10 common questions about getting data for AI
Artificial intelligence (AI) plays an increasingly essential role in a wide range of sectors, from health to finances or Real Estate. However, AI, in most of its commercial applications, is extremely dependent on data (not just GPUs/TPUs!) , and obtaining high-quality data is often a major challenge for teams of Data Scientists and developers. They rarely have expertise in managing pipelines of important data requiring manual qualification, at a granular level. In this article, we explore ten questions that these teams frequently ask about how to get data for AI projects, and how to approach them strategically and ethically.
1. Where do I start with my data?
Over the past decade, businesses across industries have accumulated huge amounts of data. Still, it can be hard to know where to start when it comes to using them for AI. The key is to get back to business goals. Identify these goals and then work to determine what data is needed to achieve them. Starting by trying to understand your data can be a complex task, especially for teams of technical experts and Data Scientists who are rarely trained in functional issues. It is then a question of working jointly with functional experts to target the main objectives of the future AI product.
2. How can I be sure that the data to be annotated is representative of the cases that the AI model will encounter in production?
One of the common mistakes is to assume that training data will be the same as production data. In reality, they can often differ considerably. To avoid surprises, you need to maintain close communication with functional and business experts to understand what the data will really look like in production. There are always atypical cases... (for example, We will think of the Tesla's on-board computer, unable to recognize an unusual vehicle, namely a cart!).
3. How can I avoid biases in my data?
Data bias is a major problem for AI. They can take a variety of forms, from societal or racist biases, to unrepresentative data sets. The only way to combat bias is to be proactive. It's about staying up to date with the latest research on AI ethics and establishing responsible processes to reduce bias, based on recommendations such as those from Google AI and the framework IBM Fairness 360.
A response from Data Scientist teams to this problem is to source annotators from all over the world (by outsourcing to India, the Philippines, Madagascar, Spain, etc.) or to use crowdsourcing. Although practical, this answer is rarely sufficient since it is almost impossible or almost impossible to build a team as diverse as the human species! On the other hand, a strategy is often necessary since not all use cases create potential biases. Distinguishing a cat from a dog is universal!
-hand-innv.png)
4. What parts of my training data should I have annotated first?
If you have a large data set, there's no point in annotating everything at once. Manual reviews as well as techniques and products on the market can help you classify your data set, allowing you to send only a balanced subset to the annotation for the first draft: a subset containing a well-distributed sample of your data. In this way, you will get balanced data that will have a greater impact on the performance of your model.
5. How do I choose the right tools for data annotation?
Choosing annotation tools is critical to ensuring high-quality annotations. Numerous platforms and software, such as Labelbox, Encord, V7 Labs or Label Studio, offer advanced features to help you get accurate results. Choose one that specifically meets your needs and offers a tailored user experience for your image and video annotators. videos.
6. How do I write clear instructions for annotators?
When preparing for the annotation process, it is imperative to create extremely precise guidelines for your annotators (or Data Labelers). These guidelines should go beyond simple instructions and clearly explain the criteria and standards to be followed. By integrating visual examples that represent what you expect, you provide your annotators with concrete models to follow, making it easier for them to understand and learn.
Be sure to define specific rules for how annotations are drawn, such as the size, shape, position, and specifications of each annotation. The more detailed and transparent your guidelines are, the more your annotators will be able to produce high-quality and consistent annotations. This will not only optimize the annotation process, but also ensure the reliability of the annotated data, which is essential for training accurate and effective artificial intelligence models.
7. How do you train annotators to get high-quality annotations?
Annotator training is of paramount importance to ensure high quality annotations. Ensuring that your annotators fully understand the overall goals of your project and the specific rules and requirements associated with them is critical. This thorough understanding is required to obtain accurate and consistent results.
If you decide to work with a labeling service provider, it is just as essential to check that the company offers a comprehensive training program for its teams of annotators. Robust training ensures that annotators are familiar with the specifics of your project, annotation guidelines, and quality criteria. It also ensures that annotators have the skills they need to effectively handle the tasks assigned to them.
Ultimately, proper training helps to minimize errors, improve annotation consistency, and optimize the efficiency of the entire annotation process, which is critical to the success of your machine learning project.
8. How do you deal with ambiguous cases in the data?
Establish guidelines for dealing with situations where the objects to be annotated are partially visible or out of focus. Annotators need to be trained to identify and handle these cases properly. It is also recommended to have a register to be fed and illustrated gradually with atypical cases, so that Data Labelers can learn about them.
9. How do you avoid over-annotation?
Avoid annotating empty areas or overlaying the same object with multiple annotations, which can lead to model errors. In case of doubt, it is important to communicate to annotators that it is better to ignore images or frames, than to label in an approximate way, with the risk of introducing errors!
10. What about ethics in the annotation of data and the respect of the rights of image and video annotators?
Respect for ethics is fundamental in data collection and annotation. Opt for a provider that is sensitive to these issues, guaranteeing confidentiality, fair remuneration and mechanisms to resolve the ethical concerns of annotators. This will maintain ethical practices throughout your AI project.
💡 By following these recommendations carefully, vYou will be fully prepared to obtain the highest quality data possible. This meticulous preparation is not only a guarantee of success and a key success factor, but it is also imperative for your artificial intelligence projects to succeed!
