Artificial intelligence (AI) is playing an increasingly essential role in a wide range of sectors, from 🔗 healthcare to 🔗 finance to 🔗 real estate. However, AI, in most of its business applications, is extremely data-dependent, and obtaining high-quality data is often a major challenge for teams of Data Scientists and developers. The latter rarely have expertise in managing large data pipelines requiring manual qualification, at a granular level. In this article, we explore ten questions these teams frequently ask themselves about how to obtain data for AI projects, and how to approach them strategically and ethically.
1. Where do I start with my data?
Over the past decade, companies across all sectors have amassed huge amounts of data. Yet it can be difficult to know where to start when it comes to using them for AI. The key is to go back to business objectives. Identify these goals and then work out 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. In this case, it's a matter of working together 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?
A common mistake is to assume that training data will be identical to 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 actually look like in production. There are always atypical cases... (for example, 🔗 think of the Tesla's on-board computer, unable to recognize an unusual vehicle, namely, a cart!).
3. How can I avoid bias in my data?
Data bias is a major problem for AI. They can take many forms, from societal or racist biases, to unrepresentative data sets. The only way to combat bias is to be proactive. This means keeping abreast of the latest research in AI ethics and establishing responsible processes to reduce bias, drawing on recommendations such as those from Google AI and the IBM Fairness 360 framework.
One response of Data Scientists' teams to this problem is to source annotators from the four corners of the globe (by outsourcing to India, the Philippines, Madagascar, Spain, etc.) or to resort to crowdsourcing. While practical, this response is rarely sufficient, since it's almost impossible to assemble a team as diverse as the human species! On the other hand, a strategy is often needed, since not all use cases create potential biases. Distinguishing a cat from a dog is universal!
-hand-innv.png)
4. Which parts of my training data should I annotate first?
If you have a large data set, there's no point in annotating it all at once. Manual reviews, as well as techniques and products on the market, can help you classify your dataset, allowing you to send only a balanced subset to annotation for a first draft: a subset containing a well-distributed sample of your data. In this way, you'll obtain balanced data that will have a greater impact on your model's performance.
5. How to choose the right tools for data annotation?
The choice of annotation tools is essential to guarantee high-quality annotations. Many platforms and software, such as 🔗 Labelbox, 🔗 Encord, 🔗 V7 Labs or 🔗 Label Studiooffer advanced features to help you achieve precise results. Choose one that specifically meets your needs and offers a tailored user experience for your image and 🔗 videos.
6. How to write clear instructions for annotators?
When preparing the annotation process, it's imperative to create extremely precise guidelines for your annotators (or Data Labelers). These guidelines must go beyond simple instructions and clearly explain the criteria and standards to be followed. By including visual examples of 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 to be drawn, specifying, for example, the size, shape, position and specifications of each annotation. The more detailed and transparent your guidelines, the more likely your annotators will be able to produce high-quality, 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 efficient artificial intelligence models.
7. How can annotators be trained to produce high-quality annotations?
Annotator training is of paramount importance in ensuring high-quality annotations. It's essential to ensure that your annotators fully understand the overall objectives of your project, as well as the specific rules and requirements associated with them. This in-depth understanding is essential for accurate, consistent results.
If you decide to work with a labeling service provider, it's equally 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 necessary skills to effectively handle the tasks assigned to them.
Ultimately, proper training helps minimize errors, improve annotation consistency and optimize the efficiency of the entire annotation process, which is essential for the success of your machine learning project.
8. How to deal with ambiguous cases in the data?
Establish guidelines for dealing with situations where the objects to be annotated are partially visible or blurred. Annotators need to be trained to identify and handle these cases correctly. It's also a good idea to have a register of atypical cases, to be fed in and illustrated as they occur, so that the Data Labelers can take note of them.
9. How to avoid over-annotation?
Avoid annotating empty areas or covering the same object with multiple annotations, which can lead to model errors. In case of doubt, it's important to tell annotators that it's better to ignore images or frames, than to label in an approximate way, with the risk of introducing errors!
10. What about ethics in data annotation and respect for the rights of image and video annotators?
Ethical behavior is fundamental to data collection and annotation. Opt for a provider that is sensitive to these issues, guaranteeing confidentiality, fair compensation and mechanisms to resolve annotators' ethical concerns. This will maintain ethical practices throughout your AI project.
By carefully following these recommendations,you'll 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, it's also imperative if your artificial intelligence projects are to succeed!
