Wendy Gonzalez is President and Interim CEO of Samasource, the leading high-quality AI training data company used by 25% of the Fortune 50.
From self-driving cars to chatbots that help us shop online, AI is becoming increasingly essential to the technologies we utilize daily. However, whether your focus is on natural language processing (NLP) or autonomous driving, there’s one thing that remains equally important: training data must be of the utmost quality, secure, diverse, ethically sourced and free of errors that might compromise the intelligence of your algorithm. Bad data could be as minor as getting an irrelevant ad, but it could also be life-threatening for an autonomous vehicle or a facial recognition system. Most importantly, training data should avoid bias.
According to IDC, AI spend is projected to hit $97.9 billion by 2023. Yet, according to Dimensional Research study, 8 out of 10 projects fail, and 96% of them run into problems with data quality and labeling. Today’s new products and features can’t be realized effectively without high-quality data.
Quality In, Quality Out
The lack of a high-quality, comprehensive dataset can cripple your ability to deliver an effective product and even shrink your addressable market. Models trained on biased data can produce inaccurate algorithms and ethical, legal and safety issues. Sometimes, they can create negative stereotypes across race and gender, which further perpetuates institutional racism and sexism.
A study by the Georgia Institute of Technology found that pedestrians with darker skin tones are more likely to get hit by a self-driving car than those with lighter skin. In Florida, a law enforcement program used to predict the likelihood of crime was found to “falsely flag black defendants as future criminals at almost twice the rate as white defendants.” And in January of this year, a Black Michigan resident was wrongfully arrested based on a flawed match from a facial recognition algorithm.
If a technology that’s meant to enhance someone’s everyday life is using an algorithm trained on biased data, it can do more harm than good.
Successful training data platforms utilize a combination of a sophisticated classification engine and a dedicated and well-trained human workforce to show the frequency of classes and dataset bias. This helps teams accurately assess the distribution of various classes in datasets to evaluate if they have enough and the correct data needed to ensure their AI functions as intended.
How To Avoid Bias
It’s important to note that models don’t choose to have bias. They learn from the data that they are exposed to in the training process. There are three primary types of bias to look out for in typical AI solutions: Dataset bias (inconsistently structured or annotated data), training bias (poor-quality labeling) and algorithmic bias (algorithms trained on biased data or with poor training rules that make inaccurate predictions).
To combat these challenges, a combination of automation and human-in-the-loop reinforcement is necessary. Through the proper use of technology and human oversight with a diverse workforce, we can achieve high-quality and truly unbiased AI solutions, such as those that offer the ability to detect the full spectrum of skin tones to avoid racial bias or the comprehensive detection of objects and traffic scenes to enable safe self-driving vehicles.
By starting with high-quality, unbiased data, you ensure that your emerging AI solutions will be up to the challenge, whether in e-commerce, autonomous transportation, manufacturing, navigation, retail, AR/VR or biotech, among many other promising AI and machine learning applications.