If your AI training dataset is “garbage,” the resulting algorithm will also be sub-par. A false positive in machine learning might produce a poor customer experience for an e-commerce chatbot, but it could be life-threatening for an autonomous vehicle or biomedical algorithm.
A recent study found a potential risk with self-driving cars, as a result of algorithmic bias. When compared to the detection of lighter-skin tones, researchers found their models detection of dark-skin tones was five percentage points less accurate, on average.
Not only does this present immense safety concerns for pedestrians and cyclists, if left unchecked, poor quality data can also perpetuate historical, negative stereotypes, especially across race and gender.
In another example, a scientific paper observed activities such as cooking are 33 percent more likely to involve females than males in a training set. While the models studied were trained to recognize gender as binary only, this is problematic because a model trained on such a dataset may reinforce stereotypical household roles, all while incorrectly identifying a person’s gender, regardless of other features.
Quality matters at every stage of the training data lifecycle, and these findings demonstrate how poor quality data results in technology that poorly impacts the lives of those it’s meant to enhance.
Download our solution brief for insights on how to achieve 99.9% quality for ML algorithms.
In machine learning, training data is the dataset of labeled images, video, audio, and other data sources used to train an algorithm. So what is high-quality training data, and how can you ensure superior data quality for your learning algorithms?
High-quality training data is data that is secure, ethically sourced and free of errors that might compromise the intelligence of your algorithm. In a recent talk, we shared examples of how bias compromises datasets, however, there are a number of errors that could compromise the integrity of your training datasets.
These errors include, but are not limited to, poor or inconsistent data labeling; the labeled data is unrepresentative of the problem or scenario the model is trying to solve; the method of data collection violates privacy or property laws; the labeled data compromises ethical values, etc.
“High-quality training data is data that is secure, ethically sourced and free of errors that might compromise the intelligence of your algorithm.”
Anything that could mislead your learning algorithm is not high-quality data, and given how poor quality data can result in AI bias and poor performance of AI systems, training data is arguably the most important element of machine learning.
Regardless of your industry or use case, an effective training data strategy can help you establish a firm baseline for data quality.
Start by clearly articulating your end training goal. This will help determine what data needs to be collected, as well as the level of data quality required to meet your goal. Dataversity shares 9 dimensions of data quality to keep in mind, in order to reduce the risk and cost associated with poor quality data. This includes: accuracy, completeness, consistency, integrity, reasonability, timeliness, uniqueness/deduplication, validity and accessibility.
After you establish a quality assurance process, automated checks for quality requirements can be put in place to expedite the speed of your data quality analysis process. For example, label inclusion/exclusion such as “label one and two can not coexist”, or label threshold rules like “nothing less than 10px should be annotated”.
Achieving 99 percent quality will require more effort than obtaining 95 percent quality, and it’s important to keep this in mind when building out test data, training data and validation data for your AI algorithms.
Remember, all datasets are contingent on each other and work together to optimize the learning algorithm. A continuous feedback loop that reviews labeling tasks in real-time can help you achieve stability in data quality, in an accelerated time frame.
At Samasource, our customer success team, project managers and data labelers use our secure annotation platform, SamaHub to deliver turnkey annotated data to clients. The platform allows us to manage the entire training data lifecycle from one dedicated platform, making it easy to retrieve a real-time report of quality thresholds and efficiency gains.
Download our checklist on how to ensure quality training data for AI.
According to McKinsey Global Institute, obtaining high-quality datasets is one of the top limitations for AI adoption, however, it can be a challenge to obtain high-quality datasets at an affordable cost and scale.
Many organizations turn to publically available datasets in the early stages of model development, but as your model matures, or if you have a unique use case, specialized training data is needed.
Crowdsourced data labeling is another option for training data, but achieving quality at scale might prove difficult. In fact, when compared to other market offerings like in-house teams or a trusted data annotation partner, an AI study found crowdsourced training data was 15 - 25% less accurate.
High-quality data is imperative to the success of your algorithm, and having a dedicated team can provide the added benefit of industry expertise, a secure technology platform and quality SLAs to deliver consistent, precise results.
Over the last decade, Samasource has submitted over half a billion labeling tasks with tens of billions of labels, to train machine learning models. During this time, we’ve learned that pilot projects are a cost-effective and highly efficient way to validate what’s needed to train AI algorithms at scale.
A trusted annotation partner can also ensure security and confidentiality of your data, while delivering higher data outputs than a single crowdworker, or overstretched in-house team could deliver within a reasonable timeframe.
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For over a decade, hundreds of organizations, including 25% of the Fortune 50 have relied on Samasource to deliver secure, high-quality training data and model validation for machine learning.
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