Inclusivity

Systemic Data Ethics Framework: A Stable Foundation for Responsible Innovation

by

Author: Peter Brownell; Publisher: Systemic Data Ethics; Publication Year: N/A. The following article and visualization establishes a framework for data ethics that can be mapped visually in a number of ways. At its core, the framework is divided into 12 distinct domains that stand up as pillars of data ethics – enough to fully encapsulate the fine issues of data ethics but still few enough to simplify the complex notion. The 12 domains are…

How to Stop the Metaverse from Becoming the Internet’s Bad Sequel

by

Author: Micaela Mantegna; Publisher: TED; Publication Year: 2022. The following talk features Micaela Mantegna who is a video games lawyer who is an advocate for ways to improve the metaverse. She explains in this video how the metaverse is failing to reach its potential of being inclusive and accessible in its current state. Currently, access to the metaverse is based on how much an individual or organization can…

AI & Data Fairness & Bias

by

Author: Kathleen Walch, Ronald Schmelzer; Publisher: AI & Data Today; Publication Year: N/A. The following podcast focusses on how building frameworks can help avoid the pitfalls associated with bias and fairness in artificial intelligence (AI). Unchecked, we could use algorithms to reinforce biases in the real world. Inclusivity is key: build your algorithms with the target audience in mind, but mind your edge cases as people are not one-size fits…

AI Ethics (AI Code of Ethics)

by

Author: George Lawton, Ivy Wigmore; Publisher: Tech Target; Publication Year: N/A. The following definition discusses how an artificial intelligence (AI) code of ethics is important because it is able to provide guidance to stakeholders to make ethical decisions regarding the use and implementation of artificial intelligence. The 4 ethical challenges surrounding AI are explainability, responsibility, fairness, and misuse. For AI to be ethical it…

Ethical Issues Surrounding Research of AI in Health Care

by

Author: F. Lisa Murtha, Pralika Jain and Kiyong Song; Publisher: Reuters; Publication Year: 2022. The following article discusses the controversy around artificial intelligence (AI) in healthcare has become a barrier to using AI to better life. Some say AI models can be flawed and effect patient safety. Models only do what we tell them, so is it entirely the AI’s fault? To further help humans lives in relation to AI, the World Health Organization released a…

How Inclusive Machine Learning Can Benefit Your Organization

by

Author: Christopher Tozzi; Publisher: ITPro Today; Publication Year: 2022. The following article discusses how businesses have started putting more of an “inclusive” focus on machine learning models to avoid bias and error while creating models. The article shares several benefits to an inclusive approach. These include reaching more users, having happier users, less complaints, less support…

The Numbers Don’t Speak for Themselves

by

Author: Catherine D’Ignazio and Lauren Klein; Publisher: MIT Press; Publication Year: 2020. The following book chapter discusses how context is everything. Numbers are just numbers and cannot speak for themselves. Future data scientists need to incorporate the context with whatever models that they build because this fosters understanding the environment where the data was collected and in turn, produce…