inadequate to prevent the rise of manipulative chatbots. Consider the case of Snapchat's My AI generative chatbot, which ostensibly assists users with trivia questions and gift suggestions. Unbeknownst to most users, their interactions with the chatbot are algorithmically harvested for targeted advertising. While this may not seem harmful in its current form, the profit motive could drive it towards more manipulative purposes.

If companies deploying chatbots like My AI face pressure to increase profitability, they may be tempted to subtly steer conversations to extract more user information, providing more fuel for advertising and higher earnings. This kind of nudging is not clearly illegal in the U.S. or the EU, even after the AI Act comes into effect. The market size of AI in India is projected to touch US$4.11bn in 2023. 

Taking this further, chatbots may be inclined to guide users towards purchasing specific products or even influencing significant life decisions, such as religious conversions or voting choices. The legal boundaries here remain unclear, especially when manipulation is not detectable by the user. 

The Crucial Dos/Dont's 

It is crucial to set rules and safeguards in order to manage the possible threats related to manipulative chatbots in the context of the general election in 2024.

First and foremost, candor and transparency are essential. Chatbots, particularly when employed for political or electoral matters, ought to make it clear to users what they are for and why they are automated. By being transparent, people are guaranteed to be aware that they are interacting with automated processes.

Second, getting user consent is crucial. Before collecting user data for any reason, including advertising or political profiling, users should be asked for their informed consent. Giving consumers easy ways to opt-in and opt-out gives them control over their data.

Furthermore, moral use is essential. It's crucial to create an ethics code for chatbot interactions that forbids manipulation, disseminating false information, and trying to sway users' political opinions. This guarantees that chatbots follow moral guidelines.

In order to preserve transparency and accountability, independent audits need to be carried out. Users might feel more confident knowing that chatbot behavior and data collecting procedures are regularly audited by impartial third parties to ensure compliance with legal and ethical norms.

Important "don'ts" to take into account. Coercion and manipulation ought to be outlawed completely. Chatbots should refrain from using misleading or manipulative approaches to sway users' political opinions or religious convictions.

Another hazard to watch out for is unlawful data collecting. Businesses must obtain consumers' express agreement before collecting personal information, and they must not sell or share this information for political reasons.

At all costs, one should steer clear of fake identities. Impersonating people or political figures is not something chatbots should do because it can result in manipulation and false information.

It is essential to be impartial. Bots shouldn't advocate for or take part in political activities that give preference to one political party over another. In encounters, impartiality and equity are crucial.

Finally, one should refrain from using invasive advertising techniques. Chatbots should ensure that advertising tactics comply with legal norms by refraining from displaying political advertisements or messaging without explicit user agreement.

Present Scenario

As we approach the critical 2024 elections and generative AI tools proliferate faster than regulatory measures can keep pace, companies must take an active role in building user trust, transparency, and accountability. This includes comprehensive disclosure about a chatbot's programmed business goals in conversations, ensuring users are fully aware of the chatbot's intended purposes.

To address the regulatory gap, stronger laws are needed. Both the EU AI Act and analogous laws across jurisdictions should be expanded to address the potential for manipulation in various forms. This effort should be driven by public demand, as the interests of lawmakers have been influenced by intensive Big Tech lobbying campaigns.

At present, India doesn’t have any specific laws pertaining to AI regulation. Ministry of Electronics and Information Technology (MEITY), is the executive body responsible for AI strategies and is constantly working towards a policy framework for AI. The Niti Ayog has presented seven principles for responsible AI which includes equality , inclusivity, safety, privacy, transparency, accountability, dependability and protection of positive human values.

Conclusion 

We are at a pivotal juncture in history. As generative AI gains more power, we must proactively establish effective strategies to protect our privacy, rights and democracy. The public's waning confidence in Big Tech and the lessons learned from the techlash underscore the need for stronger regulations that hold tech companies accountable. Let's ensure that the power of generative AI is harnessed for the betterment of society and not exploited for manipulation.

Reference

McCallum, B. S. (2022, December 23). Meta settles Cambridge Analytica scandal case for $725m. BBC News. https://www.bbc.com/news/technology-64075067

Hungary: Data misused for political campaigns. (2022, December 1). Human Rights Watch. https://www.hrw.org/news/2022/12/01/hungary-data-misused-political-campaigns

Statista. (n.d.). Artificial Intelligence - India | Statista Market forecast. https://www.statista.com/outlook/tmo/artificial-intelligence/india

Introduction

The global food industry is vast and complex, influencing consumer behaviour, policy, and health outcomes worldwide. However, misinformation within this sector is pervasive, with significant consequences for public health and market dynamics. Misinformation can arise from various sources, including misleading marketing campaigns, unsubstantiated health claims, and misrepresentation of food production practices through public endorsement or otherwise. Nutrition misinformation is one such example. The promotion of false or unproven products for profit can lead to mislead consumers and affect their interests. Misleading claims and inaccurate information about the nutritional value of food products and processes are common claims. The misinformation created about food on the global stage distorts public understanding of nutrition, food safety, and environmental impacts, leading to significant consequences for public health, consumer trust, and the economy.

Rise of Nutritional Misinformation and Consumer Distrust

Health and nutrition-related misinformation is one of the most prevalent types in the food sector. Businesses frequently advertise their products as "natural" or "healthy" without providing sufficient data to back up these claims, tricking customers into buying goods that might be heavy in fat, sugar, or salt. Words like "superfood" are frequently used without supporting evidence from science, giving the impression that they are healthier.

Misinformation also impacts the sustainability and ethics of food production. Claims of "sustainable" or "ethical" sourcing are frequently exaggerated or fabricated, leaving consumers unaware of the true environmental and social costs associated with certain products.

This lack of clarity is not only observed in general food trends but also within organisations meant to provide trustworthy information. There has been significant criticism, directed at the International Food Information Council (IFIC), for their alleged promotion of nutrition-based misinformation to safeguard the interests of large food corporations, resulting in potentially compromising public health. The preemptive claims that IFIC made about the nutritive claims have been questioned by the National Institutes of Health, USA in November 2022. They reported in their study that IFIC promotes food and beverage company interests and undermines the accurate dissemination of scientific evidence related to diet and health. This was in support of the objective of the study, which was to determine whether, there have been many claims that the nutritional value of certain foods or diets may be manipulated to favour business goals, leaving consumers misinformed about what constitutes a truly healthy diet.

Another source of misinformation is the growing ‘Free-From’ fad. The “free-from” label in the US is a food category of products that claim to be free from certain ingredients or chemicals. It has been steadily growing by 7% annually. These labels often tout products as healthier due to a simpler ingredient list. Although seemingly harmless, transparency in ingredient disclosure is often obscured in the 'free-from' trend. This can lead to consumer distrust in the long run, making them hesitant.

The Harmful Effects of Food Misinformation

The effects of misinformation about nutrition and food safety can directly affect public health.
Consumers unknowingly may accept false claims or avoid certain foods without scientific basis and adopt harmful dietary habits, potentially leading to malnutrition or other health problems. By the time the realisation sets in about being misled, their trust is eroded not only towards such companies but also towards the regulators. This distrust can lead to declining consumer confidence and disrupt market stability. 

Some food-related misinformation downplays the environmental impact that certain food production practices have. An example of such a situation is the promotion of meat alternatives as being entirely eco-friendly without considering all environmental factors. This can mislead consumers and obscure the complex environmental effects of food production systems. 

Misinformation can distort consumer purchasing habits, potentially leading to a reduced demand for certain products and unfair competition. The sufferers in this case are the small-scale producers who suffer disproportionately, while the large corporations might use this misinformation to maintain their dominance in the market. Regulatory checks, open communication, and public education campaigns are needed to combat mis/disinformation in the global food sector and enable consumers to make decisions that are sustainable, healthful and informed.

CyberPeace Recommendations

• Unfair trade practices like providing misleading information or unchecked claims on food products should be better addressed by the regulators. Companies must provide clear, transparent and accurate information about their products as mandated under the Food Safety and Standards (Advertising and Claims) Regulations, 2018. This information should include the true origins, production methods, and nutritional content on their labels.
• Promotions of initiatives and investments by public health organisations and food authorities towards educating consumers and improving food literacy should encouraged. 
• Regulating social media endorsement is also crucial to prevent the spread of misinformation and unchecked claims. Without proper due diligence on product details, influencers may unknowingly mislead their audience, causing potential harm. 
• The Social Media Platforms can partner with nutritionists, dietitians, and other health professionals who are content creators, as they can help in understanding and promoting accurate, science-based nutrition information and debunk any misleading claims.
• Campaigns should be encouraged to spread public awareness about the harms of food-related misleading claims or trends. Emphasis should be on evidence-based nutritional guidance. The ongoing research towards food safety, nutrition, and true information should be actively communicated to keep the public informed. Combating food misinformation requires more robust regulations, improved transparency, and heightened consumer awareness and vigilance.

References

• https://timesofindia.indiatimes.com/india/label-claims-on-packaged-food-could-be-misleading-icmr/articleshow/110053363.cms 
• https://www.outlookindia.com/hub4business/empowering-change-freedom-food-alliance-takes-on-global-food-industry-misinformation 
• https://insightsnow.com/misinformation-hurting-food-business/ ‍
• https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9618198/pdf/12992_2022_Article_884.pdf

As AI language models become more powerful, they are also becoming more prone to errors. One increasingly prominent issue is AI hallucinations, instances where models generate outputs that are factually incorrect, nonsensical, or entirely fabricated, yet present them with complete confidence. Recently, ChatGPT released two new models—o3 and o4-mini, which differ from earlier versions as they focus more on step-by-step reasoning rather than simple text prediction. With the growing reliance on chatbots and generative models for everything from news summaries to legal advice, this phenomenon poses a serious threat to public trust, information accuracy, and decision-making.

What Are AI Hallucinations?

AI hallucinations occur when a model invents facts, misattributes quotes, or cites nonexistent sources. This is not a bug but a side effect of how Large Language Models (LLMs) work, and it is only the probability that can be reduced, not their occurrence altogether. Trained on vast internet data, these models predict what word is likely to come next in a sequence. They have no true understanding of the world or facts, they simulate reasoning based on statistical patterns in text. What is alarming is that the newer and more advanced models are producing more hallucinations, not fewer. seemingly counterintuitive. This has been prevalent reasoning-based models, which generate answers step-by-step in a chain-of-thought style. While this can improve performance on complex tasks, it also opens more room for errors at each step, especially when no factual retrieval or grounding is involved.

As per reports shared on TechCrunch, it mentioned that when users asked AI models for short answers, hallucinations increased by up to 30%. And a study published in eWeek found that ChatGPT hallucinated in 40% of tests involving domain-specific queries, such as medical and legal questions. This was not, however, limited to this particular Large Language Model, but also similar ones like DeepSeek. Even more concerning are hallucinations in multimodal models like those used for deepfakes. Forbes reports that some of these models produce synthetic media that not only look real but are also capable of contributing to fabricated narratives, raising the stakes for the spread of misinformation during elections, crises, and other instances.

It is also notable that AI models are continually improving with each version, focusing on reducing hallucinations and enhancing accuracy. New features, such as providing source links and citations, are being implemented to increase transparency and reliability in responses.

The Misinformation Dilemma

The rise of AI-generated hallucinations exacerbates the already severe problem of online misinformation. Hallucinated content can quickly spread across social platforms, get scraped into training datasets, and re-emerge in new generations of models, creating a dangerous feedback loop. However, it helps that the developers are already aware of such instances and are actively charting out ways in which we can reduce the probability of this error. Some of them are:

1. Retrieval-Augmented Generation (RAG): Instead of relying purely on a model’s internal knowledge, RAG allows the model to “look up” information from external databases or trusted sources during the generation process. This can significantly reduce hallucination rates by anchoring responses in verifiable data.
2. Use of smaller, more specialised language models: Lightweight models fine-tuned on specific domains, such as medical records or legal texts. They tend to hallucinate less because their scope is limited and better curated.

Furthermore, transparency mechanisms such as source citation, model disclaimers, and user feedback loops can help mitigate the impact of hallucinations. For instance, when a model generates a response, linking back to its source allows users to verify the claims made.

Conclusion

AI hallucinations are an intrinsic part of how generative models function today, and such a side-effect would continue to occur until foundational changes are made in how models are trained and deployed. For the time being, developers, companies, and users must approach AI-generated content with caution. LLMs are, fundamentally, word predictors, brilliant but fallible. Recognising their limitations is the first step in navigating the misinformation dilemma they pose.

References

• https://www.eweek.com/news/ai-hallucinations-increase/
• https://www.resilience.org/stories/2025-05-11/better-ai-has-more-hallucinations/ 
• https://www.ekathimerini.com/nytimes/1269076/ai-is-getting-more-powerful-but-its-hallucinations-are-getting-worse/ 
• https://techcrunch.com/2025/05/08/asking-chatbots-for-short-answers-can-increase-hallucinations-study-finds/ 
• https://en.as.com/latest_news/is-chatgpt-having-robot-dreams-ai-is-hallucinating-and-producing-incorrect-information-and-experts-dont-know-why-n/
• https://www.newscientist.com/article/2479545-ai-hallucinations-are-getting-worse-and-theyre-here-to-stay/
• https://www.forbes.com/sites/conormurray/2025/05/06/why-ai-hallucinations-are-worse-than-ever/ 
• https://towardsdatascience.com/how-i-deal-with-hallucinations-at-an-ai-startup-9fc4121295cc/ 
• https://www.informationweek.com/machine-learning-ai/getting-a-handle-on-ai-hallucinations 

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