The global machine-learning market is expected to grow from $26.03 billion in 2023 to $225.91 billion by 2030, according to research firm Fortune Business Insights. As machine learning technology becomes increasingly adopted across various sectors, it's essential to be aware of the potential risks and pitfalls that can lead to project failure. We spoke with tech leaders and analysts to identify the most common ways machine learning projects fail, and here's what they told us.

Machine learning, a subset of artificial intelligence, refers to the process of training algorithms to make predictive decisions using large sets of data. While the potential benefits of machine learning seem limitless, it also poses significant risks if not implemented correctly. From AI hallucinations to model bias, legal and ethical risks, poor data quality, and more, there are numerous pitfalls that can lead to project failure.

One of the most common machine learning mistakes is AI hallucinations, which occur when a large language model perceives patterns or objects that don't exist or are imperceptible to humans. According to Camden Swita, head of AI/machine learning at unified data platform provider New Relic, recent research indicates that a large majority of machine learning engineers have observed signs of hallucinations in their LLMs. To combat hallucinations, developers must emphasize summarization tasks and utilize advanced techniques like retrieval-augmented generation (RAG), which greatly reduce hallucinations.

Model bias is another significant risk, which occurs when systematic errors in a model cause it to consistently make incorrect predictions. Organizations need to ensure that their data contains accurate group representation and diverse data sets, says Sheldon Arora, CEO of StaffDNA, a company that uses AI to help match candidates with jobs in the healthcare sector. Continuously monitoring model performance ensures equitable representation from all demographic groups.

Legal and ethical risks are also a significant concern, including discrimination due to model bias, data privacy violations, security leaks, and intellectual property violations. Decisions based on machine learning algorithms can negatively affect individuals, even if that was not the intent. To reduce these risks, organizations must anchor models and output on trusted, validated, and regulated data, says Swita.

Poor data quality is another common pitfall, leading to flawed models and unacceptable results. Market analysis by research firm Gartner shows that a majority of organizations have issues with their data, with many citing data unreliability and inaccuracy as top reasons for not trusting AI. To address these challenges, organizations must move beyond perfection and adopt approaches that align governance with data's intended purpose, fostering trust and adaptability, says Peter Krensky, a senior director and analyst on the analytics and AI team at Gartner.

Machine learning models can also suffer from overfitting and underfitting, which occur when a model is trained too closely to a training set or is too simple to accurately capture the relationship between input and output variables. Teams can use cross-validation, regularization, and the appropriate model architecture to address these problems, says Elvis Sun, a software engineer at Google and founder of PressPulse, a company that uses AI to help connect journalists and experts.

Legacy system integration issues can also hinder machine learning projects, particularly when integrating with older systems. It's crucial to ensure that the systems in place can support new machine learning-based products, says Damien Filiatrault, founder and CEO of Scalable Path, a software staffing agency. Machine learning models can be integrated with older systems through APIs and microservices that enable interaction among them, Filiatrault says.

Performance and scalability issues are another significant concern, particularly as the use of machine learning grows over time. If systems are not able to maintain their performance and efficiency when dealing with significantly larger datasets, increased complexity, and higher computational demands, the results will likely not be acceptable. Machine learning models must be able to handle growing data volumes without a significant decline in performance or speed, says Arora.

Lack of transparency and trust is another common pitfall, particularly in environments where confidentiality is key. Using interpretable models whenever possible or employing explanation frameworks like SHAP could help address this problem, says Filiatrault. Proper documentation and visualization of decision-making processes might also help foster user trust and compliance with regulations to guarantee the ethical use of AI, Filiatrault says.

Not having enough domain-specific knowledge is another significant issue, particularly in industries with complex data structures, business procedures, and laws and regulations. Companies that lack the right people on their teams may find this type of domain-specific knowledge to be a major issue, says Sun. To bridge this gap, machine learning professionals must collaborate closely with those in related fields, Sun says.

Finally, a machine learning skills shortage is a significant challenge, particularly as organizations struggle with change management and driving adoption and aligning teams with evolving capabilities. Organizations are overcoming these challenges by focusing on reskilling, fostering collaboration across disciplines, and embracing new roles such as AI translators, says Krensky.

In conclusion, machine learning technology holds immense potential, but it's crucial to be aware of the potential risks and pitfalls that can lead to project failure. By understanding and addressing these common machine learning mistakes, organizations can ensure the successful implementation of machine learning projects and unlock the full potential of this technology.