What is AI Training

Artificial Intelligence (AI) is a transformative force in modern technology, powering innovations that range from voice assistants and self-driving cars to personalized advertisements and search engine results. At the core of these advancements lies a critical component: high-quality training data. Without this foundation, even the most sophisticated AI models cannot function effectively.

Whether you are a machine learning engineer, a data scientist, or a business leader looking to leverage AI, this white paper offers valuable insights into the intricacies of training data. By understanding these foundational elements, you can better navigate the complexities of AI development and harness its full potential.

Understanding AI Model Training Techniques

Training a model using data scraped from the web can involve different types of machine learning, depending on how the data is used:Unsupervised Learning: Data can be collected via web scraping or unlabeled data collections. A model trained on this data will find patterns within this data with no pre-annotations, and the model essentially creates its own patterns and labels.

Unsupervised Learning: Data can be collected via web scraping or unlabeled data collections. A model trained on this data will find patterns within this data with no pre-annotations, and the model essentially creates its own patterns and labels.

Supervised Learning: Data is labeled by skilled annotators, creating a ground truth for models to refer to when defining patterns. Then, new data is fed into the model, and labels are applied using the human-created data as a ground truth. 

Semi-Supervised Learning: Data is collected and scraped in vast quantities, but some human annotated labels are also created and fed to the model as part of the training process.

Companies partner with data annotation organizations that identify, qualify, validate, and employ workers across the globe with varying backgrounds and skill sets to annotate and make judgments on training data.

An alternative to using annotated data is Reinforcement Learning, which is an approach that trains a model with rewards. We’ll discuss this further in later sections.

The Impact of Data Annotation

Engineers and scientists require model management and data pipeline management to create a pathway for ingesting clean data into models to continue training them in supervised learning and reinforcement learning methodologies. IT resources across industries have had to focus on generating training data and labeling and validation. Suppose you’re looking to create a model that can recognize the language being spoken into a mic. In that case, engineers will build a dataset of samples of confirmed examples of that language text.

If you want to train that same model to turn audio samples of that language into text, you’ll need hours and hours of audio samples that are transcribed in a similar manner, adhering to guidelines, to train the model to transcribe future samples of that language. The machine learning model then learns to recognize that language in an audio sample and transcribes it into text.

If the transcriptions are inconsistent, the model’s output will be inconsistent. Guidelines and quality assurance are necessary to ensure consistency in your data sets.

Types of Data and Their Annotations

In AI development, various data types play distinct roles, each requiring specific annotation techniques to train models effectively. This chapter explores the primary data types—structured and unstructured data—along with their associated annotations. It also delves into specialized tasks such as video, audio, and text annotations, as well as the complexities introduced by Generative AI. 

Structured vs. Unstructured Data

Structured data refers to data that has a standardized format, such as the standard columns in a bank statement, which include the amount, the entity that charged you, and the date of the charge. On the other hand, unstructured data lacks standardization and typically includes free-form text or images, like the open text in a customer survey response.  

Image Data

In the development of AI models, image data plays a crucial role across various applications. For example, image recognition technologies, like those used in Google Image search or smartphone facial recognition, rely heavily on high-quality image datasets. It’s essential that data collectors have proper consent, especially since these images may contain Personal Identifiable Information (PII). Ensuring compliance with privacy laws and data management standards is non-negotiable.

A notable subset of image data is medical imagery. AI is transforming the healthcare sector, with early applications focusing on radiological image analysis. For instance, AI models are now capable of identifying tumors or other abnormalities in MRI scans with greater speed and accuracy than human experts.

Video Data

Video data contains rich, multifaceted information that surpasses still images. When annotating videos, various details, such as context, intent, and even body language, need to be labeled. This type of annotation is akin to labeling individual frames in a sequence, often down to the pixel level. For instance, video annotations are foundational for technologies like smart doorbells that can recognize and alert users about package deliveries.

Audio Data

Audio data, ranging from short clips to lengthy recordings, is essential for training models in speech-to-text (STT) and text-to-speech (TTS) applications. These models are integral to voice assistants and medical devices that generate synthetic voices for patients who cannot speak.

Annotations in this domain often involve creating Reference Transcripts, also known as Golden Data, which align transcribed text with the original audio to ensure accuracy.

For example, consider the challenge of developing a voice recognition system intended for global use. The data collection process must account for a diverse range of speakers, each with unique backgrounds, accents, and locales. Variations in pitch, tone, and speed further complicate the task.

Additionally, real-world audio is rarely captured in pristine environments; background noise, overlapping conversations, and filler words like “um” and “ah” must be carefully annotated and handled to ensure the model’s robustness. By incorporating this diversity in training data, AI systems can better understand and respond to users across different regions and contexts.

Sensor Data and Multimodal Data

In AI applications, sensor data plays a critical role, especially in fields like autonomous driving and robotics. This data can be structured, as seen in the output of specific sensors like LiDAR (Light Detection and Ranging) and Radar. 

LiDAR uses light to create detailed 3D maps of the environment, enabling machines to perceive depth and spatial relationships. It’s particularly essential in autonomous vehicles for generating real-time 3D models of the surroundings. 

Radar complements LiDAR by using radio waves to measure distance, location, and speed. The fusion of data from multiple sensors, such as LiDAR and Radar, allows for more accurate and reliable decision-making in AI systems, like those used in self-driving cars.

Text Data and NLP Annotation

Text data is at the heart of Natural Language Processing (NLP) applications. For instance, intent classification—a key task in chatbots and virtual assistants—relies on accurately labeled training data. When a user interacts with a customer service bot, the model must identify the intent behind the query and extract relevant details. Properly annotated data ensures that the model can predict user intentions accurately.

Additionally, in multilingual contexts, translation models must not only translate but also localize content, ensuring that phrases like “cup of joe” are culturally and linguistically appropriate in the target language.

Generative AI Annotation

Generative AI (GenAI) has created an entirely new avenue of annotation requirements that uplevel complexity of responses that annotators rank, write, and rewrite. New tasks are becoming a subset of annotation training aimed at enhancing GenAI and machine learning model performance.

These tasks include the following and many more:

Crafting and refining prompts: To generate accurate, relevant, and contextually appropriate responses for large language models (LLMs). Adjusting both the input prompts and the generated responses to optimize model performance and ensure high-quality outputs. 

Factuality Testing and Verification: Implementing methods to test and verify the factual accuracy of the model outputs, maintaining reliability and trustworthiness in AI-generated content.

Model Output Ranking: Evaluating and ranking the outputs generated by models to ensure that the best possible responses are selected and used. 

Adversarial Testing: Creating hypothetical scenarios to test how the AI models handle and correct potentially misleading or incorrect inputs.

Data Labeling Processes

In machine learning, data labeling is the process of identifying raw data (images, text files, videos, etc.) and adding one or more meaningful and informative labels to provide context so that a machine learning model can learn from it.
These tasks include the following and many more:

The Decisions Associated with Data Pipelines

When creating an annotation approach, you need to first understand how to handle the necessary volume of data and then determine the method and personnel (or technology) for labeling your data. Some annotation providers can leverage efficiencies by utilizing automated or pre-labeled data, but humans still need to be in the loop and provide and validate annotations at multiple points (if not entirely) throughout the process.

Human Exclusive Labeling

AI is built by humans for human goals, and humans have long been the main source of truth for labeling and validation. We want humans to find the right answer and establish the truth, to improve user experience, while being faithful to the model’s intended tone, style, and much more. Humans have a subjectivity that makes relevance and intent depend on human-annotated data. A human can still decide which answer is best for the specific purpose of the question being asked. So, for natural language processing and most kinds of interaction (image, audio, and video) humans can provide much better labels than models. While Human labeling may incur slightly higher costs, it can also be more effective, leading to more of the data set being used for model training due to its high quality. This can lead to the same or lower spending for better-quality data, thereby reducing training cycles. Humans also have biases, so by creating a diverse data creation community, you can train your model to reduce some of that bias (although it can never be fully eliminated). Machine labels are trained on the same data repeatedly, which can significantly increase data bias, exacerbating the problem.

The benefits of human labeling include:

Improved precision: Humans help fix mistakes that machines might overlook, resulting in better data quality. 

More contextual awareness: Humans can offer subtle insights and context that machines might not understand, enhancing the usefulness and suitability of the data.

Increased Reliability & Efficiency: The repeated feedback cycle between humans and machines helps improve algorithms over time, making the system more dependable and efficient. At the same time, humans continue to refine their skills and become more efficient and consistent at labeling over time. However, it is important to note that there is also an opportunity to manage workforces that cycle on and off projects to allow fresh sets of evaluations and annotations, particularly in more subjective data annotations. A workforce management partner can advise when this is most appropriate.uts.

Machine Labels

Some training companies claim that they can provide only Machine labels, and that this can replace or reduce the need for human labelers. However, recent industry research shows that using outputs from other models as training data creates a harmful cycle that amplifies errors and convergence of models. Machine labeling has a role in some use cases but should not be relied on by itself.

The preferable approach is “Humans in the Loop,” which enhances machine labels with human-provided annotations and is a way by which reinforcement training can take place. Industry leaders in data labeling have always relied on humans as the main source of truth Finally, it’s important to be cautious of poor labeling practices. There are various methodologies for measuring annotator agreement, using a wide range of annotations and associated metrics with those outputs to provide clean data inputs into data pipelines. It’s essential to work with your data pipeline partner on requirements and use a trusted advisor to plan and define your workflow requirements including whether humans are providing the labels.

Security and Compliance in Data Annotation

Key security requirements include:

Email, Phone, and IP Verification: These measures help confirm the identity of annotators and prevent unauthorized access. 

Location Verification: Ensures that annotators are operating from approved locations, reducing the risk of data breaches. 

Identity Verification: Involves verifying the identity of annotators through various means, such as government-issued IDs. 

Automated Identity Assurance: Uses automated systems to continuously verify the identity of annotators. It isn’t enough to check once to ensure annotators are who and where they say they are. It must be a continuous process throughout the lifecycle of a project or program.

Behavioral Flags: Detects unusual behavior and prevents the creation of multiple accounts by the same individual.

These measures collectively ensure that the data annotation process is secure and that the workforce is reliable and trustworthy.

Location of Annotators

The location of annotators can significantly impact the data annotation process’s quality and efficiency. Annotators can be located:

Onsite: In secure facilities designed to handle sensitive data. These facilities are equipped with the necessary infrastructure to ensure data security and are often used for high-sensitivity tasks. 
Remote: Annotators working from home or other remote locations. This model offers flexibility and access to a broader talent pool but requires robust security measures to ensure data protection. 

Offshore: Leveraging annotators in different countries to take advantage of cost efficiencies and specialized skills. This approach requires careful management to ensure compliance with local regulations and standards.

On-site Facilities

Onsite facilities are essential for tasks that require high levels of security and control. These facilities are equipped with secure infrastructure and are designed to handle sensitive data. Key features of onsite facilities include:

High-Sensitivity Moderation: Ensures that sensitive content is handled appropriately.
Secure Facility Options: Provides a controlled environment to prevent data breaches and unauthorized access.

Remote

Remote annotation offers flexibility and access to a diverse talent pool. Key considerations for remote annotation include:

Identity Assurance: Implementing measures to verify the identity of remote workers. 
Real-Time Monitoring: Ensuring that remote workers are monitored in real-time to maintain high standards of security and quality. 

Quick Ramp Times: The ability to quickly scale up the workforce to meet project demands. 
Reduce Worker Collusion: Remote work environments naturally reduce the chances of collusion, as workers are isolated from one another. This means there is no risk of biased or homogenized data that does not accurately reflect individual effort or interpretation.

Remote annotation is supported by a robust infrastructure that ensures data security and quality, even when annotators are working from home.

Employment Model

The employment model for data annotation can vary based on the specific needs of the project. Common models include:

Crowd: Leveraging a large, diverse group of annotators to handle a wide range of tasks. 
Managed Service: A dedicated team of annotators managed by a service provider to ensure consistency and quality. 

Staffing: Hiring specialized annotators for specific tasks or projects.

These models can be adapted to different locations, whether remote, nearshore, or offshore, to provide the necessary flexibility and specialization.

Offshoring

Offshoring involves leveraging annotators in different countries to take advantage of cost efficiencies and specialized skills.
Key considerations for offshoring include: 

Cost Efficiency: Reducing costs by leveraging lower labor rates in different geographies. 

Specialized Skills: Accessing specialized skills that may not be available locally. 

Compliance: Ensuring compliance with local regulations and standards.

Offshoring requires careful management to ensure that the quality and security of the data annotation process are maintained.

Security Certifications

Adhering to industry standards and obtaining relevant security certifications is essential for ensuring the security and compliance of the data annotation process. Key certifications include:

ISO: International standards for information security management. 

SOC2: Standards for managing customer data based on five “trust service principles”—security, availability, processing integrity, confidentiality, and privacy. 

HIPAA: Standards for protecting sensitive patient data in the healthcare industry.

These certifications demonstrate a commitment to maintaining rigorous security protocols and ensuring the confidentiality and integrity of client data.

The Importance of Language and Culture

Language skills and knowledge are essential for your annotation workforce. Annotators must be able to deal with the ambiguity of language in their annotations. Natural language has variations, idioms, words that have multiple meanings, and much more complexity than we can capture with rules-based models. This is why humans are still the best source of annotation.

Language also differs across locations. For example, think of English in America, Canada, and Australia, and English or Arabic in Morocco with French mixed in versus Modern Standard Arabic in Egypt. Each variation can affect the results slightly. For this reason, it’s crucial to have a wide range of variations and locations for annotating data, but it’s also important to consider who is doing the annotating. Having an English person annotate an American English model could lead to inconsistency.

Cultural context is also important. When building models that rely on cultural understanding, it’s crucial to engage annotators who are not only familiar with historical context but also current slang and social dynamics. For instance, an annotator who has not been in a country for the past 5 years may not be the best fit for annotating content such as a food advertisement for the most popular local restaurant, as they may not be aware of the latest meanings and trends. In developing models and seeking partnerships, it’s vital to find a partner capable of handling large datasets from different locations.

Modern models require extensive training and tuning to ensure they are inclusive and accessible to diverse global populations. Succeeding in a new market is neither easy nor quick, so partnering with a provider that has an established presence can offer the geographical coverage needed to swiftly meet data requirements.

Data Annotation Platforms

Data Training depends on who (or what!) is doing the data labeling. To label data, you need a reliable and effective data annotation platform for users, whether they work for you or for others, remotely or onsite.
Many companies find themselves with one of two choices:

When deciding which to pick, it’s important to know the requirements. If you need to keep all the data and workflow within your system, and if you need to build your own IP, then creating a platform (with enough money and resources) is possible.

Another choice is to use a third-party annotation platform, where you would have less control over your platform annotation workflow, but you would not need to invest in building tech in an application form. This can be the right choice for many companies, as the annotation worksurface can be standardized and requires less investment.
Many companies choose a hybrid approach: utilizing a third-party annotation platform to gather data and create annotations while also implementing additional enhancements, such as connectors to inbound workflows, pre-labeling, quality checks, and benchmarking.

The Welo Data platform provides various options, and Welo Data offers a team that can collaborate with any platform required by the customers. This flexibility is essential in a dynamic environment. Most importantly, data is not shared across platforms and customers, so customer data is not utilized to train other customer data. This helps to ensure that you retain control over your model.

Ensuring Data Quality

Data quality is crucial for machine learning, as the learning process depends on data. If you have bad data or labels, you will have bad outcomes. We want to give you the most important quality metrics and discuss how to best ensure that your labeling quality is high.

Data annotation plays a pivotal role in training robust AI models. Whether you’re working on natural language processing, computer vision, or any other machine learning task, ensuring high-quality annotated data is essential.

Dimensions of Quality
Quality is the compass guiding effective data annotation, whether you’re building your own pipeline or collaborating with third-party providers. In this dynamic landscape, defining the right parameters tailored to the data type and the ultimate goals of your AI models becomes pivotal.

Subjectivity and Relevance:
Relevance work often dances on the edge of subjectivity. What’s relevant to one annotator may not resonate with another. Here, inter-annotator agreement – the alignment of responses across multiple annotators – becomes our beacon. When consistency emerges, we harness it as valuable input for model training. Relevance involves incorporating diverse perspectives while maintaining consistency. It ensures that annotated data represents a comprehensive view of the subject matter.

Some best practices include:

Inclusion of Voices: Source data from diverse raters to capture different viewpoints. This diversity helps in creating a more holistic and accurate dataset. 

Consistency: Ensure uniformity across annotations while accommodating context. Training annotators to adhere to standardized guidelines helps in achieving this consistency.

Stakeholder Alignment: Collaborate with stakeholders to define relevance criteria. Engaging with various stakeholders ensures that the criteria used for determining relevance are comprehensive and aligned with the project goals.

Imagine annotating images for “person” or “dog.” In this case, there are clear right or wrong answers. However, if you ask about the emotions of the object (whether a person or a dog), you could have a variety of interpretations. Contextual nuance, therefore, becomes a delicate balance between specificity and inclusivity. Raters must be trained to adhere to the overall principles of the model goals. Someone (or some teams) must define the North Star or Golden Data.

Golden Data Set Adherence:
The golden data set captures these harmonious responses—the agreed-upon correct annotations. It’s our benchmark and guiding principle.

Managing workforce adherence—ensuring raters stay in sync with the golden data set—is our backstage choreography. Specialized training programs fine-tune, rehearse, and ensure that every annotation aligns with our guiding principle.

Quality isn’t a static milestone; it evolves with the task and as culture changes through time. At Welo Data, we use our proprietary quality framework to define customized quality solutions for each program or project.

Accuracy
Accuracy refers to aligning annotated data with established guidelines and standards. It ensures that data deliverables are free from anomalies and adhere to applicable requirements.

Best Practices:

Guidelines Adherence: Clearly define annotation guidelines for annotators.

Quality at Source: Set teams up for success by emphasizing quality from the outset.

Continuous Monitoring: Regularly assess performance trends and identify error patterns.

Corrective Actions: Implement corrective measures based on monitoring results.

Fidelity
“Fidelity” refers to the practice of ensuring that annotated data is trustworthy, original, and accurate. It guards against misrepresentation and fraud. Welocalize uses a variety of industry-leading techniques to ensure that Fidelity is consistently and continuously ensured throughout the data pipeline.

Best Practices:
Identity Validation: Implement compliance and identity assurance checks to ensure accurate and true location, identity and quality. 

Originality: Verify that data is not copied or misrepresented; checks using AI tools to validate that work is human created, in the tone of style guide. 

Quality Assurance: Conduct thorough assessments before delivery.

Quality Measurements
High-quality data annotation is crucial to models, and there are many dimensions, as discussed above, to the data pipeline. Actual primary measures of data annotation quality are important to yield highly meaningful and impactful data sets, improving the performance of algorithms for your enterprise.

A few examples of common measures include:

Krippendorf’s Alpha
This measurement assesses the level of agreement among annotators that goes beyond what can be attributed to chance alone. Krippendorf’s Alpha is well-suited for complex annotation tasks that involve a range of agreement values, such as tasks requiring a ranking on a scale of 1-5. The values of Krippendorf’s Alpha range from 0 (indicating perfect disagreement) to 1 (indicating perfect agreement).

Cohen’s Kappa
Cohen’s Kappa is one of the more widely used metrics for assessing Inter-annotator agreement. This method of measurement accounts for agreement and chance agreement, allowing for measurement between two annotators, especially for binary and nominal data. This is particularly effective for ensuring consistency across less subjective tasks for large rater pools with rapid turnaround time data. The scale ranges from -1 (worse than chance agreement), to 1 (perfect agreement).

F1 Score
F1 Score is a common metric in machine learning that takes into account both precision and recall, two important metrics for evaluating model performance. Precision is calculated by measuring the consistency of correct answers, while recall measures correct answers vs. incorrect answers.
Imagine you’re taking a test: Precision would be like answering only the questions you are absolutely certain are correct. For example, if you answer 50 questions out of 100 and all 50 are correct, your precision score would be high. On the other hand, recall measures your ability to answer all the questions related to the test material. If each of the 100 questions covered a different topic, you could only score 100 if you understood all of the material.

The F1 score combines precision and recall giving a balanced measure of the model’s ability to be accurate while also responding to as much relevant information as possible.

Workflow for Quality Assurance
Quality does not occur by chance, nor is it a simple process to create high-quality data at scale. Data annotation processes are meticulously designed to ensure robust AI models that perform accurately across diverse contexts, domains, and subjective reasoning and assessment.

Pre-Production
When designing the data pipeline workflow, it is crucial to pay attention to the initial steps. Understanding the details and defining the requirements upfront is essential for ensuring the quality of the output. It’s important to remember the well-known saying, “Garbage in, Garbage Out.” These pre-production steps ensure that your annotators are best positioned to prevent garbage being input into the data stream.

Resource Selection: When choosing annotators, consider their relevant expertise. This is more complex than it may appear. It’s essential to identify the skills, knowledge, expertise, and experience needed for different data types. This requires a holistic view of annotators. Simply having a PhD in a subject is no longer sufficient to make someone an expert annotator. 

Clear Instructions: Unambiguous guidelines should be provided with a process for updating and training on guideline changes implemented throughout the workflow. 

Pre-production Assessment: Continuously assess performance with the right quality measure for the data type required.

Monitoring and Evaluation

Use the quality metrics mentioned above, as well as any additional metrics, to regularly assess the data annotations. Many programs can support almost real-time evaluation and can ensure that if data quality declines (for example, if an annotator is having a difficult time), annotations can be paused, and corrections can be made immediately.

Establish standard operating procedures (SOPs) to achieve optimal efficiency and viable data quality for both data workflows, training, and corrective actions.

Capture and analyze data to identify areas for improvement. Standardized error annotation workflows are in place and adaptable based on project type

Continuously monitor accuracy to ensure raters are who and where they say they are to protect your data pipeline.

Performance Management

In addition to productivity measures and performance indicators, use golden data sets to assess your annotation workforce performance.

Rater Ranking: Combine program and quality metrics to identify the best and worst performers. Provide additional training and incentives to support improvement. Assess rater performance and assign annotators best matched for task requirements, creating an extremely efficient workflow. 

Accuracy Checks: Analyze worker data to identify specific tasks that result in reduced annotator agreement or that reflect incomplete or ambiguous guidelines. Ensure your annotation workforce understands the impact and importance of their work. 

Continuous Learning: Launch Quality Improvement Plans and train and optimize based on insights.

Best Practices for High-quality Data Sets

For High quality data sets, there are many best practices employed across the industry. The primary focus is to start with the best source of data, which would be the most appropriate and applicable workforce.

If you do not start with the guiding principle of the right type of annotators, no amount of machine data or automated quality processes can fix your data pipeline.

Conclusion

When defining your data pipeline process, consider various factors such as selecting the annotation platform, collaborating with third parties who can handle the platform, workforce, and workflow, and ensuring the delivery and management of quality data. It’s crucial to be mindful of the complexities that can impact the accuracy of your model output, as this can greatly influence your enterprise’s adoption and implementation of AI. These processes are not simple, and this white paper aims to help you understand the nuances required to create a data pipeline that aligns with your objectives. Remember that you can always take a step back, assess, and make necessary adjustments and changes.