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Important Steps to Take to Address the Bias in AI

Smart Data Collective

In this case, to ensure that the algorithm is fair, developers need to conduct a simple test, letting the AI system evaluate two candidates with identical skillset and experience, with the only difference being gender or any other non-essential variable. Let’s get back to our example of a company that hires a frontend developer using AI.

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Having a Definition of Ready: Harmful or Helpful?

Leading Agile

Mike Cohn’s August 2016 article, The Dangers of a Definition of Ready describes certain problems that can occur when a team uses the concept of a Definition of Ready. He writes: You can think of a Definition of Ready as a big, burly bouncer standing at the door of the iteration. He’s right. Maybe in theory it could.

Agile 105
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Do you need to excel in data analytics before moving into a BA role?

Analysts Corner

Being a person who gets pumped up with every new idea, I started making a list of skillsets required to become a data analyst. Programming languages Languages such as SQL and Python are definitely not mandatory to be a business analyst. No extra knowledge is harmful.

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AWS Re:Invent 2020 – Virtual Cloud Conference!

Whizlabs

Community events are definitely one of the notable things everyone looks forward to, especially in the cloud community. However, canceling an event that had more than 60,000 people in attendance from all over the world last year is definitely not on the cards for 2020. Keynote Speaker Sessions. Register now Free for the AWS Re:Invent.

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The Agile Transformation Coach: Understanding the Roles and Responsibilities

Leading Agile

If that’s your definition of an Agile Transformation Coach, you’d be right—but only partially. To do this, you need a team of coaches with varied skillsets and domain expertise. . Setting Up Team Spaces. Storyboarding. Sprint Planning. Release Planning. Backlog Creation & Refinement.

Agile 118
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Escaped Defects

Leading Agile

Teams that have established Definitions of Done, (DoD) including code quality standards and testing standards and automation throughout enjoy a different existence than the vast majority of teams that I have encountered. Hemming and hawing about when a defect should be logged only muddies the waters. Did you find the typo? Happy Journeying.

Agile 17
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AI in Analytics: The NLQ Use Case

Sisense

In this mode, the user avoids putting too much effort into the definition of a specific search, and instead, relies on a random exploration path with the assisted exploration of NLQ. The Challenges of Getting NLQ Up and Running. For new vendors in the analytics market, one of the most obvious challenges is the absence of historical data.