The speedy growth of artificial intelligence (AI) has exploded its impact on both the business and social worlds: from Natural Language Processing (NLP) to automotive and facial recognition and beyond. This expansion leads to a high need for diverse data, and the fact that the industry is lacking it is becoming a serious problem.
AI models are always hungry for data. Emergent types of AI include life-long learning machines which are being built to obtain data constantly and permanently. A stable source of data helps better guarantee satisfactory results.
Nevertheless, while data is becoming increasingly important and machine learning (ML) is commonly used to make predictions for businesses, the struggle with data bias is also turning into a growing concern. Biased AI models, especially when implemented at scale, can bring social risks.
Data is the starting point of ML. From numbers and text to photos – numerous types of data are collected from various sources and then used as training data, the information which the ML model will learn from. The more diverse the training data is, the better the model will perform.
Even though ML models can contribute to the success of an AI company using data assets to leverage products and results, it is important to keep in mind that they will always just be as good as the data they are trained on. This means that if the data is not diverse enough, or is not well-processed, the algorithms can face a problem called overfitting – the model learns the noise and disturbing details in the ‘low-quality’ data which results in negative performance of the model.
To put it another way, when the data does not meet diversity and quality, the ML models will perform good results on the data they have been given in training, but poor results on new data. As the importance of data diversity and quality is increasing especially in the field of data science and cybersecurity, it is critical to avoid even minor errors.
Besides vast amounts, data also needs to have good ‘qualities’: accurate, clean, balanced, and representative. Bias happens when the training data does not represent fully the diversity of a general population. A dataset can be bias toward, for instance, a certain gender, race, or class, and has a very high chance of creating an inaccurate model as a consequence.
Algorithms nowadays play an important role as they are being increasingly utilized to lead critical decisions that have impact on substantial groups of people. Therefore, it is crucial to set up specific safeguards to detect and fix errors of bias in AI and ML. Often occurring as an accident, it is hard to notice bias right away. Indeed, some can even go unnoticed for a long time. Specialists will have to carefully assess prediction results to detect bias from a model.
Data scientists have to be cautious in data collection and annotation stages to ensure that training data is diverse, balanced, and ideally covers corner cases. It is essential to accomplish balanced and representative training data from a global subject pool if the model is eventually applied to a global data pool, especially in cases that concern human populations such as facial recognition or sentiment analysis.
For instance, a facial recognition algorithm that only learnt facial features of the Western population will likely struggle with recognizing African or Asian populations, giving inaccuracy, or will misclassify; or an internal recruiting application that had not been given diverse data will likely be partial to one gender or race. Well-known examples include when Amazon’s internal automated recruiting system discriminated against female candidates, Google Photos’ facial recognition identified African-Americans as gorillas, and Facebook AI put a ‘Primates’ label on video of black men.
In short, data has a substantial influence on the success of technology. Delay and carelessness in tackling data bias will consequently lead to low-quality AI models and negative results.
It is a moral responsibility for AI companies to address and tackle data bias, not just for customers but also for their own good. For this, data diversity is a solution.
Read more: What data is out there and when do you need to create your own?
The most practical approach to address and solve data bias is to actively tackle it at the data collection and curation phase. Algorithms have the ability to circulate or even magnify biases in their data sources. Diverse data should be used here at this point to diminish bias.
To ensure that the data is adequately diverse and proportionally accounts for all variables, there needs to be concrete procedures in place. Companies that operate internationally should establish a system that includes a step of analyzing data from all its functions to generate a new procedure before adopting AI. Especially, businesses that specialize in NLP or computer vision should pay attention to this process since it will simultaneously enhance product quality and market access.
Data collection and curation should ideally be executed by a diverse team: diverse age, gender, race, ethnicity, backgrounds, experience, and points of view. Such a diverse team together can contribute to better consideration and prediction of different business use cases and scenarios.
Last but not least, a type of diversity that is not popularly mentioned but is still essential and relevant: intellectual diversity. From education backgrounds, political views to teamwork style – our unique individual characteristics not only help to boost a team’s creativity and efficiency, but also to enhance the ability of identifying and correcting bias.
Read more: Collecting data for NLP models: what you need to be aware of
It has always been a substantial challenge to evade bias entirely but with well-planned awareness and strategy, minimizing its existence is possible. After detecting a bias, data scientists have an important task to balance it. Common methods include relabeling samples, adjusting sampling distribution, modifying confidence weighting, and executing new mitigation strategies.
It is important to be aware that dealing with data bias is a much more challenging task than detecting and collecting it. This task should be human-in-the-loop as it cannot be simply automated. Here, data scientists or engineers need to examine distributions thoroughly, in order to identify unusual activities. Human force involves considering factors that cause odd correlations, such as gender, race, and age, no matter whether these variables are inputs to the model.
StageZero have got you covered here, with 100% human-verified data for a range of specific use cases, we ensure that bias is maintained at a minimum to support your model’s accuracy. To learn more about how we can help you, contact us. Keep up with our most recent developments by following us on LinkedIn.
