Heartbeat

SEPTEMBER 13, 2023

Source: ResearchGate Explainability refers to the ability to understand and evaluate the decisions and reasoning underlying the predictions from AI models (Castillo, 2021). Explainability techniques aim to reveal the inner workings of AI systems by offering insights into their predictions. What is Explainability?

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Unite.AI

SEPTEMBER 23, 2024

The allure of AI-driven drug discovery lies in its promise of creating novel therapies faster and cheaper, providing a solution to one of the industry's biggest challenges: the high cost and long timelines of bringing new drugs to market.

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NYU Center for Data Science

JUNE 16, 2023

Motivated by applications in healthcare and criminal justice, Umang studies how to create algorithmic decision-making systems endowed with the ability to explain their behavior and adapt to a stakeholder’s expertise to improve human-machine team performance. By Meryl Phair

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DataRobot Blog

MAY 12, 2022

DataRobot is a proud partner enabling governments and organizations to face their unique challenges and leverage AI for value they can trust. Numerous government agencies recognize the opportunity with AI. DataRobot believes trusted, explainable AI can help generate better outcomes than either humans or machines alone.

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ODSC - Open Data Science

MAY 30, 2023

For example, rising interest rates and falling equities already in 2013 and again in 2020 and 2022 led to drawdowns of risk parity schemes. However, changing correlations can be a challenge for this type of portfolio allocation technique. In 2023-Q1, we even saw failing banks like SVB simply because of investments in “safe” treasury bonds.

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ODSC - Open Data Science

OCTOBER 24, 2023

Indeed, the whole technique epitomizes explainable AI. Figure 1: Synthetic data (left) versus real (right), Telecom dataset The main hyperparameter vector specifies the number of quantile intervals to use for each feature (one per feature). It is easy to fine-tune, allowing for auto-tuning.

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DataRobot Blog

MAY 25, 2022

From 2018 to 2020, the U.S. Using either the code-centric DataRobot Core or no-code Graphical User Interface (GUI), both data scientists and non-data scientists such as risk analysts, government experts, or first responders can build, compare, explain, and deploy their own models. The scale and costs of weather disasters in the U.S.

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