AI Trust and COVID-19 Impact

a set of clear circles connected by thread like a network. a hand is holding a marker and the word “Skills” is written on the center clear circle.
a set of clear circles connected by thread like a network. a hand is holding a marker and the word “Skills” is written on the center clear circle.
Image credit: pixabay

In Part I of this blog, I introduced the concept of Skills Scarcity, described a data-driven approach to measure its value, and illustrated two exemplary use cases. In Part 2, I will cover the AI trust that we built around Skills Scarcity, as well as sharing some insights on how scarcity values have been impacted by COVID-19.

AI Trust

AI trust is an integral part of any AI asset development. At the end of the day, without human’s trust on AI prediction, recommendation or insights, such AI output has no values. Consequently, to gain end users’ trust…


What is Skills Scarcity? How to measure it? Where to use it?

a set of clear circles connected by thread like a network. a hand is holding a marker and the word “Skills” is written on the center clear circle.
a set of clear circles connected by thread like a network. a hand is holding a marker and the word “Skills” is written on the center clear circle.
Image credit: pixabay

Skills are the new currency in the changing world of work. Studies [1] show that the half-life of skills is five years, meaning that what we learned today will become obsolete or be forgotten in just five years. According to a recent survey by Mercer Global Talent Trends [2], more than half of organizations which responded to the survey, are targeting at upskilling and reskilling their critical talent pools to drive workforce transformation. On the other hand, based on a survey of 10K people across UK, Germany, China…


Notes from Industry

7 stages from Problem Formulation to Solution Sunset

Adding machine intelligence into our business workflows has become norm now, and there are increasingly more data-drive predictive analytics being developed and integrated into existing business operations to assist decision making, improve efficiency, reduce risks and enhance employee experience.

Nevertheless, with the proliferation of analytics and AI models produced, we are facing the challenge on efficiently managing the analytics lifecycle to ensure that the models yield solid business insights leading to optimal decisions, identified opportunities and righteous actions. It is a multifaceted and complex task.

Below is my view of managing an analytics’ entire lifecycle, which provides a step-by-step guidance…

Ying Li

Ying Li manages the Data Science team at IBM CHQ HR, leading projects that develop advanced people analytics to support leaders in key decision-making

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