As the industry’s ability to collect digital data increases, a new wave of machine-based learning and deep learning technologies are offering the promise of helping improve systems and outcomes. Yet, as exciting as these new machine learning capabilities are, there are significant considerations that we need to keep in mind when planning, implementing, and deploying machine learning. One of the biggest challenges in any industry considering machine learning is bias. This interesting topic came to us from IEEE Spectrum in their article, “It’s Too Easy to Hide Bias in Deep-Learning Systems.”
The world around us is increasingly choreographed by algorithms designed by humans to target a particular set of criteria of other humans. They decide what advertisements, news and movie recommendations they see. Those algorithms are also involved in more important decisions, like determining who gets loans, jobs or parole.
How soon will those algorithms decide what medical treatment patients receive or how cars will navigate the streets? Transparency allows developers to debug their software, end users to trust it and regulators to make sure it’s safe and fair – and without bias.
Melody K. Smith
Sponsored by Data Harmony, a unit of Access Innovations, the world leader in indexing and making content findable.
