The world is connected today in more ways than it ever has been before, as billions of objects are now capable of connecting to the internet or interfacing with devices that are already online. The new “Internet of Everything” generates a deluge of data, which is increasingly directed to the cloud for processing and storage. Meanwhile, Artificial intelligence is increasingly utilized to analyze and derive value from these enormous stores of data. In industries such as healthcare, transportation, industrial manufacturing, and financial services, AI algorithms are now being applied to increasingly difficult tasks, including critical decision-making processes.
What differentiates human from machine is the quality of judgement, creativity, and critical thinking. Humans still have the edge, but intelligent machines are slowing progressing in their ability to replicate the human decision-making process. Deep learning algorithms utilize artificial neural networks inspired by the human brain, performing a task repeatedly with small variations to find an optimal outcome.
The key to success in Machine Learning and ultimately Artificial Intelligence is data. Copious amounts of data along with rapidly advancing computing power allow machines to solve increasingly complex problems. Data not only needs to be plentiful but it also needs to be clean, representative, and balanced. If training data is not wholly representative of the diversity of a general population, then the results will undoubtedly be subject to bias. Such biases, whether intended or unintended, can manifest in subtle ways or via colossal and public failures such as the recent examples of age, gender and racial bias found in the ML offerings of some of the world’s largest software companies.
The issue of bias is well documented in sociology, psychology, and other disciplines. Our society has implemented many different safeguards to ensure that bias, and its more offensive derivatives prejudice and discrimination, are kept in check across situations as varied as employment, creditworthiness, education, and social club membership. Because algorithms are increasingly being used to guide important decisions that affect large groups of people, it is critical that similar safeguards are enacted to identify and correct issues of bias in machine learning and AI. This bias is often unintended and can also go unnoticed for a long time, so it is important to carefully evaluate the prediction results from a model to look specifically for instances of bias.
Machine learning models are entirely reliant on the underlying data that they were trained on. If this training data is biased, limited, unbalanced, or flawed in some fashion then the model will inevitably end up producing biased outputs. Data Scientists must exercise care and caution in their data collection and data labeling phases. Data should be balanced and diverse and ideally cover corner cases. If related to populations of humans in some way, such as in face recognition or sentiment analysis, it is important to achieve balanced and representative training data from a global pool of subjects if the model will potentially be applied to a global pool of actual data.
AI provides a comprehensive solution for your data collection and annotation needs. We often assist clients seeking to improve diversity in training data by offering a spectrum of regions from which data can be collected. We utilize our global network of partners and affiliates to collect samples from Asia, Africa, Europe, and the Middle East. Meanwhile our proprietary annotation platform ensures highly accurate and cost-efficient data labeling in the cloud or on premises. With a focus on accuracy and effectiveness, AI is committed to providing world-class annotation solutions across industry sectors.
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