explaining-bias-in-your-data 
Dataiku
JUNE 4, 2020
Over the last five years, unfairness in machine learning has gone from almost unknown to hitting the headlines frequently, and new cases of unwanted bias introduced in automated processes are frequently discovered. However, there is still no “one-size-fits-all” standard machine learning tool to prevent and assess such bias.
CIO Business Intelligence
DECEMBER 11, 2023
As AI pilots move toward production, discussions about the need for ethical AI are growing, along with terms like “fairness,” “privacy,” “transparency,” “accountability,” and the big one —”bias.” What’s the plan if customers are presented with false data, or if critical decisions are based on inaccurate AI responses?
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O'Reilly on Data
FEBRUARY 19, 2021
The data that powers ML applications is as important as code, making version control difficult; outputs are probabilistic rather than deterministic, making testing difficult; training a model is processor intensive and time consuming, making rapid build/deploy cycles difficult. A Wave of Cloud-Native, Distributed Data Frameworks.
IBM Big Data Hub
JANUARY 25, 2024
Yet many AI creators are currently facing backlash for the biases, inaccuracies and problematic data practices being exposed in their models. In mathematical language: the wider your variance, the more standard your mean. In mathematical language: the wider your variance, the more standard your mean.
CIO Business Intelligence
JANUARY 13, 2023
And the murder of George Floyd — and the social unrest that followed — made it clear that taking a stand around social justice is necessary to recruitment, retention, and even the viability of your brand. Did they get promoted and build diversity in your management team? Much of the disappointment is not with recruitment, however.
Smart Data Collective
MAY 29, 2023
The world of artificial intelligence (AI) is constantly changing, and we must be vigilant about the issue of bias in AI. Understanding Bias in AI Translation Bias in AI translation refers to the distortion or favoritism present in the output results of machine translation systems.
Rocket-Powered Data Science
FEBRUARY 15, 2023
First, don’t do something just because everyone else is doing it – there needs to be a valid business reason for your organization to be doing it, at the very least because you will need to explain it objectively to your stakeholders (employees, investors, clients).
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