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In AI we trust? Why we Need to Talk About Ethics and Governance (part 2 of 2)

Cloudera

In part 1 of this blog post, we discussed the need to be mindful of data bias and the resulting consequences when certain parameters are skewed. In 2019, the Gradient institute published a white paper outlining the practical challenges for Ethical AI. Systems should be designed with bias, causality and uncertainty in mind.

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Climate change predictions: Anticipating and adapting to a warming world

IBM Big Data Hub

These proactive measures are made possible by evolving technologies designed to help people adapt to the effects of climate change today. 5 The Global Disaster Preparedness Center recommends policymakers and others adopt a range of measures to help their regions adapt to higher heat. Red Cross Red Crescent Climate Centre, 2019.

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How Digital Native & Cloud Native Will Influence FP&A

Jedox

In this blog post, we’ll dig deeper into digital native, cloud native, and how it will influence the work of FP&A professionals. Of the most common jargon used to talk about the changes in technology and business, it is unlikely it would be the first thing that comes to mind. Digital Transformation , anyone?)

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Covid Data: An anomalous blip, or the new normal?

Cloudera

That compares to only 36 percent of customer interactions as of December 2019, which was before the pandemic impacted business, and only 20 percent in May 2018. Insurance and finance are two industries that rely on measuring risk with historical data models. appeared first on Cloudera Blog. Data Variety.

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How to create a culture of innovation

CIO Business Intelligence

It requires bold bets and a willingness to persevere despite setbacks, criticism, and uncertainty,’’ wrote McKinsey senior partners Laura Furstenthal and Erik Roth in a recent blog post. “By Innovation is a double-edged sword: It is critical to growth — but that’s also what makes it risky. “It

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Responses to Negative Data: Four Senior Leadership Archetypes.

Occam's Razor

During 2019, our Net Promotion Score has dropped 15 points. A decade ago, data people delivered a lot less bad news because so little could be measured with any degree of confidence. In 2019, we can measure the crap out of so much. The conversion rate is down 30% at launch. Why be hurtin’? It is a lot of stuff!

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Towards optimal experimentation in online systems

The Unofficial Google Data Science Blog

the weight given to Likes in our video recommendation algorithm) while $Y$ is a vector of outcome measures such as different metrics of user experience (e.g., This blog post discusses such a comprehensive approach that is used at Youtube. Crucially, it takes into account the uncertainty inherent in our experiments.