Like machine learning engineers, machine learning scientists are in high demand in today's job market. That's because organizations are eager to adopt machine learning-powered tools to enhance the value of their data and analytics and add automation to processes. Amy Steier, principal machine learning scientist at the developer tools provider, Gretel.ai. Demand for machine learning technologies is on the rise, according to market research. Potential applications include customer segmentation and investment prediction in the financial services sector; image analytics, drug discovery and personalized treatment in healthcare; and inventory planning and cross-channel marketing in retail.

When you harness the power and potential of machine learning, there are also some drastic downsides that you've got to manage. Deploying machine learning, you face the risk that it be discriminatory, biased, inequitable, exploitative, or opaque. In this article, I cover six ways that machine learning threatens social justice and reach an incisive conclusion: The remedy is to take on machine learning standardization as a form of social activism.When you harness the power and potential of machine learning, there are also some drastic downsides that you've got to manage. Deploying machine learning, you face the risk that it be discriminatory, biased, inequitable, exploitative, or opaque. In this article, I cover six ways that machine learning threatens social justice and reach an incisive conclusion: The remedy is to take on machine learning standardization as a form of social activism.When you use machine learning, you aren't just optimizing models and streamlining business.

However, I would also posit that all intelligent systems, including humans, are biased, for our own cognition is predicated on our personal experience and knowledge (aka "Training Data" in parlance). With the hyperbole surrounding today, bias is being cast as an evil crippling flaw unique to that will limit its value and widespread adoption. As Jonathan Vanian notes in an article for Fortune, is only as good as the data that humans provide. Vanian goes on to write that, as practitioners, we know: "the data used to train deep- systems isn't neutral. It can easily reflect biases, conscious and unconscious, of the people who assemble it. Data can be slanted by history, with trends and patterns that reflect centuries-old discrimination."