I collect links to articles and papers that inform my own thinking and writing. From time to time I will share some of those that seem relevant to recruiters, learning and development folks, data scientists, and HR people.
It is increasingly obvious that data analysis and A.I. are changing and improving our understanding of human behavior. These tools are helping us to better understand the past by analyzing vast amounts of historical data. We are also learning more about our own biases through these tools and how our algorithms are influenced by these same biases.
This selection of links and articles discuss how A.I. and data analytics are
If you have other links you would like to share, please share them in the comment section below.
3.2 Million USD raised for VR-based diversity & inclusion app
I saw something like this demonstrated in Australis about 3 years ago.
It places you in a simulated environment where you take on different roles and genders and either experience bias and lack of inclusion or act as the perpetrator of bias. Interesting but not sure it justifies a $3.2 million investment.
An Introduction to Bias & Statistical Reasoning
This is an excellent and easy-to-understand discussion on what bias is, why it is so common, and also almost impossible to remove. We make so many assumptions that seem logical yet when looked at statistically are not. One example from the article:
“Imagine meeting someone for the first time, and knowing nothing about them except that they’re shy.
Question: Is it more likely that this person is a librarian, or a salesperson?
Most people answer “librarian.” Which is a mistake: shy salespeople are much more common than shy librarians, because salespeople in general are much more common than librarians—seventy-five times as common, in the United States.”
How Data Analysis Can Enrich the Liberal Arts (Paywall warning)
An Economist article describes how data analytics is helping researchers of literature, language, social science. music and other liberal arts understand the evolution of words, connections between historical events and social events, and much more. This marriage of data and arts has the promise to revolutionize academic research. Good examples of data tools used for studying words include Google Books, which has cataloged over 25 million books, and Google Scholar with more than 75,00 articles published since 2016. For music, Spotify has recorded the tempo, time, and timbre of more than 60,000 tunes.
All The Ways Hiring Algorithms Can Introduce Bias
Understanding bias in hiring algorithms and ways to mitigate it requires us to explore how predictive technologies work at each step of the hiring process. Though they commonly share a backbone of machine learning, tools used earlier in the process can be fundamentally different than those used later on. Even tools that appear to perform the same task may rely on completely different types of data, or present predictions in substantially different ways. An analysis of predictive tools across the hiring process helps to clarify just what “hiring algorithms” do, and where and how bias can enter into the process. Unfortunately, most hiring algorithms will drift toward bias by default. While their potential to help reduce interpersonal bias shouldn’t be discounted, only tools that proactively tackle deeper disparities will offer any hope that predictive technology can help promote equity, rather than erode it.
What Buddhism can do for AI ethics
How Buddhist teachings could influence how we develop and use A.I.
Buddhism teaches that an action is good if it leads to freedom from suffering or reduces pain or makes a process more user-friendly. It also teaches that we should “do no harm” and practice compassion. Do our A.I. systems comply with these?
Data trusts are just emerging but are legal entities that manage your personal data and ensure that it is safe. A very interesting concept. This is what I had hoped blockchain would do, but that has not been successful yet at getting people to understand and use it.
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