CIO Business Intelligence

MAY 17, 2022

Even if the AI apocalypse doesn’t come to pass, shortchanging AI ethics poses big risks to society — and to the enterprises that deploy those AI systems. The following real-world implementation issues highlight prominent risks every IT leader must account for in putting together their company’s AI deployment strategy.

Risk 142

Risk Consulting Machine Learning Data Science 142

Cloudera

NOVEMBER 9, 2018

While such a city might sound like a utopian dream, it could potentially turn into a dystopian nightmare if we overlook the risks brought about by the rise of smart cities. Reflect back on the recent SingHealth breach in Singapore, in which non-medical personal data of 1.5 billion in 2014. IoT opens doors to threats.

IoT 56

IoT Machine Learning Internet of Things Data Collection 56

Cloudera

AUGUST 25, 2021

In the private sector, data is viewed as a way to gain a competitive edge, so companies must grapple with the question of the public good outweighing their own interests. When sharing data, organizations have to wrestle with questions such as, “How will data be used once fed into artificial intelligence (AI) and machine learning (ML) engines?”

Data-driven 88

Data-driven Machine Learning Data Collection Risk 88

Domino Data Lab

AUGUST 1, 2021

The interest in interpretation of machine learning has been rapidly accelerating in the last decade. This can be attributed to the popularity that machine learning algorithms, and more specifically deep learning, has been gaining in various domains. Interpretable Machine Learning , Christoph Molnar, Section 5.1,

Modeling 139

Modeling Deep Learning Machine Learning Knowledge Discovery 139

IBM Big Data Hub

AUGUST 7, 2023

In an ideal scenario, they would be able to, with relative and consistent accuracy, predict performance of clinical trial sites that are at risk of not meeting their recruitment expectations. 2014 Bentley C, Cressman S, van der Hoek K, Arts K, Dancey J, Peacock S. Department of Health and Human Services.

Optimization 57

Optimization Forecasting Data-driven Strategy 57

Domino Data Lab

FEBRUARY 4, 2019

We’ll unpack curiosity as a core attribute of effective data science, look at how that informs process for data science (in contrast to Agile, etc.), and dig into details about where science meets rhetoric in data science. That body of work has much to offer the practice of leading data science teams.

Data Science 66

Data Science Experimentation Data-driven Machine Learning 66