Precision has long been a human ambition and pursued in all endeavors. Precision in AI means things like accurately recognizing objections and making good decisions. Precision in data annotation starts with having a good data set.
Data annotation is the process of improving a data set by adding metadata. That metadata adds context, concepts, and the ability to recognize objects by labeling them. Data annotation is also used to tag or label many other detailed characteristics and properties.
A poor-quality data set can lead to costly problems in AI and algorithms.
For example, Zillow overestimated the accuracy of their AI and the quality of their data. A mistake that caused them to overpay for real estate and lose an awful lot of money, around half a billion dollars and more in revenue. 2,000 people also lost their jobs, and the company had to make a lot of painful cuts.
Inaccuracies in AI can also have a social cost. For example, a more human and familiar racial bias crept into COMPAS, an algorithmic system used by law enforcement to judge the likelihood of recidivism in the USA. That is to say, how likely a person up for parole might violate their parole or get caught breaking the law again.
COMPAS somehow thought that black people were at higher risk when they were at low risk. It also thought the highest risk white people were actually very low risk. That led to much worse outcomes for everyone in American society, especially black people. It didn't only cost a company money. Inaccurate AI has cost human suffering and more crime.
So accuracy in AI is really important to get right. We need to be as certain as possible that the AI is honestly fair and making the right decisions. To do that, just like humans need factual information to make well-informed decisions, AI needs a good, inclusive data set for machine learning.
A good data set is inclusive and has factual, accurate information. The better the data that goes into the algorithms is, the better the output. That makes for more accurate AI.
A good data set is a very large data set with relevant and accurate information. The signal makes up most of the data. A certain amount of erroneous data or wrong information or outliers and noise will always be there in the data. You want as much signal and as little noise as possible.
A large, inclusive data set is essential for avoiding unknown bias in AI. Biases decrease the accuracy of any AI. So, you need a lot of high-quality data. One way to improve the quality and quantity of your data is to use the best data annotation services with the best image labeling tools.
Bad data annotation is more than costly to one company because models must be iterated on, machine learning algorithms retrained, and many complicated steps repeated. It is also costly to society. Bad data annotation causes the data to need to be reviewed. It makes it so that models need to be redesigned and iterated.
More than that, it can cost the innocent their freedom. It can let loose those who were better locked up or watched. It can cost other companies money or get people hurt or misdiagnosed. Finally, it can worsen people's lives when they are incorrectly denied credit.
These problems are avoidable and able to be solved. The answer is to use the best data set possible, work with the best data annotation companies, and use the best data labeling tools. There are also ways to test your data, test your data annotation and test your model. There are also some best practices to follow. Here are some helpful tips:
Tips On Improving the Precision of Your Data Annotation
- Make sure you provide clear instructions and what is most required is understood.
- Include an additional review team and review cycle to check the data annotation work.
- Use a consensus process. If most agree on something, then they are probably correct.
- Screen for the accuracy of your data annotators.
- Use evaluation tasks where all the answers are known to check for accuracy.
- Use the best data labeling tool you can.
- Outsource to an expert data annotation service with the skills, technology, and experience you need.
So, let us say you are worried and want to test the accuracy of your model and the data you used to train it. The first step will be to test the model's performance. Does it give the expected results when you know what that result should be with certainty?
Regardless of how well your model performed, it is a good idea to randomly pull subsets of data from the data set you used to train your model.
Then audit that data in detail. Is there any metadata missing? How accurate are the various tags and labels? Are any bounding boxes, cuboids, skeletons, polygons, and so on where they should be? This kind of detailed auditing is important in determining the quality of your data.
It is vital that the sample be as random and as representative as possible for this to be accurate.
Having the most useful data with lots of signal and very little noise makes a big difference in how well your data training works. In addition, labeling tools for AI and precise data annotation increase the quality and accuracy of your data set.
Precision is definitely needed to train helpful artificial intelligence. It is crucial to avoid cementing bias into an algorithm. The costs to your company in dollars can be pretty high, and the costs to human life measured in suffering can be even higher. Outsourcing your data collection and annotation to the best data annotation company with the best labeling tools is cost-effective by comparison.