Is AI Dangerous?

Written by Coursera Staff • Updated on

Discover the potential risks of artificial intelligence, like cybersecurity issues and data bias, as it evolves with applications that impact everyday lives.

[Featured Image] A group of employees meet to discuss whether AI is dangerous.

The answer to "Is AI dangerous?" isn't a simple "yes" or "no" response. The response is often just as complex as the definition of AI, but the most straightforward answer is that artificial intelligence presents risks.

Over the past few years, artificial intelligence (AI) has gone from a concept in a lab to real-life applications like helping doctors diagnose patients or suggesting movies based on your interests. 

While AI applications continue to grow, business leaders will likely explore ways to use AI while mitigating risks, such as security concerns, lack of governance and transparency, and the potential to rely on biased data.  

What is AI?

Artificial intelligence, or AI, uses computers and machines to replicate the human mind's abilities in making decisions and solving problems. It combines mathematics and cognitive science to empower computers to assess massive amounts of data to solve problems quickly and efficiently. Simply put, AI uses high-tech computers to attempt to mimic human intelligence. 

The rapid rise of AI in the corporate world

In the corporate world, nearly any business can use AI to boost efficiency, improve service, and analyze customer data. Applications vary by industry and needs, but examples include:

  • Improving customer service via an AI-powered chatbot on a website

  • Offering product recommendations based on customer data quantified by AI tools

  • Segmenting audiences through AI data analyzation

  • Using cybersecurity measures that identify problems via suspicious transactions

Is AI dangerous? 4 risks associated with AI

Despite AI's infancy, companies that use AI tools have already identified risks. Concerns can range from privacy and cybersecurity issues to third-party relationships and data bias. By exploring the potential risks, leaders can work to mitigate them.  


The rapid evolution of AI presents security risks at several different junctures. For example, during the development of an AI system, a company could outsource data collection or model selection, but working with another vendor introduces more chances for security risks. 

Once a system is operational, if specific safety measures aren't implemented, hackers can exploit vulnerabilities or launch cyber attacks on poorly-protected platforms. 

Lack of governance

An AI system is only as strong as the data used to train it. If the data used to support an AI system is poor or insufficient, the outcomes provided will follow suit. Even with solid data, providing enough data and situational awareness to teach an AI-powered platform to provide an accurate outcome for every scenario is almost impossible. As a result, AI can fail to achieve its objectives. As a result, governance over the system during development, implementation, and beyond is necessary.

Lack of transparency

AI is challenging to explain, making people feel like there needs to be more transparency. For example, if a business owner can't understand how an AI automation tool makes its recommendations, it creates doubt and distrust.


AI can inadvertently perpetuate biases that stem from the training data or systematic algorithms. Data ethics is still evolving, but a risk of AI systems providing biased outcomes exists, which could leave a company vulnerable to litigation, compliance issues, and privacy concerns.

How companies are mitigating AI risks 

Some companies struggle with the idea of trusting AI, whether worried about cybersecurity risks or making decisions from biased data. As a result, many business leaders are working on responsible AI practices to identify possible risks, methodically develop AI tools, oversee implementation, and ensure its outcomes are aligned with the company's mission and values.  Some best practices to mitigate AI risks include setting guidelines, offering training, vetting vendors, and tracking regulatory changes. 

Set guidelines.

Each business needs a set of ethical guidelines to follow when implementing any AI-powered tools within the company. The policy should cover AI development and use and address issues like transparency, bias, privacy, and security.

There are online templates that could help. Microsoft, for example, offers a free resource called Microsoft Responsible AI Impact Assessment Template, a 17-page guide that walks you through system information, intended uses, potential negative impacts, and data requirements.

Name a point person or team and offer training.

To ensure guidelines are followed and people within your company are well-trained, appoint a staff member to oversee all AI implementations. Take time to decide who that person or team member should be. It might include business leaders, IT professionals, or other senior executives who would oversee the use of AI tools.  

Incorporating AI into your corporate culture safely and practically requires detailed training. Ensure everyone receives proper training, not just in daily use, but also explain risks and train employees to identify problems and alert executives of issues. 

Vet vendors.

Many companies turn to vendors to buy and implement AI systems, but vetting your options is important. Business leaders should create robust criteria to partner with a vendor, paying special attention to security measures, ethical guidelines, and the vendor's regulatory compliance record. Likely, this will result in a conversation about the vendor's commitment to AI safety and ethical practices. 

Even after your company makes a purchase, regular audits and conversations should occur to ensure continued adherence to your standards.

Track regulatory changes.

As AI uses expand, lawmakers and regulators will likely weigh in on consumer concerns. New laws might tighten restrictions on data collection or work to mitigate consumer privacy issues, for example. You can task your AI team with monitoring changes and addressing any issues that directly impact your company. 

How to start a career in AI risk mitigation

As more companies explore the use of AI, new job opportunities have followed, including AI risk mitigation jobs. While career paths are still developing to meet this new need, the most common starting point for an AI risk mitigation job is usually computer science.

Due to AI's reliance on data, a beginning role as a junior data scientist is common, which could lead to a data scientist position. Career advancement could lead to a senior tech role, with job titles like data architect, principal data scientist, or more specialized categories like AI risk mitigation.

Job outlook and salary

The job outlook for data scientists, an occupation closely tied to AI career advancement, should grow by 35 percent through 2032, compared to an average of 3 percent growth in other sectors, according to the US Bureau of Labor Statistics [1].

The average annual salary for data scientists is $114,288, according to Lightcast™ [2].

Learn more about AI with Coursera.

If you'd like to pursue a career in AI but want to learn more about AI and its applications, explore online courses on Coursera. Introduction to Artificial Intelligence (AI) by IBM, for example, explores what artificial intelligence is, reviews AI applications, and explains terms like machine learning, deep learning, and neural networks. Introduction to Generative AI by Google Cloud introduces learners to generative AI and provides instruction to develop generative AI apps. Both courses are for beginners and offer flexible schedules.

Article sources


US Bureau of Labor Statistics. "Data Scientists," Accessed March 1, 2024.

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