AI, the buzz word known as Artificial Intelligence, in practice can be explained as Augmented Intelligence; a tool that has been in development since 1950s to extend human capabilities to complete tasks no human or machine could accomplish on their own. The world has already shifted towards building an AI integrated future and it's our responsibility to ensure it's heading in the right direction. "People are overlooked for a variety of biased reasons and perceived flaws; mathematics cuts straight through them" (Moneyball, 2011) Despite its immense potential, some major barriers still exist for AI hindering its progress. Biased behavior uncovered in current AI models has made us question if AI is the right way forward. To avoid such unfavourable outcomes and consequences, it is imperative to regulate the implementation of AI by an ethical framework assuring the key attributes; Transparency, Accountability, Privacy and Lack of Bias.

This code pattern describes a way to gain insights by using Watson OpenScale and a SageMaker machine learning model. It explains how to create a logistic regression model using Amazon SageMaker with data from the UC Irvine machine learning database. The pattern uses Watson OpenScale to bind the machine learning model deployed in the AWS cloud, create a subscription, and perform payload and feedback logging. With Watson OpenScale, you can monitor model quality and log payloads, regardless of where the model is hosted. This code pattern uses the example of an Amazon Web Service (AWS) SageMaker model, which demonstrates the independent and open nature of Watson OpenScale.

This code pattern uses a German Credit data set to create a logistic regression model using Azure. The pattern uses Watson OpenScale to bind the machine learning model deployed in the Azure cloud, create a subscription, and perform payload and feedback logging. With Watson OpenScale, you can monitor model quality and log payloads, regardless of where the model is hosted. This code pattern uses an example of an Azure model, which demonstrates the independent and open nature of Watson OpenScale. IBM Watson OpenScale is an open environment that enables organizations to automate and operationalize their AI.

IBM continues to serve as an industry leader in advancing what we call Trusted AI, focused on developing diverse approaches that implement elements of fairness, explainability, and accountability across the entire lifecycle of an AI application. Under our Trusted AI efforts, IBM released in 2018 the AI Fairness 360 toolkit (AIF360), which is an extensible, open source toolkit that can help you examine, report, and mitigate discrimination and bias in machine learning models throughout the AI application lifecycle. It contains over 70 fairness metrics and 11 state-of-the-art bias mitigation algorithms developed by the research community, and it is designed to translate algorithmic research from the lab into the actual practice of domains as wide-ranging as finance, human capital management, healthcare, and education. Now, IBM is adding two new ways in which AIF360 is becoming even more accessible for a wider range of developers, as well as increased functionality: compatibility with scikit-learn and R. AI fairness is an important topic as machine learning models are increasingly used for high-stakes decisions. Machine learning discovers and generalizes patterns in the data and therefore, could replicate systematic advantages of privileged groups.

Many financial institutions are rapidly developing and adopting AI models. They're using the models to achieve new competitive advantages such as being able to make faster and more successful underwriting decisions. However, AI models introduce new risks. In a previous post, I describe why AI models increase risk exposure compared to the more traditional, rule-based models that have been in use for decades. In short, if AI models have been trained on biased data, lack explainability, or perform inadequately, they can expose organizations to as much as seven-figure losses or fines.

Run a Google search on "bias in AI" and you'll find all kinds of stories about what can -- and does -- happen when systems become automated and the human element is removed. Of course, today, AI and machine learning are embedded into myriad different technologies, and while it no doubt plays a positive role, biased data is often problematic. As AI applications become more prevalent, channel firms can play a role in helping customers mitigate algorithm bias. Biased AI ranked the second biggest AI-related ethical concern associated with AI in Deloitte's 2018 "State of AI in the Enterprise" study, behind AI's power to help create and spread false information. "Today, algorithms are commonly used to help make many important decisions, such as granting credit, detecting crime, and assigning punishment," the report notes.

With its electro-light tulip garden, disco ball-adorned trees and no stone-left-unturned music lineup, "Denmark's Most Beautiful Festival" aims to surpass guests' expectations on safety, comfort and entertainment, from its uncannily clean bathrooms down to its whimsical camp-in-a-beer-can glamping options. The Skanderborg Music Festival (aka "Smukfest"), located in the northern European country of Denmark, is no stranger to nature's mayhem and its impact on tens of thousands of battle-hardened party warriors. Though it takes place during the second weekend of August in a bedazzled eco-village village deep in a beechwood forest, the weather doesn't always comply. After 2018's sunbaked soiree, a veritable jester court of cloud-bursts in 2019 left lesser warriors stomping out early in their rain boots – leaving waiting oranges longing to fulfill their destiny as a vodka's partner in crime. Could those unpreventable forces be mitigated by data and AI?

What does a "journey" mean to you? At IBM, our long standing tradition of journey exploration has led humans to the moon and coined the term machine learning 50 years ago. Now we are helping organizations scale the ladder to AI to reap rewards in growth, productivity and efficiency with IBM Watson. This journey to AI mirrors the history of travel. In this article, I'll explain how IBM Cloud Pak for Data accelerates the journey to AI and delve into the ways AutoAI helps boost the speed of business returns.

This tutorial is part of the Getting started with Watson OpenScale learning path. In this tutorial, you'll see how IBM Watson OpenScale can be used to monitor your artificial intelligence (AI) models for fairness and accuracy. You'll get a hands-on look at how Watson OpenScale will automatically generate a debiased model endpoint to mitigate your fairness issues and provides an explainability view to help you understand how your model makes its predictions. In addition, you'll see how Watson OpenScale uses drift detection. Drift detection will tell you when runtime data is inconsistent with your training data or if there is an increase the data that is likely to lead to lower accuracy.

Climbing the AI ladder: How does that affect my business? Businesses love the idea of putting data to work. Building and scaling AI with trust and transparency -- sounds great, right? As enterprises adopt machine learning to streamline customer service and remedial tasks, their employees can provide better customer experience while freeing themselves up to work on more interesting problems. IBM leads the industry in empowering enterprises to accelerate the journey to AI.