Artificial intelligence (AI) is changing everything: how we shop, how doctors diagnose diseases, and more. Yet, it’s far from perfect and comes with big challenges. Some of these AI problems are serious enough to slow down progress or even lead to major negative outcomes.
Let’s talk about what’s wrong with AI, what can be done, and who’s working on solutions.
1. AI Struggles with Bias (and That’s a Huge Problem)
AI systems learn from data, so its quality is the first thing to care about before you employ any algorithms. Otherwise, if that data is biased, AI makes biased decisions, too. Unfortunately, this isn’t just a theory because it’s happening all the time.
A study by MIT and Stanford found that facial recognition software had an error rate of 34.7% for dark-skinned women but only 0.8% for light-skinned men. Imagine being wrongly identified at an airport or denied a job just because of a biased system.
The best way to fix the AI problems with bias so far is to train it on diverse data. AI deployment specialists are also working on algorithms that detect and correct bias in real-time. But this still needs serious shared effort from companies, regulators, and developers.
2. AI Needs a Ton of Data (and Not Everyone Has It)
Yes, AI development depends on large and diverse datasets, and the problem is that not all companies have access to the amount or quality of data they need. Small businesses can’t compete with giants like Google, which has endless user data.
Artificial intelligence consulting firms often help businesses build AI models using synthetic data. Such data is fake but realistic enough to train AI without privacy concerns. Platforms like Mostly AI also give essential data for companies that lack training datasets or need more diverse data.
3. AI Still Can’t Explain Its Decisions
Another significant challenge of artificial intelligence is its old black box problem. AI technologies make decisions, but they can’t always explain how they come to conclusions. This is a serious issue in industries like healthcare because if an AI system rejects a patient for treatment, doctors need to know exactly why.
On the other hand, explainable AI (XAI) is a growing field focused on making AI’s decision-making process transparent. For example, Fiddler AI and IBM are developing AI technologies that make AI explain itself.
4. AI Challenges in Security Needs Way More Control
AI is often hacked, too, and some of the attacks lead to truly disastrous consequences. In 2020, researchers tricked a Tesla into changing lanes by placing stickers on the road. At times, the car was already sold nearly 500,000 times. The example shows AI systems are vulnerable to adversarial attacks, where hackers manipulate AI into making bad decisions.
A while ago, AI vision models were also fooled by simple tricks that humans would never fall for. The testers changed just one pixel in an image causing an AI technology to misidentify it completely.
Luckily, AI experts are working on more secure and smart AI models. One approach is adversarial training, where AI recognizes and resists attacks. Governments are also making moves like the EU’s AI Act, which regulates high-risk AI systems.
Another approach is reinforcement learning, or training AI in simulated environments that mimic real-world complexity. Within this approach, self-driving car companies are investing more in AI that learns from edge cases (rare but critical situations).
5. AI Technology Is Expensive (Which Is a Barrier for Many)
Just like any advanced tech, AI development isn’t cheap either. Training a state-of-the-art AI model like GPT-4 can cost millions of dollars depending on its purpose and scale. This puts AI out of reach for many companies, especially small ones.
Cloud-based AI systems from Google, Microsoft, and Amazon are trying to address the issue and make AI more affordable. Businesses rent AI power from cloud providers instead of building it from scratch. Other solutions are open-source AI tools like Hugging Face, which give some free models.
6. AI Still Struggles with Common Sense
The next significant challenge is quite surprising: AI is great at crunching numbers but terrible at basic logic. Back in 2022, OpenAI’s GPT-3 was tested with this question:
If I put cheese in the fridge, will it melt?
It answered: Yes.
Such failures happen because AI doesn’t understand the world like humans do. Imagine how many simple questions have never been tested, so we cannot say if the basic logic of AI has actually improved much.
To tackle this, researchers are working on neuro-symbolic AI, which combines traditional logic with machine learning. MIT and DeepMind are leading research in this area.
7. AI Takes Jobs, But It Also Creates Them
Another challenging thing is job displacement. A Goldman Sachs report in 2023 estimated that 300 million jobs could be automated. But AI also creates jobs in AI development, AI consulting, and ethical AI practices.
Hopefully, companies and governments will focus on reskilling programs more, since AI isn’t going away. Workers need training to adapt and countries like Germany and Singapore already lead by example with AI-focused workforce programs.
8. AI Ethical Concerns: Who’s Responsible When AI Fails?
Now let's imagine what will happen if an AI-powered medical tool makes a wrong diagnosis. Who will be responsible for the outcome and consequences? Options are:
- The developer who trained the model;
- The doctor who used the AI-powered tool;
- The hospital which allowed the use of such tools;
- The patient who gave their consent;
- The government which allowed the use of AI by healthcare providers.
This isn’t even a theoretical issue as AI failures have already caused financial losses and more. That's why governments are introducing AI laws to hold companies accountable. The above-mentioned EU AI Act and Biden’s AI Executive Order are among the pioneer rules for ethical AI development.
9. AI Energy Consumption Is Out of Control
Since AI requires much data, training AI models needs massive computing power, which means high energy use. A study by the University of Massachusetts Amherst found that training a single AI model generates as much carbon dioxide as five cars over their entire lifetime.
To fix this, companies are investing in energy-efficient AI. For example, Google’s DeepMind uses AI to optimize its data centers, reducing cooling costs by 40%. Researchers are also exploring neuromorphic computing, which mimics the human brain and consumes far less power.
10. AI Can Be Manipulated with Fake Data
AI systems learn from the data it’s given, so organizations need to be transparent about who gives data to AI and why. If bad actors feed it fake or misleading data, they can easily manipulate AI outputs. This is especially dangerous for AI in finance, healthcare, and law enforcement, where biased or false predictions often have real consequences.
In fact, AI-generated deepfakes are getting so good that they fool people into believing fake news, impersonate politicians, and even scam businesses. In 2023, a deepfake video of a CEO tricked employees into transferring $25 million to scammers.
So, it shows that AI-powered recommendation systems are already used for manipulative advertising. These messages exploit users’ fears, biases, and emotions, influencing behavior in ways people don’t realize.
The good news is that the challenge is manageable because AI specialists use data validation techniques that detect and filter out unreliable data. Blockchain technology, for example, is being explored to create tamper-proof AI training datasets.
Others are working on deepfake detection tools. Microsoft and Adobe now offer watermarking and authentication systems to verify real vs. AI-generated content. And regulators are pushing for AI transparency laws, requiring companies to disclose when AI is being used for advertising or content recommendations. That's why platforms like YouTube and TikTok are now experimenting with why am I seeing this? features.
11. AI Struggles with Creativity and Context
Let's move to the more down-to-earth tasks that artificial intelligence does for us.
Today, marketers and content creators generate text, art, and music with AI for many purposes. But many content creators will confirm: AI doesn’t truly understand creativity. Instead, it works by predicting patterns rather than having original ideas. This is why AI-generated content sometimes feels generic or off.
It's hard to say if it's possible to blend real creativity with machine generative power. But some researchers are exploring hybrid models that combine AI’s pattern recognition with human input. A well-known OpenAI is working on AI-human collaboration tools that add to creativity instead of replacing it.
12. AI’s Legal and Copyright Issues Are a Mess
The challenges of artificial intelligence in content creation raise legal questions outside the security scope, too. Like:
- Who owns an AI-created image?
- Can AI-written books be copyrighted?
Laws haven’t caught up yet, and companies are already facing lawsuits for AI models trained on copyrighted data. Some artists and writers are suing AI companies for training AI models on copyrighted work without permission.
It's mostly a governmental function to work on AI copyright laws. Meanwhile, AI providers like OpenAI develop ethical AI training methods that respect intellectual property rights.
13. AI Can Reinforce Social Inequality
Since AI is often deployed in hiring, banking, and criminal justice, it can affect the three areas where bias has life-changing consequences. Studies show that AI-powered hiring tools have discriminated against women and minority candidates. Similarly, AI-driven loan approvals often favor wealthier applicants.
That's why some organizations go beyond testing and hire entire AI ethics teams and regulators to push for AI fairness audits. This way they try to ensure that AI isn’t reinforcing discrimination. Some companies, like IBM, have developed AI bias detection software to flag problematic decisions.
The more this practice spreads, the more we can be sure AI stays fair and doesn't harm accessibility.
14. AI in Healthcare Still Has Accuracy Issues
The reason why we mention the healthcare industry so many times is that the field has collected unparalleled amounts of data globally. AI-powered medical tools are promising, but they’re still far from perfect. Studies found that AI systems diagnosing skin cancer had an error rate of up to 30% in some cases. So, relying too much on AI in medicine is risky.
The findings mean AI in healthcare should always be used with human oversight. On the tech side of things, a recent development, multi-modal AI, aims to improve accuracy by combining different types of data (like images, lab tests, and patient history).
15. AI Models Can Be Too Slow for Real-Time Applications
Some AI applications, like fraud detection and cybersecurity, need instant decision-making. But AI models can be slow, especially when processing large datasets. In finance, a delay of even a few milliseconds often results in massive losses.
To improve AI efficiency, developers turn to edge computing, which allows algorithms to process data locally instead of relying on cloud servers. And big names like Nvidia and Intel are even building AI chips optimized for speed.
16. AI Governance and Regulations Slow Down Development
Right now, AI laws vary by country with the already-mentioned EU AI Act being one of the first serious attempts to regulate AI. But the U.S. and China have different approaches. This makes it hard for global companies to comply with AI laws across different regions.
In the ideal AI governance world, governments and tech companies will agree on global AI standards. OECD and the UN are the ones attempting to create international AI guidelines for consistency in AI governance.
17. AI’s Economic Impact Is Uncertain
As we covered, while AI creates jobs, it also eliminates them. Industries like customer service, transportation, and even software development are seeing jobs replaced by AI systems. A 2023 study by McKinsey estimated that by 2030, 12 million U.S. workers may need to change careers due to AI.
To make the right balance, not only governments but businesses need to invest in reskilling and upskilling programs and help workers transition into new roles. Some governments are also experimenting with AI taxes, where companies that automate jobs contribute to worker retraining programs.
18. AI Struggles with Emotional Intelligence
Yes, AI systems analyze text and voice tones to detect emotions, but they don't actually understand feelings. The common cases are when customer service chatbots misinterpret sarcasm, frustration, or humor, leading to awkward interactions.
In the attempts to solve this, AI researchers are improving sentiment analysis models by incorporating context awareness and multimodal AI, which combines text, voice, and facial expressions. However, for now, human oversight is still essential.
19. AI Can Struggle with Local Cultures and Languages
Plus, since most AI systems are trained in English and major languages like Chinese and Spanish, they forget about smaller languages and dialects. As a result, AI translation tools often fail to capture local slang, idioms, and cultural nuances and act unreliable in many regions.
One of the ways to change the situation is by expanding language datasets and using community-driven AI training, where native speakers contribute to improving AI models. Giants like Google and Meta also opt for universal language models to support more languages.
Let's Solve Today’s AI Challenges Together
Like many technologies, AI isn’t perfect, but the good news is that people and governments are working on solutions. With better data management, smarter algorithms, and stronger security, AI systems will continue to improve. The biggest challenge, however, isn’t the technology but making sure AI is developed responsibly.
As AI moves forward, businesses, governments, and AI experts must work together on data related challenges, ethical and privacy concerns, machine learning and AI adoption issues, and general solutions to artificial intelligence problems. If they do, AI will be powerful, fair, safe, and useful for everyone.
Need AI consulting to make sure your AI tools will overcome all challenges? At Akveo, we help:
- Identify how AI can help your business
- Choose the right AI technologies
- Solve AI challenges like bias, data security, or ethics
- Stay compliant with AI regulations, and more
Get in touch, and let's make the most out of your AI-powered solutions!
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