Risks of AI and how to mitigate them

By: Thomas Broderick, Edited by: Rebecca Munday

Published: May 8, 2025

Are you worried about artificial intelligence's (AI) potential impact on your job and life? If so, now is the best time to learn about its dangers. Understanding this transformative technology's potential downfalls can equip you with valuable tools.

Explore the most significant dangers of AI and how AI experts mitigate them on the job.

What are the risks with AI?

The risks and dangers of AI include inherent bias, cybersecurity concerns, data privacy risks, and lack of accountability, among other pitfalls.

Bias

AI technologies, such as large language models (LLMs), require vast amounts of human-created content for training purposes. A programmer's decisions regarding data sets and training algorithms or the parameters that oversee generative AI models can create bias within the technology.

Bias can appear in AI output in different ways, such as preferring some responses over others, providing incorrect answers, or displaying racial prejudice. Evidence of bias presents many challenges for AI adoption, as users may not trust AI output as authoritative.

Cybersecurity

AI proponents often point to the technology's ability to write code. However, early results suggest that AI cannot write code with cybersecurity in mind. Flaws include buggy or incomplete code that is susceptible to hacking.

Addressing AI's cybersecurity dangers presents significant challenges. At a foundational level, programmers did not develop cybersecurity-optimized AI programming scripts. With current models, fixing these deficiencies in AI-developed code may take programmers the same amount of time as if they had written the code from scratch.

Data privacy

Cybersecurity faults in AI-developed code can put user data at risk to hackers.

The data sets that AI trains on offer another privacy concern. Often, data sets are used to train AI technology without the consent of the data's creator.

Interacting with AI chatbots reveals other data privacy concerns. Some chatbots store conversations without users' knowledge. This leaves users unaware that their private information is vulnerable.

Intellectual property infringement

LLMs and other AI must train on large data sets to function correctly. Programmers provide this data by scraping text and images from millions of websites. However, this is often done without the compensation or consent of content creators.

Creators have responded in different ways, such as licensing data to or filing lawsuits against AI companies. Still, fresh, human-made data is essential for AI technologies to function. Using older data for AI training may lead to model collapse, a phenomenon wherein an AI becomes less reliable if it solely pulls from AI-generated content for training purposes.

Job loss

Many employers view AI as a way to streamline workforces, increasing the chances that many once-stable professions may soon disappear. AI may reduce or eliminate the need for receptionists, customer service representatives, and tellers.

According to the Bureau of Labor Statistics, growth of these roles is projected to decline between 2023-33 due to automation, customer service chatbots, and technological advancement. Receptionists are projected to decline by 1%, customer service representatives by 5%, and tellers by 15%.

Workers in a field impacted by AI may want to explore AI courses and programs. These educational opportunities can teach you to use AI effectively in your job, improving your employability during economic uncertainty.

Lack of accountability

While early adopters of AI technology may have used it to replace workers, they often don't take accountability when technology bugs cause mistakes.

A well-known example involves Air Canada's customer service AI chatbot, which provided incorrect information and refused customer requests. Air Canada sidestepped responsibility by claiming that its AI chatbot is a separate legal entity that does not speak as an official company representative.

Companies that do not enact AI accountability guidelines may face significant consumer backlash as complaints mount.

Lack of transparency

Controversies surrounding data scraping and copyright infringements have put the media spotlight on the lack of transparency in AI. Critics point to AI companies' refusals to disclose how they obtain AI training data. Companies have responded to this secrecy by claiming that openness would reveal proprietary methods that could harm their business.

A continued lack of transparency may lead to fewer contracts and increased copyright infringement lawsuits.

Misinformation and manipulation

Advances in AI image generation since 2022 show the technology's potential to create lifelike images and short videos. Malicious actors may use this technology to create convincing misinformation, such as political propaganda. People, especially those unfamiliar with AI, may fall victim to manipulation.

You can protect yourself from AI misinformation and manipulation by double-checking claims against multiple credible sources, such as reputable news sites.