“Ground Truth” in Data Science: a pillar for reliable AI models!
Define the concept of “Ground Truth”
The ground truth, in Artificial Intelligence, is a concept that is very recognized and respected in the fields of Data Science. This concept refers to data that is labeled and considered to be perfectly correct, accurate, and reliable. This is the foundation on which AI algorithms learn, and are capable of making decisions similar to those that a human being could make. Ground truth is the reference, the ultimate objective, the unique and reliable source of data guiding the accuracy of each analysis and element usable by a model.
The “ground” in ground truth refers to the characteristics of reality, the concrete truth that machines and data analysts strive to understand and predict. It is the real state of affairs against which all the outputs of a system, of a model, are measured.
What is the role of “Ground Truth” in machine learning and data analysis?
In machine learning and data analytics, ground truth acts as a compass in the field, directing models toward reliability, accuracy, and comprehensiveness. Without ground truth, AI models can go astray, leading to erroneous applications and inappropriate or biased decisions.
The ground truth is not static; it evolves over time, reflecting changes in motives and truths. Its dynamic nature underscores its importance, pushing Data Scientists and Data Engineers to continuously refine and validate their training data to match current truths.
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Establishing the “Ground Truth” through data collection and annotation
Collecting data and associating it with a label, a well-known label, can be a daunting task at first glance, especially in areas like image recognition, where the object identification, people, or patterns in images can be subjective. However, several methods of creating a “ground truth” data set can be used to anchor your data in reality, that is to say in the “truth”:
Expert labelling and consensus
Hiring data annotation experts to complete the tedious tasks of labeling data can be an initial step of truth. However, it is important to recognize that subjectivity exists in manual annotation tasks (i.e., made by humans).
To mitigate this, a consensus approach can be implemented, ensuring the validity of labelled data through majority agreements. Didn't you understand? Let us explain to you: “consensus”, in Data Labeling, refers to the process where several people independently evaluate the same set of data to assign labels or classifications. Consensus is reached when the majority of these evaluators agree on a specific label for each piece of data. This process is critical to ensuring the quality and reliability of data used in machine learning and other artificial intelligence applications.
In other words, the data to be labeled is distributed to several annotators. Each annotator evaluates the data and assigns labels to it independently, without being influenced by the opinions of others. Once tagging is complete, the labels assigned by different annotators are compared. Consensus is generally defined as the label (or labels) that the majority of annotators agree on. In some cases, a specific threshold is set (for example, an 80% agreement).
In complex annotation processes, consensus is typically measured using inter-annotator agreements, often referred to by the English term “Inter-Annotator Agreement” or “Inter-Rater Reliability”. This term refers to the extent to which different annotators (or evaluators, or even Data Labelers) agree in their evaluations or classifications of the same data. This concept is essential in many areas where subjective judgments need to be standardized, as is the case in areas where data sets can be extremely ambiguous, such as surgery or psychology.
Integrating human judgment into the annotation cycle
Integrating human judgment into consecutive loops in the data labeling process can refine and converge ground truth labels. The platforms of Crowdsourcing offer a vast pool of potential labellers, helping in the data collection process. However, it is important to note that crowdsourcing is not the only method for high-quality data labelling. Alternatives exist, such as the employment of specifically trained experts, who can provide a deeper understanding and specific expertise on complex topics.
In addition, semi-supervised learning techniques and reinforcement learning approaches can be used to reduce dependence on large, manually labeled data sets, by allowing models to learn and improve progressively from small sets of high-quality annotated examples. These methods, combined or used independently, can contribute to increasing the efficiency and accuracy of data labeling, leading to more reliable results for learning artificial intelligence models. At Innovatiana, we believe that it is best to employ experts to annotate smaller data sets, with a much higher level of quality!
Increased automation and consistency checks
Leveraging automation in the labeling process, through specialized artificial intelligence models, can dramatically speed up tedious annotation tasks. This approach provides a consistent method and reduces the time and resources required for manual data processing. This automation, when properly implemented, not only makes it possible to process a massive volume of data at an impressive speed, but also ensures consistency that can be difficult to achieve with human labeling.
However, automation has its limits and requires continuous validation by human stakeholders, especially for image data, in order to maintain the accuracy and relevance of truth-ground data. Automation errors, such as data biases or misinterpretations due to the limitations of current algorithms, need to be constantly monitored and corrected. In addition, the integration of regular human feedback makes it possible to adjust and improve AI models, making them more robust and adapted to the subtle and complex variations inherent in real world data.
By combining the capabilities of automation and human expertise, we can achieve an optimal balance between efficiency, precision and reliability in the data labeling process, essential for creating rich and varied databases that are essential for training efficient artificial intelligence models.
What are the real applications of Ground Truth in AI, in Tech and Startups in particular
The use of quality datasets and in particular datasets”Ground Truth“resonates across the technology services sector and tech ecosystems, stimulating innovation and promoting growth. Here are some use cases that we identified in our various missions, all of which were facilitated by the use of high-quality big data:
Improving the accuracy of predictive models in Finance
By using “Ground Truth” data for the design and development of predictive models in finance, it is possible to predict trends, demands, and risks with unprecedented accuracy. This level of foresight is essential for making decisions that are proactive and based on data (rather than assumptions).
Facilitating decision making with data”Ground Truth“
The ground truth allows businesses to make data-based decisions that resonate with the needs of their markets. It provides the assurance needed to take calculated risks and chart strategic paths for growth.
Natural Language Processing (NLP)
Ground truth datasets make it possible to train AI models to understand, interpret, and generate human language. They are used in machine translation, sentiment analysis, speech recognition, and text generation.
Fraud detection and prevention using “Ground Truth” datasets
In the financial sector, models trained with accurate datasets can identify fraudulent or anomalous behavior, such as in the case of suspicious credit card transactions.
Precision farming
The use of ground truth datasets helps to develop AI solutions for the analysis of satellite or drone data in order to optimize agricultural practices, such as the detection of areas requiring irrigation or particular treatments.
What are the challenges associated with obtaining “Ground Truth” datasets?
Despite its irrefutable importance, obtaining and maintaining ground truth data is full of obstacles that require skilful management. These represent so many challenges for Data Scientists and AI Specialists. These challenges generally relate to the following aspects:
Data quality and accuracy
Maintaining data quality is a perpetual struggle, with inaccuracies and misinformation that can seep through various information channels. Ensuring the intact nature of your ground truth data requires constant vigilance and the implementation of robust quality controls.
Subjectivity and bias in labelling
Human perception prevents perfect objectivity, and this often colors data labeling processes, introducing biases that can skew representations of the ground truth. Mitigating these biases requires a thoughtful and thoughtful approach to label assignments and validation processes.
Coherence in time and space
The ground truth is not only subject to temporal variations, but also to spatial disparities. Harmonizing ground truth labels across geographic points and time boundaries is a meticulous undertaking that requires thorough planning and execution.
Some strategies to adopt to reinforce your Ground Truth
To build a resilient ground truth, an arsenal of tactics and technologies must be employed. Here are some strategies to consider:
Rigorous data labeling techniques
The implementation of strict data labeling methods, such as labeling”Double pass“and arbitration processes, can strengthen the reliability of your ground truth data, ensuring that it accurately reflects the reality it aims to represent.
Harnessing the power of Crowdsourcing or validation by experts
Mobilizing the collective intelligence of experts can offer diverse perspectives, enriching the breadth and depth of your ground truth data. Validation by experts serves as an important checkpoint, affirming the credibility of your labelled data.
Use of tools to industrialize annotation
Les data annotation platforms can speed up the labeling process, by establishing rules and mechanisms for managing annotation teams, monitoring their activities and behavior (for example: is the time spent by an annotator on annotating an image consistent with the objective). Perhaps this time is too short or, on the contrary, too long, which is an indicator of the quality and consistency of the data). These tools, when complemented by human surveillance, can constitute a powerful team alliance in building the truth on the ground.
💡 As we venture into an age characterized primarily by omnipresence and complexity of data, our ability to discern and define the truth on the ground will mark the distinction between progress and obsolescence. The future of AI lies in convergence of ground truth and innovation.
Focus on data quality to create a “Ground Truth” dataset: what is the best approach?
This is a question that we are often asked at Innovatiana... if there is no single answer, we must recognize that there is a lot of prejudice in the community of AI specialists, as to the best method for producing reliable data. These prejudices are linked in particular to the excessive use of platforms for Crowdsourcing (such as Amazon Mechanical Turk) over the last decade - and the resulting (often) reduced data quality.
Prejudice 1: a consensus approach is essential to make my data reliable
As a reminder, a consensus annotation process involves the mobilization of a multitude of annotators to review the same object in a data set. For example, it could be asking 5 annotators to review and annotate the same payslip. Then, a quality review mechanism will determine a reliability rate based on the answers (for example: for 1 annotated payslip, if I have 4 identical results and 1 result in error, I can estimate that the reliability of the data is good for the object treated).
This approach has, of course, a cost (efforts must be duplicated) that is both financial and, above all, ethical. Crowdsourcing, which has been very popular in recent years, has tried to justify the use of service providers freelance located in low-income countries, paid very little and working on an ad hoc basis, without real expertise and without any professional stability.
We believe that this is an error, and while the consensus approach has virtues (we think in particular of medical use cases, which require extreme precision and do not allow for error), simpler, less expensive approaches, and more respectful of data professionals, such as annotators, exist.
For example, an approach”Double pass“, consisting in the complete review of the labels by successive “layers” (1/ Data Labeler, 2/ Quality Specialist, 3/ Sample Test), offers results as reliable as a consensus approach, and above all much more economical.
Prejudice 2: a quality data set is necessarily 100% reliable and contains NO errors
This is of course completely untrue! From our previous experiences, we have learned the following lessons:
1. Rigor, not perfection, is the foundation of a solid data quality strategy.
Artificial intelligence models are very resistant to errors in data sets: a quest for perfection is also incompatible with human nature, impractical and useless for models.
2. The ground truth is obtained through the manual work of human annotators... and the error is human!
Humans inevitably make mistakes (typos, careless mistakes, etc.). It is impossible to guarantee a 100% reliable data set.
3. Your AI model doesn't need perfection.
For example, deep learning models are great for ignoring errors/noise during the training process. This is true as long as they have a very large majority of good examples, and a minority of errors: what we guarantee in our services!
From this, we have deduced some main principles of quality control that we use in the context of our missions. We encourage our customers to apply these same principles when controlling the datasets we annotate to meet their needs:
Principle 1 : Review a random subset of the data to ensure that it meets an acceptable quality standard (95% minimum).
Principle No. 2 : Explore the distribution of errors found during random reviews. Identify Patterns and recurring errors.
Principle #3 : When errors are identified, search for similar assets (for example: text file of the same length, image of equivalent size) within a data set.
💡 Do you want to know more? Discover our article and our tips for building a quality dataset !
In conclusion
The quest for Ground Truth is not just an academic exercise but a vital Data Science undertaking. It underlies the integrity of our analyses, the validity of our models, and the success of our technological innovations. By investing in processes and technologies that improve the accuracy and reliability of ground truth data sources, we are essentially investing in the future of informed decision-making and strategic foresight (and not just in the future of artificial intelligence).
The challenges are significant and the work is demanding, but the rewards — increased insight, improved results, and a deeper understanding of our increasingly complex world — are unequivocally worth the effort. As artificial intelligence advances, let's evangelize the importance of ground truth and the use of human annotators to prepare the data used as the foundations of models!
