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Introduction to Responsible AI: The CallMiner Research Lab Responsible AI Framework

Callminer

The CallMiner Research Lab Responsible AI Framework outlines definitions and concerns, as well as some of the driving questions, leading to deeper considerations about our tools, models, and datasets.

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Welcome to the CallMiner Research Lab blog: The cutting edge of innovation

Callminer

Welcome to the CallMiner Research Lab blog! We are excited to share what we are working on and what we are thinking about.

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Technical basics series: The singular value decomposition (SVD) 101

Callminer

The CallMiner Research Lab team explores several SVD applications. Singular value decomposition (SVD) is important to data science, as it provides a ranking of features stored by a matrix.

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Technical basics series: Bayesian inference 101

Callminer

The CallMiner Research Lab breaks it down in this new blog. All statisticians use Bayes’s theorem, even frequentists. What makes you a Bayesian is in how you interpret it.

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Technical basics series: From Python to Haskell

Callminer

Instead of laying out the language systematically, the CallMiner Research Lab starts with the parts most similar to Python and expands outwards. Haskell is a big functional language.

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Introduction to Responsible AI: Unpacking the harms

Callminer

The latest in our Responsible AI blog series, the CallMiner Research Lab explores two of the main categories of harms that AI outputs can cause: Harms of Allocation and Harms of Representation.

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Introduction to Responsible AI: Unpacking Bias

Callminer

The CallMiner Research Lab unpacks some of the many terms that define the space. Part of what makes Responsible AI difficult is the vast set of ideas, theories, and practices that it interacts with.

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