Teams working on artificial intelligence (AI) continue to face major challenges in data collection. There are many reasons why data collection is a problem for teams building artificial intelligence (AI). No matter what the reason, accurate and scalable data solutions are becoming more important.
The Best Methods to Collect High-Quality Data
To be an AI practitioner, you must ask the right questions to develop a plan for data collection.
What type of data do you need?
What data do you require depends on the problem you are solving. Speech data from speakers representing the entire range of customers is required for a speech recognition model. This includes speech data that covers all languages, accents, and characteristics of your target customer.
Where can I get data?
First, find out what data you have at your disposal and whether that data is relevant to the problem you are trying solve. There are many public online data sources that can provide additional data if you require it. Crowdsourcing can also be a viable option. You can also create synthetic data to fill gaps in your existing dataset.
Another important thing to remember is that your model must be able to access reliable data for a long time after it goes into production. You need continuous data to retrain your employees after the launch.
How much data do I need?
It will all depend on what problem you are trying to solve and your budget. But the general rule is to collect as much data as possible. When it comes to machine learning models, there is no need for too much data. It is important to ensure that your data covers all possible use cases for your model, even edge cases.
Solutions & Advanced Research Group
It is better to be inclusive than biased
GTS has seen a significant shift in how customers interact with us over the past 18 months. AI is becoming more ubiquitous and has revealed many gaps in its construction. AI bias can be reduced by using training data. We have advised clients that a representative crowd of people to collect data will create a faster, more economic AI. We advise our customers to focus on inclusivity in their sample design, as nearly all of the training data is derived from data collected by individuals. Although this requires more effort and experimental design, the ROI is much higher than a simple sample design. This is simply because you can get more accurate and diverse ML/AI model with more specific demographics. In the long-term, it's far more cost-effective than trying to fill in gaps by eliminating bias from your production AI/ML models.
A well-designed data collection will be the sum of its parts. A solid foundation is a comprehensive sample frame. But what drives throughput, data quality, and throughput is a user-centric approach for all aspects of the engagement process, including invitation, qualification, onboarding (including Trust & Safety), and the experiment experience. Teams often forget that there are people who will complete these projects. This will result in poor project adoption and data due to lower-than-average written tests and UX.
Ask yourself if you are willing to put in the effort when designing your experiment or user flow. You must also ensure that you test your experiment from beginning to end. There are always improvements that can be made if you get frustrated or stuck.
Interlocking quotas: From six to sixty-thousand
Take the US Census and create an experiment around six data points: gender, age, state, ethnicity, and mobile ownership. You will have more than 60,000 quotas.
This is due to the interlocking quotas. An interlocking quota is where the number of interviews/participants required in the experiment is in cells requiring more than one characteristic. The above US census example will show that there will be one cell that has n-numbers of users who have the following characteristics: males, 55+, Wyoming, African Americans, and owns an Android smartphone. This is a low-incidence, extreme example. However, by creating your interlocking matrix, before you price, write the experiment, or go in-field you can test to see if there are any unusual or unintuitive combinations of characteristics that could impact the success of your project.
More incentives are important than ever
Last but not least, you need to examine the incentive you're offering to users to participate in the experiment. While it is common to make commercial trade-offs when designing AI Data Collection
experiments for research, you should not compromise the incentive that the user receives. They are the key to delivering timely and high-quality data. You will pay less to users to get the data, but you'll have lower quality, uptake and ultimately, more to pay.
You might consider global purchasing power parity (PPP) if you have a tight budget. Can your dollar travel further in different parts of the world than it does in one region? You can reduce your quota requirements by grouping 24-40-year-olds into one group. These are just some of the many techniques you can use to maximize your project's commercial value.
The future of AI will be determined by how we make decisions about responsible protocols and policies now. Data is a key component of these efforts. It's at the heart of all AI technologies directly influencing model performance. Data is the key area in which AI practitioners can make a real difference when it comes to establishing governance practices.
It all comes down to the data
Data scientists will spend most of their time collecting and annotating data when working on an AI project. These tasks are crucial in protecting data privacy, mitigating data bias, and ethically sourcing the data.
Data privacy and security should be a top concern for AI practitioners. This area is covered by legislation, and your data handling protocols must be consistent with it. For example, ISO standards (which are internationally-recognized) exist around protecting personal information, the GDPR (General Data Protection Regulation) covers data management in the EU, and other requirements are present worldwide. It is important that your business adheres to the standards applicable to each of its customers.
Data protection laws may not be in place or inconsistently applied to certain areas. However, responsible AI is about implementing data security management practices to protect data suppliers. Before using any personal data, you should obtain consent from the individual and take security measures to prevent it from being misused.
You may want to consult a third-party provider if you are unsure about the security protocols that you should include in your data management processes. They will have the experience and knowledge to help you.
Biased data equals biased outcomes. This is a fact of AI development. However, it becomes more complicated when you consider all the possible ways bias could be introduced into AI models. Let's say you are building a speech recognition system for use in a car. Speech can be characterized by different tones, accents and grammar. If you want your speech recognition system to be able to recognize drivers from different backgrounds and demographics, then you will need data that includes each use case.
Your speech recognition model will struggle to recognize female voices if you have data that is predominantly male. This is because the model has not been exposed enough to this type of data during training. It is important to create a dataset that covers all possible use cases. To create an AI product that works for all users, it is important to ensure that all users are included in the training data.
This is ethical data sourcing. It refers to the treatment of those who prepare and provide the data. If you give data, it is a good idea to be compensated (and aware that you are providing it). You can get compensation in the form money or services.
It is a fact that data is often harvested without our knowledge and sometimes, it is unclear who actually owns the data. Who would be the owner of voice data if you were on a call with your company? Your company? The video calling provider? The individual speakers? Boundaries can get confusing quickly. Companies that are committed to responsible AI must be open about who they're collecting data and what data it is. They should also make every effort to compensate individuals for any data they have collected.