Big data analytics and machine learning are two of the emerging technologies that are set to disrupt the traditional business processes and functions. A joint survey by MIT Technology Review and Google Cloud states that 60 percent of the respondents have started using machine learning technology in their organizations. Additionally, Deloitte predicts that the market for Artificial Intelligence (AI) and Machine Learning (ML) will see significant growth and reach to $57.6 billion in 2021, which was just $12 billion in 2017.
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The increasing popularity of big data analytics and ML techniques has given rise to the concerns about the data that is fed to the systems. These intelligent technologies are only as good as the data sets that they gather through various direct and indirect channels. There is a significant chance that these systems can grow biases if the data is left unmonitored. For example, researchers, studying machine learning processing found out that female names were associated with more family-based terms, and career-based terms were generally associated with male names. Therefore, companies need to be extra cautious in ensuring that the data which is fed to analytics and machine learning systems are free of any biases.
Enterprises can use specific measures to reduce or eliminate the incidences of biases arising out of big data analytics and machine learning platforms. For example, eliminating all the gender data from the data sets can mitigate gender biases. Frameworks like Reducing Bias Amplification take a verb that has a heavy bias, and constrains the algorithms from creating any further bias that exists in the initial data sets. There are also possibilities of data meddling by humans, which can also result in the creation of additional bias. Many companies are using blockchain technology to decentralize AI algorithms so that one AI machine can learn from another to protect against any biases.