What Are the Risks and Limitations of Machine Learning?

Along with the immense benefits and exciting applications of machine learning, entrepreneurs should also be aware of machine learning limitations, challenges, and possible risks for organizations and the public.

 

Onix’s developers can help you navigate the various challenges of machine learning implementation while you grow your business. We have years of experience developing ML solutions and have completed hundreds of projects for international clients.

 

For example, for a drone and satellite solution for mapping and geographic information systems, we utilized technologies based on computer vision, object detection, and image segmentation and processing and resolved markup issues when scaling and cutting images.

Onix’s mapping and GIS solution detects buildings, roads, rivers, forests, and fields, puts them on a generated map, and can detect changes through comparisons with previous observations.

In this article, we’ll outline

 

• the constraints of the machine learning technology to consider before choosing it as a solution for your business problem
• the difficulties you may face when implementing ML solutions and how to address them
• possible risks and negative effects of ML implementation

If you have any questions or have encountered issues in your ML project, please contact us! 

Why You Should Care about Machine Learning Issues at All

Main article: Machine Learning in Business: Pros and Cons

Machine learning is an area of artificial intelligence where machine learning models identify patterns in data or make predictions or decisions autonomously.

ML models are made of algorithms that can be trained in several ways.

 

 

Learning from data and previous experiences, ML models enhance their performance over time.

Learn more: How to Build a Machine Learning Model for Your Business

Key machine learning techniques include:

 

• regression and classification for predictive modeling
• clustering for identifying patterns and groupings in data
• decision trees for interpretable rule-based predictions
• anomaly detection for identifying outliers or irregularities
• neural networks for handling complex, high-dimensional data in tasks like image recognition, natural language processing (NLP), and deep learning applications

Deep learning (DL) is an ML subset that uses artificial neural networks (ANNs) to model complex patterns and representations in data. Inspired by the human brain’s structure, ANNs consist of layers of nodes/neurons processing and transforming input data.

Each layer applies to the passing data mathematical operations called weights and biases, so one layer’s output becomes the input for the next. DL leverages multiple neuron layers to automatically learn hierarchical features.

The training of DL models focuses on minimizing the error, or loss, between the predicted output and the true output. This involves iterative optimization, normally using gradient descent.

This foundational ML algorithm works by calculating the gradient of the loss function with respect to the model’s parameters (weights and biases) and updating these in the direction of the steepest decrease in the error.

Stochastic gradient descent, mini-batch gradient descent, and advanced optimizers like Adam, RMSprop, and Adagrad can help improve convergence speed, stability, and performance.

The goal is to find the set of parameters that minimize the loss function, ensuring generalization, i.e. proper performance on previously unknown data.

Deep learning algorithms can learn features from data automatically, eliminating the need for manual feature engineering. They have a wide range of applications, including:

 

1. computer vision (e.g., image and video recognition, object detection, semantic segmentation, etc.)
2. NLP, where models are trained to comprehend and interpret human language
3. speech recognition, voice identification, and voice synthesis

The global machine learning market is expected to reach USD 79.29 billion in 2024 and, growing at a staggering CAGR of 36%, exceed 500 billion by 2030. The main applications of ML include, but are not limited to:

 

• Pattern recognition
• Content and product recommendations
• Fraud detection
• Security systems
• Social media
• Chatbots, such as ChatGPT
• Sentiment analysis 
• Speech recognition
• Translation
• Content generation
• Virtual assistants
• Healthcare

Onix has been active in all of these areas and beyond. For instance, our team has developed an advanced system for translating graphic novels and webtoons. The system employs text detection, optical character recognition (OCR), computer vision, and machine translation to automatically perform a sequence of tasks:

1) The system defines a bounding box with text, recognizes the text, and cuts out surrounding image areas.

2) The text and associated graphic elements are removed from the image. The resulting empty spaces are filled automatically using the analysis of the surrounding image.

3) Translation from the source language to the target language can be done using ChatGPT when texts are expected to have the characteristics of its training data, such as noisy user-generated English texts. NLP can improve the quality of translation.

 

Read also: ChatGPT in Your Application: Opportunities & Integration Tips

 

4) The system fits the translated text into the source comic callout, removing the background around it. If the original comic callout style is not essential, vector graphics processing software can perform the task.

5) The system uses the callout coordinates from step 1 to overlay the callout with translated text from step 4 on the image from step 2. 

Onix also builds and trains ML models for purposes as varied as language identification, news categorization, image denoising, face recognition and manipulations, and green energy output prediction.

When Onix and our clients deal with machine learning, challenges may arise at any phase of the project. In this article, we classify them into three groups that will be discussed in corresponding chapters:

 

1. The inherent limits of ML algorithms and the current ML technology
2. The technical, organizational, and other problems of ML deployment faced by businesses
3. The pitfalls of ML implementation and risks that ML-powered solutions pose for businesses and the public

The Limitations of Machine Learning

The limits of machine learning simply mean that this technology isn’t perfect or all-purpose. ML is powerful but still young and comes with a number of rules and requirements. Understanding the ML limitations is critical for 

 

1. setting realistic expectations
2. effective solution conception, design, and implementation
3. informed decisions regarding a specific issue or application
4. devising appropriate risk mitigation methods
5. responsible and effective ML system deployment

The key limitations of ML are related to data quality and quantity, time, accuracy, biases, and lack of comprehension.

 

1.Dependence on data quality and quantity

Data is one of the key issues in machine learning projects. For example, it is a major qualifier when you decide whether ML is the right choice for your business problem, keeping in mind that each application requires separate training.

Learn more: A Business's Step-by-Step Guide to an Artificial Intelligence Project

It’s important to educate decision-makers on ML model limitations, promote transparency, and encourage open discussions about results and uncertainties.

Stakeholders should know as early as possible if there is a suitable set of data to “feed” to the ML system. What if you need historical data but your organization is young? How are you going to acquire information: buy or generate the necessary amount on your own?

For instance, if a manufacturing company needs a predictive maintenance system to prevent breakdowns, it will have to install sensors on machinery to supply real-time data.

Stakeholders must also understand that ML models use patterns learned from historical data and can’t offer meaningful insights or predictions about things outside the scope of their training data or generalize to new situations or contexts.

 

For example, an application for political sentiment analysis will likely be useless for hotels wishing to personalize their services.

The effectiveness and reliability of ML models heavily depends on the quality of data they are trained and tested on. If the training and validation data is incomplete, inaccurate, outdated, biased, or irrelevant, ML systems will produce misleading or erroneous results.

To ensure the high quality of training and testing datasets, developers must consider the source reliability, data validity, and the results of pre-processing. Quality Engineering processes applied throughout data handling can help improve the outcomes.

If deployed machine learning systems consume flawed, incomplete, or biased data, their decisions will be equally flawed. Owners must ensure a system’s ready access to the necessary data, which must be of high quality and adequately pre-processed.

Ensuring data quality is a continuous and resource-intensive process that requires strategic planning. 

2. Risk of overfitting and underfitting

Overfitting occurs when a model becomes too complicated, is too closely tailored to the training data, capturing noise or irrelevant patterns, and becomes overly specialized.

This leads to erroneous predictions and lower performance on unseen data. For instance, an overfit model may excel in stock market prediction based on historical data but fail to generalize to new market conditions.

Read also: How to Create a Stock Trading App Like Robinhood in 2025

This is a common issue in deep learning, particularly with large neural networks. Overfitting may be caused by insufficient training data, model complexity, or a lack of normalization. Larger and more diverse datasets, regularization, hyper-parameter tuning, and cross-validation help solve these machine learning problems.

Underfitting happens when a model is too simplistic or can’t capture the underlying patterns and complex relationships in the training data. Without learning crucial correlations, an underfit model has a significant bias and low variance. This leads to suboptimal performance even on the training data and poor predictions during the training and afterward.

For instance, a model may be trained to predict exam scores based on the number of hours that students studied. An underfit model will likely capture a correlation between more study hours and higher grades. However, missing more complex and non-linear relationships between study time and exam success in the real world, it will likely make false predictions. 

Increasing the model complexity and integrating more essential features, developers can reduce this likelihood. The training data must be more robust or noisier.

Finding the balance between underfitting and overfitting, model complexity and interpretability are some of the main challenges for ML teams.

3. Training time

Significant computational results can be achieved only over a certain time span.

“We can build an ML system beta version operating in a controlled environment in several months. However, testing and experimentation to achieve the desired level of accuracy and stability may take up to 10 months more,” said Oleksii Sheremet, the Tech Lead at Onix’s ML Department. Oleksii is a Doctor of Technical Science, Professor, and Head of Department at the Donbas State Engineering Academy.

Unfortunately, even after months of rigorous training, ML systems can’t guarantee a 100% accurate answer.

4. Limited accuracy

Errors, uncertainty, and variability of models’ outputs are persistent issues in ML. They may be caused by model assumptions, parameter settings, data noise, and random fluctuations. Stakeholders should be aware of this and the types of errors ML models can produce.

During the system training stage, the team must build an accuracy assessment strategy and make corrections once they notice the reliability score is below a permissible level.

Even after proper training, it may still be challenging to achieve generalization. For example, a spam classifier may excel in identifying common spam but falter when new spam techniques emerge. Cross-validation and regularization should be applied to improve performance.

It’s also vital to continuously evaluate and validate your ML models and use appropriate technical means and metrics to quantify their uncertainty and variability. 

 

5. Biased decisions

Cognitive biases may be introduced to ML models either by developers or through training on insufficient datasets or datasets containing biased information.

For instance, an HR assistant trained on historical data may inadvertently discriminate against candidates of a particular gender or minority and prefer others.

Carefully selected metrics must be applied to address prejudice and discrimination in data collection, pre-processing, algorithm design, and assessment. Fairness-aware algorithms, diverse training data, regular audits, and continuous monitoring help reduce bias for more impartial and trustworthy ML systems.

Read also: The Use of AI at the Olympic Games in Paris and Beyond

6. Limited intelligence

The fundamental problem of machine learning is that computer algorithms lack common-sense understanding. They can’t truly capture the meaning of data and computational results. That’s why ML systems, however powerful, require human involvement. 

For instance, an ML system trained on historical data to forecast stock prices may consider factors like corporate earnings, news sentiment, and market movements.

 

However, if it ignores the underlying causal reasons behind stock market movements, such as geopolitical developments, its forecasts will fail when causal factors differ from historical correlations.

If causality is not included, it may also be difficult to understand and explain the reasoning behind a model’s predictions or judgments – a key requirement for machine learning systems.

Respective domain knowledge and causal inference can remedy this. Randomized controlled experiments, causal graphical models, and counterfactual reasoning help identify and model cause-and-effect linkages for more reliable predictions.

The inability to incorporate social, aesthetic, or moral considerations into decision-making limits machine learning systems’ capability to make decisions that require subjective judgment, balancing trade-offs, intuition, or creativity.

The challenges in machine learning projects are largely determined by the type of ML. For example:

 

• Supervised learning requires high-quality labeled data and has issues with unusual or new classes or events.
• The limitations of unsupervised learning include difficulties in evaluating and validating learned representations, understanding observed patterns, and dealing with high-dimensional data.
• Reinforcement learning disadvantages include the large sample complexity, sophisticated construction of appropriate reward systems, high computational demands, and possible unstable learning during exploration.

DL algorithms often provide solutions for traditional ML challenges, such as processing large and intricate datasets, handling unstructured data (images, text, and audio) and missing data, and detecting non-linear relationships within data.

However, they also come with their own constraints. The limitations of deep learning include, but are not limited to:

 

• the demand for significant volumes of labeled data and computational resources
• difficulties in understanding DL models’ internal representations and decision-making processes
• difficult identification of errors and biases
• sensitivity to adversarial assaults

Moreover, deep learning systems often fail silently and continue working despite implementation errors.

The Challenges of Machine Learning System Implementation

System design, implementation, and use cases all contribute to the problems of machine learning projects.

 

 

For instance, Tan, Taeihagh, and Baxter categorized the risks posed by ML systems into:

1) First-order risks. These risks arise from system design and implementation choices, properties of the chosen dataset and learning components, and intended and unintended use and relate to the ways an ML system can fail.

 

2) Second-order risks. These risks relate to the impact of an ML system on the organization, human rights, the natural environment, etc. 

Let us offer a more detailed list of challenges that entrepreneurs may face during ML solution development.

 

1.Financial concerns

Limited budgets and prohibitive costs are significant challenges for machine learning adoption among smaller businesses, so the economic aspect of an ML system integration is one of the first things entrepreneurs should assess.

Will the innovation pay off? How soon? The investment may include, but is not limited to: 

 

• expenses incurred with collecting, storing, and cleaning the necessary data
• infrastructure and hardware acquisition 
• the cost of software development, deployment, and maintenance
• possible losses when the ML system is integrated with your workflows and processes
• personnel training expenses
• labor costs
• ongoing maintenance

 

2. Inadequate documentation

Insufficient specification of the requirements and operational scope of an ML system increases the risk of encountering factors it was not designed to handle. 

The value of a proper product specification increases for high-stakes ML systems that can’t be easily updated after deployment.

Clear and extensive documentation of experimental setups, data sources, pre-processing methods, algorithm setups, code, etc., also helps address another common issue in machine learning projects: failure to replicate or reproduce functions under conditions similar to those under which they were developed and tested.

 

3. ML algorithm-related risks

These risks stem from an ML algorithm, model architecture, optimization technique, or other aspects of the ML training process being unsuitable for the intended application.

The choice of an ML algorithm or model architecture depends on the problem type (e.g., classification or clustering), data type, and the desired output. For example, NLP tasks require recurrent neural networks and image recognition – convolutional neural networks (CNNs).

An experienced ML team’s advice may prove invaluable. For instance, Onix possesses expertise in image denoising – an essential task in image processing and transmission within infocommunication systems.

Statistical filtering algorithms are effective for specific noise types like Gaussian noise but often struggle with the random and complex nature of non-Gaussian noise. Onix’s developers employed CNNs capable of adaptive noise suppression to address this limitation.

Unlike conventional filters, denoising CNNs (DnCNNs) excel in blind denoising tasks where the noise characteristics are unknown. They generate correction signals within the infocommunication pipeline, improving image quality at the receiver end.

“By incorporating these correction elements into infocommunication systems, Onix demonstrated the capability to enhance real-time image transmission and processing. This approach not only mitigates noise-related artifacts but also ensures higher fidelity in transmitted images, unlocking new possibilities for automation, analysis, and user experience across dynamic environments,” said Oleksii Sheremet, Onix’s ML Tech Lead.

Source

Pre-trained on large, diverse datasets, these CNN-based models can be fine-tuned for precise filtering across various scenarios.

Different combinations of ML model architecture, optimization algorithm, and training procedure have different effects on its final performance. For instance, a language model can be trained with the causal or masked language modeling objective. The latter is suitable for text classification but may be suboptimal for text generation.

Read also: Text Classification: Use Cases that Change Your Business Strategies

Domain adversarial training may improve an ML system’s ability to generalize to new domains with minimal extra training data but may decrease performance on the original domain.

It is also crucial to consider the reliability and resource intensiveness of the chosen ML algorithm, model architecture, and optimization technique combination in production scenarios. A highly accurate system that is computationally intensive or failure-prone may be less desirable than a slightly less accurate system without those flaws.

The opacity and unpredictability of some algorithms complicate designing rules and predicting inputs that could yield unsafe or discriminatory outcomes. They also make it difficult to ensure accountability while the General Data Protection Regulation (GDPR) and other regulations mandate transparency. 

Explainability alone can’t reduce biases in the system or make it safer, but it may help detect biases and reduce safety and discrimination risks. A system that can explain its decision when a mistake took place is highly desirable when human lives or funds are at stake.

 

4. ML design and development team recruitment

Good algorithm, modeling technique, and system design choices require engineers with relevant experience. Experts will also identify gaps in the design requirements, foresee security and ethical concerns, etc., and help mitigate risks.

Main article: How to Build Machine Learning Teams for AI Projects

Onix’s ML team has vast experience in completing projects for international clients. For instance, we smartly applied ML in the development of an iOS app for the cosmetics industry. 

To find beauty products best suited to each user’s unique skin and tastes, the AI-powered app

 

• analyzes a user’s registration details and skincare requirements
• generates personalized skincare recommendations
• using current scientific data and analyzing relevant products’ formulas, checks them for toxic or hazardous ingredients
• generates a scientifically substantiated selection of eco-friendly ingredients and products perfectly meeting the user’s needs

Onix’s developers employed ML to classify all possible ingredients used in cosmetic products. According to Oleksii Sheremet, our ML Tech Lead, “Typically, compiling, analyzing, and comparing such amounts of information within legal departments takes months. Onix’s solution accomplishes this in a single day.”

This enabled the app to

 

• analyze data from over 21,000 scientific research sources
• analyze 45,000+ products.
• examine 200,000+ formulations
• create over 100,000 ingredient catalogs

The skincare & cosmetics analysis app built by Onix selects the most suitable products for each consumer. 

 

Inadequate domain expertise also hinders problem formulation and algorithm selection and increases the risk of negative consequences of the ML system implementation.

 

The ML team should consult experts in human rights, legislation, environmental protection, etc., user researchers, and affected stakeholders throughout the solution development process.

 

5. ML system design risks

Data pre-processing and modeling choices are key challenges of machine learning system design.

ML systems often pre-process raw input before passing it into their modeling components for inference. The methods include tokenization, image transformation, and data imputation and normalization.

Data from multiple sources and modalities may also be combined and transformed in ETL (extract, transform, load) pipelines before the model consumes it.

The developers’ choices at this stage will impact the training and operation of the ML model.

Operationalization of an abstract construct as a measurable quantity requires some assumptions about how the construct manifests in the real world. The measurement process introduces errors even when it is applied to tangible constructs that seem straightforward.

A mismatch between the abstract construct and measured quantity can result in poor predictions. Confusing the measured quantity for the abstract construct can have long-term negative societal consequences.

ML systems that operate on tabular data often make use of hand-engineered features. Possible risks during the feature selection include:

 

• Training a model on spurious features, which can impact generalization and robustness
• Using demographic attributes, such as race, religion, gender, sexuality, or proxy attributes (e.g., postal code, name, or mother tongue) for prediction, which may lead to discrimination against historically marginalized groups

Read also: The Ultimate Guide to AI Proof of Concept

 

6. Training and validation data risks

These risks stem from the developers’ choice of training and validation datasets.

Training a model on data encoding historical or social biases may result in similar biases in predictions, training on misrepresentative data without mitigating mechanisms – in performance disparities between majority and minority demographics. For instance, poor speech recognition of vernacular English varieties may cause faulty interactions or failure to serve some customers.

Read also: Creating Next-Generation AI Apps: Innovate with ChatGPT 

Validation datasets are often used to evaluate an ML model’s generalization beyond the training data, to new examples from the same distribution, or to examples with different characteristics.

 

Representative validation data can be used to detect potential mismatches between the training data and the deployment environment, such as the presence of social biases or spurious features in the training data.

Developers increasingly use pre-trained models like GPT-3 and BERT for processing unstructured data. This method reduces the control over training data for developers who don’t pre-train their own models and build on top of publicly released models or ML API services.

Learn more: How to Build a GPT Model: Prerequisites and Essential Steps

As popular datasets may include systemic labeling errors, stereotypes, and even adult content, it is important to consider possible negative consequences of using models pre-trained on these datasets. Publicly available models may also have been trained on private datasets that developers cannot audit independently.

If training datasets contained personal information, models may memorize it. This could harm privacy when cybercriminals use membership inference attacks to extract such information.

When training data matching a niche deployment setting is not available, developers may resort to approximation. This comes with the risk of domain mismatch, deteriorating performance.

Since labels are often crowdsourced, there is also a risk of bias being introduced due to the annotators’ sociocultural backgrounds. Other factors affecting the quality of labeled data include the expertise level of annotators, inter-annotator agreement, and overlaps between validation and training data.

Training on datasets scraped from the web makes manual data annotation hardly feasible. Such datasets often contain noisy labels, harmful stereotypes, and even illicit content.

 

Leakages of machine-generated data, such as machine-translated texts and AI-generated images, into training datasets introduce new challenges to machine learning development.

 

7. Implementation risks

Code implementation choices and errors also may result in system failure. Faulty coding and code review and integration practices lead to more bugs in system implementation. The intertwined nature of the data, model architecture, and training algorithm creates more challenges in ML system testing.

Read also: How to Do a Code Review Right – Onix’s Guide

Open-source software packages maintained by volunteers, such as PyTorch, increase the odds of introducing bugs into a system without the developers’ knowledge. API changes that are not backward-compatible are another source of bugs.

The use of external libraries, particularly when the development team isn’t familiar with the internals, increases the risk of failure due to bugs in the dependency chain. 

A library that is widely used and regularly updated by a paid team will likely be more reliable than someone’s hobby project, but it isn’t always the case.

Read also: Top 10 Java Machine Learning Libraries & Tools

Over-reliance on open-source libraries may also lead to critical systems failure if the dependencies are taken online.

Onix has worked with a variety of technologies on diverse projects. For example, below you can see Speech2Mindmap – a distributed system for automatic speech-to-mind-map conversion.
 

The project tech stack included the following Python libraries:

 

1. PyTorch – ML framework library that helps load ML models, evaluate the results, and build own ML solutions. We used model evaluation that helped with embeddings for the phrases and utilities from this framework to evaluate the similarity score using cosine similarity metrics.
2. Whisper – OpenAI’s automatic speech recognition neural network. We used its base model to extract text from speech.
3. spaCy – a library for NLP that can distinguish noun phrases and named entities (NP/NE) in a text using a preloaded model.
4. Sentence-transformers (SBERT) – a state-of-the-art BERT model modification that embeds phrases into n-dimensional space. It supports evaluation of huge numbers of embeddings (words) at low cost time- and memory-wise.

Read also: 7 Reasons Why Python Is Best for AI, ML, and Deep Learning

Mind maps are widely used, but their creation is not fully automated yet and requires specialized software and time. Particularly, human intervention is required for memory maps which would still somewhat lag behind an actual speech or discussion.

The Onix ML team designed a solution that processes speech to structured text sequences and returns ordered graphs nearly in real time. A mind map graph shows the speech flow and determines its theme and how it changes over time.

 

“As separate nodes’ weight can be determined autonomously, the objectivity of the results increases,” said the Tech Lead Oleksii Sheremet.

This solution can be implemented into a mobile application with a web-based core or apps running on mobile capabilities.

 

8. Insufficient robustness

An ML system may fail or be unable to recover upon encountering inputs that are invalid, noisy, or from a distribution different from the training distribution.

Out-of-distribution inputs include

 

• inputs that should be invalid
• inputs specially crafted to evade perception or induce system failure
• inputs that are noisy due to background noise, scratched/blurred lenses, typos, sensor errors, etc.
• natural variations, such as different accents or grammatical variants

For instance, objects that systems must recognize may appear different under various lighting conditions. Inability to handle deviations from the training data distribution, invalid inputs, or stressful environmental conditions may negatively impact safety or fairness.

A large deployment environment with changing conditions usually necessitates either a more comprehensive training dataset that can capture the full range of variation or a mechanism that makes the system resilient to input variation.

Operation in a public environment increases the possibility of adversarial attacks on a system. Adversarial training reduces robustness risk, but consumes extra computational resources during training and inference.

Ensuring model robustness is a continuous challenge in ML projects. Developers should also develop mechanisms enabling a system to recover from temporary failure.

Learn more: The AI Life Cycle: Unlocking Business Value through Strategic Implementation

 

9. Privacy and security requirements

ML often involves processing sensitive data, raising concerns about privacy and security breaches, especially in healthcare and finance.

Compliance with data protection regulations are also significant challenges.

Read also: A Guide to HIPAA Compliance for Software Developers

Privacy breaches often result from compromised databases. Decentralized storing of training data may help prevent this problem, but training examples may still be recovered from models. Information about the training data can also be retrieved from an ML model.

Besides data protection, organizations must protect the different processes that handle data, such as pre-processing, version control, analysis, and reuse.

There is also a risk of data loss or harm from intentional subversion or forced failure of ML systems. For instance, perturbing the input with small quantities of adversarially generated noise can induce mispredictions in neural computer vision models.

An ML model can also be ‘stolen’ through ML-as-a-service APIs by making use of returned metadata. Bad guys can use extracted models to craft adversarial examples to fool the original models.

ML systems tend to be vulnerable to attacks if their models have not been explicitly trained.

 

10. Control issues

The ability to shut down an ML system before it causes harm can significantly reduce its second-order risks and prevent negative consequences for individuals, entities, and environments.

ML systems have three levels of autonomy:

 

• human-in-the-loop (human execution)
• human-on-the-loop (human supervision)
• full autonomy

The first two require the ability to intervene quickly and either take manual control of or shut down the system. It must be easy for an observer to identify situations requiring intervention and to react appropriately. The latency of the connection to the ML system is crucial, so stakeholders should consider remote and on-site intervention options.

These factors are often connected to the design choice of a system’s non-ML components. For example, appropriate interpretability functionality may help a supervisor identify failures. Supervisors of high-stakes applications must be trained or even certified for the job.

Fully autonomous systems may be more difficult to regain control of if they malfunction. Developers must thoroughly design, program, and test contingency measures.

 

11. Data availability

Researchers use several approaches to address ML’s demand for vast amounts of data:

 

• Data augmentation technique uses pre-existing data to create modified copies of a dataset. 
• Transfer learning leverages the knowledge or parameters learned from one domain or task on similar domains or tasks. This reduces training time and data requirements. For instance, an NLP model may transfer the word embeddings learned from a large corpus to specific tasks like sentiment analysis or machine translation.
• Active learning involves selecting the most informative or useful data points for labeling, which can help the model optimize its data acquisition in interactive environments. For example, a face recognition system may ask for the names of the most unfamiliar faces.

Onix also has experience in face detection. For instance, an AI-based solution we made can be used to replace a character’s face with a user's face in short videos.

Onix tried a number of face detection methods when developing the face-replacing app for video streams.  

 

Read also: Live Video Streaming App Development Guide by Onix

Publicly available datasets, data-sharing programs, and collaborations also help with data availability.

 

12. Computational resources

Computational resources comprise the hardware, software, and computing infrastructure required to perform ML tasks. These include processors (CPUs or GPUs), memory, storage, and specialized hardware accelerators.

Inadequate computational resources

 

• limit ML models’ size and complexity, restricting their learning capacity and performance
• result in extremely long training times or delayed inference, reducing ML models’ efficiency and utility

The training of deep learning models, especially large neural networks and image recognition tasks, involves powerful GPUs, specialized hardware accelerators, and extensive memory, which can be expensive.

ML methods are also computationally intensive when used on massive datasets and distributed systems.

Practitioners must optimize their algorithms and code to make the most of available resources. Cloud computing services and shared computing clusters may provide on-demand access to substantial computational resources without significant upfront infrastructure investments.

 

13.Scalability challenges

When designers select models, it’s critical to strike a balance between performance and scalability. They can also apply techniques like model parallelism or distributed training to overcome scalability limitations.

Deploying ML models at scale involves setting up a robust infrastructure, integrating models into existing systems, ensuring low-latency predictions, and handling increased user load.

Containerization technologies, such as Docker, and orchestration frameworks like Kubernetes can help streamline deployment and facilitate scalability.

As organizations’ data volumes and user bases grow, ML systems may need to be re-architected to maintain performance. Scaling ML solutions to handle increased data volumes and user traffic can also be challenging and require substantial infrastructure investments.

 

14. Challenges of dynamic and interactive environments

ML systems are often deployed in environments where data, feedback, and objectives change over time. For instance, a recommender system encounters different consumers whose preferences and behaviors also change.

Beside transfer learning and active learning, two more techniques may help adjust your models to changes.

Online learning is an ML paradigm that updates model parameters continuously as new data arrives, rather than using a fixed and static dataset. A model can learn from the latest data and forget outdated or irrelevant data. Online learning is suitable when data is generated in a stream, such as web analytics, sensor data, or social media.

Ensemble learning involves training several models independently and then combining their predictions to produce a better output than any single model can produce.

 

This helps reduce the variance and bias of the individual models and achieve better accuracy, generalization, and robustness compared to individual models. This technique is suitable for scenarios where data is complex, noisy, or non-linear, such as classification, regression, or anomaly detection.

A real-world example of ensemble learning is the Random Forest algorithm, which combines multiple decision trees to make finance, healthcare, and marketing predictions.

Read also: How AI Technology Will Transform Customer Engagement

 

15. Continuous monitoring and maintenance

Machine learning systems can also change or degrade over time due to changes in data, the environment, or their problem.

Regular monitoring and validation against a holdout set can provide insights into model performance, allowing for timely adjustments.

Owners should constantly update their ML models and use re-training, fine-tuning, or transfer learning techniques to adapt them to new or changing situations. It’s a perpetual dance between adaptation and stability to ensure that the model learns from new data without forgetting valuable past information.

 

The Pitfalls of Machine Learning Implementation

These ‘second-order risks’ may be the symptoms of problems in ML systems’ design and development but may also be unexpected ‘side effects.’

In literature, algorithmic impact assessments are often used instead of risk assessments. This is understandable, since risks refer not only to what can go wrong for entities implementing ML systems but also to possible negative consequences for individuals, groups, institutions, or the environment.

There are also challenges and risks that are beyond ML adopters’ control. For example… 

 

1. Insufficient, disparate, or ambiguous regulations & standards

The growing demand for personal and corporate information protection has brought about regulations like the GDPR, HIPAA, or CCPA that dictate how service providers must store and process user data collected based on intent.

As the speed and scale of ML adoption grows, so does the need for regulators and private sector actors to identify risks and develop measures before proposed ML systems harm them.

Legislation regulating the sale and use of ML systems is also developing. The examples include the EU Artificial Intelligence Act, the US Algorithmic Accountability Act of 2022, and California’s Automated Decision Systems Accountability Act.

Accurately characterizing the risks posed by ML systems and their possible negative or positive impacts is crucial to meaningful regulations. A comprehensive taxonomy should enable organizations to recognize and address the problems of ML implementation and to define internal policies on ML applications not covered by laws.

Regulations should also prevent actors from intentionally or unintentionally misinterpreting requirements. Poorly defined notions create loopholes that can be exploited by malicious actors.

 

2. Risks of penalties and reputational damage

In the US, Section 5 of the Federal Trade Commission Act prohibits unfair or deceptive practices that also include the use of biased algorithms.

In December 2023, the FTC banned the Rite Aid Corporation from using facial recognition for five years. 

The FTC alleged that Rite Aidy’ AI-based surveillance system incorrectly matched some customers in its stores with identities of shoplifters and other troublemakers; this was more likely to happen in plurality-Black, Asian, and Latino areas. The company allegedly failed to:

 

• consider and mitigate potential risks from misidentifying consumers
• test, assess, measure, or inquire about the facial recognition technology’s accuracy before deployment
• prevent the use of low-quality images by the technology
• regularly monitor and test the system’s accuracy post-deployment
• adequately train employees operating this technology and flag that it could generate false positives
• implement a comprehensive information security program

“Rite Aid’s reckless use of facial surveillance systems left its customers facing humiliation and other harms, and its order violations put consumers’ sensitive information at risk,” said the Director of the Bureau of Consumer Protection. “

 

Today’s groundbreaking order makes clear that the Commission will be vigilant in protecting the public from unfair biometric surveillance and unfair data security practices.”

An organization may also incur significant damage when its ML system is proven or presumed to have harmed safety, health, fairness, human rights, businesses, or the natural environment.

 

3. Application and misapplication risks

These risks are firstly posed by an ML system’s intended application or use case. For example, an ML system that affects a company’s staff will have a lower upper bound of negative impact than a software deployed worldwide. The consequences of an image classifier’s error will be more severe in medical diagnostics than when it helps sort Lego bricks.

Read also: 26 Real-World Image Classification Use Cases Across 6 Industries

Negative consequences also arise when an adequate ML system is used for a purpose or in the way that was not intended by its creators. For instance, GPT-3 was found to be unreliable and dangerous for healthcare applications.

Read also: 2025’s Technology Trends in the Healthcare Industry
 

4. Emergent behavior risk

Although the most commonly discussed ML systems are trained on static datasets, active and online learning have models updated when new data becomes available. While this enables them to adapt post-deployment, it introduces the risk of acquiring novel undesirable behavior.

This risk is most relevant for robots and other embodied agents designed to adapt to changing environments.

Read also: Innovation in Construction: 5 Trends to Embrace in 2025

Novel behavior can also emerge due to interactions between similar systems (e.g., AVs on the road) or different systems (e.g., AVs and aerial drones).

 

5. Safety risks

While ML has the potential to improve the quality of life and revolutionize healthcare, manufacturing, transportation, finance, and education, it also has the potential to bring about new harms and exacerbate existing ones.

Read also: How AI Is Reshaping the Banking Industry: Benefits & Use Cases

Machine learning systems may harm people and communities through errors, inaccuracies, biases, unexpected interactions between ML models and real-world systems, and other ways. 

For example, a cancer identifier trained on insufficiently diverse data may issue unnecessary chemotherapy recommendations. A driverless car failing to recognize a pedestrian may run them over. Losing control of an autonomous weapon in time may result in shooting a civilian.

Read also: Deepfake Threats: How to Protect Your Business from AI’s Dark Side

 

6. Growing inequity risks

Even despite ML adopters’ good-faith efforts, systems gatekeeping access to jobs, loans, education, healthcare, privacy, and liberty still run the risk of discriminating against some demographics, e.g., due to stereotypes encoded in data.

The impact can take the form of accidents resulting from an ML-informed decision, denied loans, credit limits assigned based on gender, online abuse, etc. Gender- and racially-aligned discrimination by ML systems used for recruitment, education, automatic translation, and immigration have already been reported.

The benefits of ML adoption in healthcare, education, and other sectors will extend primarily to groups and nations with access to advanced technology and resources, while others will be left behind.

 

Businesses that can invest in ML solutions will also gain advantages over competitors that can’t. These factors will continue widening the present socio-economic gaps.

Read also: AI in Mental Health: Use Cases and App Ideas to Watch

 

7. Ethical use concerns

The ideals of fairness, openness, privacy, and responsibility should guide the appropriate development and ethical use of machine learning systems.

 

 

By adhering to these points, entrepreneurs can maximize the potential of ML while mitigating its limitations and ethical concerns.

 

8. Environmental risks

ML systems can harm the environment in three ways:

 

1. The consumption of resources during ML model training and inference may contribute to pollution and climate change. Owners must consider the energy-efficiency of the chosen algorithm, its implementation, and training procedure, the energy efficiency of a system’s computational hardware, and the type of power grid powering it.
2. ML systems’ predictions may impact the environment. This risk is related to a system’s use case, prediction accuracy, and robustness. For instance, an error by an ML system used for server scaling may raise electricity consumption.
3. Task automation often has knock-on effects, such as increased usage due to increased accessibility. For example, public transit users may shift to private AVs and cause a net increase in the number of vehicles on the road.

 

9. Institutional, organizational, and consumer resistance

This group of risks can be linked to AI-related misconceptions, fears, and prejudices. For instance, as AI systems become more independent and powerful, the old concerns about losing control over them proliferate. Possible consequences may range from investors’ hesitations to civil unrest.

The concerns about AI overtaking or rendering jobs obsolete are justified. McKinsey Global Institute’s 2023 report predicted that jobs accounting for 30% of the time worked in the US economy might be automated by 2030 thanks to generative AI.

Read also: Generative AI in Travel: Unlocking Personalized Experiences 

Others fear that in the data-driven world, humans are increasingly viewed as data generators whose behaviors can be predicted and controlled by capitalists. There are also risks of machine learning-powered misinformation, large-scale psychological manipulation, and dehumanization.

These concerns are exacerbated by the current insufficient research, evidence base, and public education and lack of established methods of AI impact monitoring and evaluation.

For instance, ML platforms don’t have access to customer data to detect unethical use by their customers. It will also be difficult to observe and prove AI’s negative impact or unjust treatment of some groups and communities and to identify the culprits.

 

How Onix Can Help with Your Machine Learning Problems

“Onix’s developers have consistently demonstrated expertise, innovation, and responsibility in tackling complexities and challenges in machine learning and data science for the benefit of our clients and advancement of AI technologies,” said Oleksii Sheremet, the Tech Lead at Onix’s ML Department.

 

Our team has completed hundreds of projects across diverse industries. The key areas of our expertise include:

 

Large language models (LLMs)

We deploy and integrate cutting-edge LLMs, such as LLaMA, Mixtral, or GPT-4, for text summarization, conversational AI, dynamic knowledge retrieval, and other purposes.

 

These models are tailored to support complex workflows, such as automating document analysis, generating contextual recommendations, and facilitating real-time decision-making.

For instance, Onix has recently trained an LLM-based chatbot to discuss political, historical, and social topics related to Ukraine’s modern history.

Onix’s AI Chef, a ChatGPT-based nutritional app, becomes better at recommending dishes as it learns each user’s preferences and feedback.

 

Our team specializes in fine-tuning LLMs to meet specific client needs, whether it involves improving model accuracy for niche domains, ensuring compliance with industry regulations, or scaling systems to handle high-demand environments.

 

Retrieval-augmented generation (RAG)

We specialize in implementation of RAG pipelines leveraging state-of-the-art vector databases to enhance data retrieval processes and provide contextually rich generative AI outputs.

 

Document interaction frameworks

The Onix team develops intelligent systems for analyzing and interacting with complex document structures. By integrating tools like LangChain, we enable efficient processing of PDF and text-based data, supporting interactive information extraction and context-driven responses.

 

Sentiment analysis

Our experts create advanced sentiment analysis tools for assessing public sentiment on social media, customer reviews, and feedback. Actionable insights empower businesses to make data-driven decisions and improve customer satisfaction.

 

Predictive maintenance

We build regression-based and ML models to monitor and diagnose industrial equipment. “Our solutions enhance reliability, optimize performance, and reduce downtime for measurable cost savings,” said our ML Tech Lead Oleksii Sheremet.

 

Speech-to-mind-map systems

The automation of real-time conversion of speech into dynamic and visually appealing mind maps enhances productivity, simplifies decision-making, and streamlines idea-sharing.

 

Text classification

The Onix team implements powerful classifiers to analyze content across diverse domains, including sensitive topics, for risk assessment and moderation. Our solutions ensure accuracy and efficiency in handling complex classification tasks.

 

Video analytics

Onix’s experts develop cutting-edge video analytics tools for crowd behavior analysis, facial recognition, and object tracking. These systems improve security, event management, and operational efficiency in real-time environments.

Onix created a crowd video analysis system that can be used for marketing or public space and event security, e.g., by detecting threats and risks and issuing alerts in real time.

 

Image processing

Onix deploys advanced image processing technologies for applications like object detection, face swapping, and high-resolution image generation. These tools cater to both creative and technical use cases with precision.

Learn more: Face Swap App Development

 

Anomaly detection

We develop advanced anomaly detection solutions for fraud prevention, quality assurance, and system monitoring. These solutions leverage ML to identify and mitigate irregularities in real time.

 

Time-series analysis

Onix deploys predictive models for analyzing trends and making informed decisions in industries like finance, healthcare, and energy. Our time-series solutions provide accurate forecasting and actionable insights.

 

Data visualization and statistical data analysis

Intuitive dashboards and visual analytics tools facilitate the representation of complex datasets in user-friendly formats. These visualizations assist in quick trend identification, effective decision-making, and clear data presentation. Complementing these capabilities, statistical data analysis ensures that visualizations are grounded in rigorous quantitative methods.

Advanced statistical models, ranging from descriptive analytics to inferential and predictive techniques, are employed to uncover hidden patterns and provide meaningful context.

 

This integrated approach supports clear data visualization while enhancing the understanding of underlying dynamics, enabling precise and confident data-driven decision-making.

 

Recommendation systems

Onix’s experts design personalized recommendation engines utilizing collaborative filtering and deep learning techniques. Our systems enhance user engagement and optimize sales for e-commerce platforms and content delivery networks.

 

Formulation and compliance

We develop cutting-edge tools for product formulae analysis and regulatory compliance, enabling real-time evaluation of product safety, transparency, and sustainability. These solutions integrate scientific data and global industry standards.

 

Smart library system

For a digital library system with automated book retrieval, PDF parsing, OCR for image-based documents, and metadata extraction, Onix’s experts employed NLP techniques for indexing, term extraction, and seamless navigation, ensuring efficient library management.

 

Social media monitoring

We implement comprehensive tools for monitoring trends and analyzing digital engagement on social media. These solutions help clients craft targeted marketing strategies and gain a competitive edge.

 

Chatbot development

Our team creates highly interactive AI-driven conversational agents for customer service and information retrieval. These chatbots are equipped with multilingual capabilities and tailored to meet unique business needs.

Onix built an enterprise chatbot based on Rasa using spaCy NLP to improve recognition and response to requests. 

 

DeepStack reimplementation

We have experience in reimplementation of a poker-playing AI with a modular development approach, emphasizing optimization of ML/DL workflows and enhancement of neural network efficiency.

This included designing flexible neural network architectures, utilizing advanced libraries and accelerators for improved performance, and seamless integration across diverse platforms.

 

Innovative data processing techniques were applied to advance AI capabilities in decision-making under uncertainty, with comprehensive documentation supporting scalability and further development.

 

Let’s Summarize!

Machine learning offers immense benefits, from enhanced decision-making and automation to improved customer experiences, security, and healthcare. However, it also poses risks, particularly concerning data privacy, bias, and ethical concerns.

Successful ML implementation is a strategic process that requires a deep understanding of both the capabilities and limitations of ML systems. Businesses that adopt best practices in data governance and bias mitigation and engage skilled and experienced ML developers can harness the power of machine learning while mitigating its potential downsides.

If you have questions about the technical, ethical, security, or other machine learning pitfalls or are not sure if ML is the appropriate choice for your startup or business, please send us a message. Our ML experts will be happy to talk with you and help solve your problem.

 

FAQ

 

Are there limitations to how well machine learning models can handle unstructured data?

No. However, unstructured data presents several challenges for machine learning teams: 

 

• The lack of standardized formatting makes data indexing, storing, retrieving, and management more challenging.
• The analysis and processing of unstructured data requires specialized pre-processing and feature extraction techniques and domain-specific models and tools.
• Unstructured data’s diverse origins and forms, coupled with storage across multiple platforms, raise security concerns.
• The storage costs are higher compared with traditional data management and storing methods.
• The integration of unstructured data with an organization’s structured data resources may be complicated.

 

What challenges do machine learning models face in dynamic environments?

In dynamic environments, ML models have to learn from new information and adapt to new scenarios, changing conditions, new data sources and data distributions, and unforeseen events to maintain the ML system’s accuracy and relevance.

 

How do ethical concerns limit the adoption of machine learning?

Concerns about machine learning include personal data misuse, unfair practices, discrimination, and potential abuse, leading to skepticism and mistrust. The lack of understanding of ML models’ work and decisions breeds reluctance to rely on or invest in ML. 

 

Disparate and ambiguous restrictive regulations may also stifle ML research, experiments, development, and implementation. Workplace dilemmas and economic inequality further undermine trust in ML, hampering its adoption and progress.

 

What are the risks of relying too heavily on machine learning systems? 

Increasing dependence on technology for information collection and analysis, decision-making, problem-solving, content generation, and other tasks may diminish critical thinking, understanding of complex systems and processes, and creativity. In the long term, such trends could lead to expertise erosion.

Blind trust in technology without adequate human oversight, participation, and insight can also lead to complacency and result in errors that are not immediately discovered and addressed.