User Interface Design Ethics in AI – Part I

“The essence of traditional interfaces is a deep emphasis on clarity (or intuitiveness). One look at the interface tells you what to do. That doesn’t work with likelihoods. Intelligent output like you’d find in machine learning requires the user to think before deciding.” - John Sumser
 

HR Tech: AI and Intelligent Software Implementation – Part II

“From data quality to regulatory compliance, there are 28 key parameters to evaluate when considering a purchase of intelligent tools for HR. We’ve also provided 40 key questions to ask to evaluate an AI or Intelligent Software vendor solution.” - John Sumser
 

HR Tech: AI and Intelligent Software Implementation – Part I

“The question is when, not whether, organizations will use intelligent tools. The future is inevitable. The sooner you get started, the easier it will be to keep up. Conversely, the longer you wait, the more competitive advantage you will lose.” - John Sumser
 

HRExaminer v12.02

Heather Bussing explains how the pandemic demands that we reimagine our indispensable work. Reimagining What is Necessary Work During the Coronavirus Pandemic.



Jon Stross and his Greenhouse co-founder noticed that certain ways of leading and communicating during the pandemic suddenly snapped into focus while previous methods no longer fit the scene. Sometimes Leading Means Letting Go During the Pandemic (Lessons Learned in Zoom Rooms).



After the Furlough there are five things you should do to prepare for your employees’ return. John Sumser has more.



Recognizing AI in our HR Tech means trying to remember, in the onslaught of machine opinion, that by accepting the machine’s opinion you are making a decision. John Sumser has more in, Can we recognize baked-in AI in HR Tech in order to manage it properly?



 

Can we recognize baked-in AI in HR Tech in order to manage it properly?

“AI is quickly becoming a table-stakes commodity. Since it will often be unlabeled, the question will be, ‘How do we recognize it?’ This is not the discovery of a rarity, it’s remembering to be effective in the flow of recommendations, suggestions, forecasts, probabilities, and interpretations that fill our every transaction. Recognizing AI means trying to remember, in the onslaught of machine opinion, that by accepting the machine’s opinion you are making a decision.” - John Sumser
 

HRExaminer v12.01

Our systems were not designed to scale during an abrupt shift from centralized to distributed work. TJ Fjelseth, CHRO of Socrates.ai expands on the Conversations emerging during an abrupt shift from centralized to distributed work.



What should modern bereavement leave look like in pandemic times? Surely, the standard three days of paid leave to say farewell won’t cut it. Read, Modern Bereavement Leave in Pandemic Times from John Sumser.



Michael Kannisto, Ph.D. explains that college recruiting has a strict timeline and with each passing day the class of 2020 faced the prospect of being excluded from the recruiting process. The Lost Generation of College Recruits.



Neurodiverse people often possess remarkable technical and problem-solving capabilities. They end up surfacing insights that others miss. Heather Bussing has more about Hiring Neurodiverse People.



John Sumser asks you to consider who is really important at your company. Instead of paying attention to the people with charisma and connections, you need to focus on people with competence. 8 Steps to Identify Your Everyday MVP Employees During The Pandemic.



 

HRExaminer v11.04

In times of scarcity or crisis like the pandemic, the rules change. Business continuity becomes the governing question. Michael Kannisto, Ph.D. looks at MVP’s during the Pandemic: Modern Succession Planning for Operational Continuity.



In March 2020, many workers fled their offices for the safety of home. The change was hurried by necessity, but with little understanding of the consequences. Stella Lupushor has more on Zoom and a bit about the downsides of decentralized work.



Fara Rives discusses how AI can help overcome a key roadblock with outdated employee and contractor information. AI Can Improve Talent Mobility.



It’s one of the most common workplace mistakes: applying the wrong solution to a situation simply because you went with your “go to”move. Hello Hammer, Meet Nail from Dr. Todd Dewett.



 

HRExaminer v11.03

Heather Bussing writes, “If there is only a 10% chance of rain, you probably won’t take an umbrella. But one in ten times, you will still get wet.” Read Heather’s two-part series on How to Not Screw Up Predictions (when you’re working with AI and Intelligent Tools).



In part two of her series, Heather Bussing shares an exercise and analysis on How to Not Screw Up Predictions (Exercise: Consider and Decide).



John Sumser writes, “The work of synthesizing, prioritizing, and then acting on the data is increasingly what work is all about. And that’s where the new tools come in handiest.” Modern HR Data Types and Attributes (From Text to Machine Generated or Monitored Data).



“Ethical questions about how we use data and technology in organizations are growing in prominence and importance and bias is the noisiest of the ethics questions now facing HR.” John Sumser has more in, Bias in AI: Are People the Problem or the Solution?



In this combined article and podcast the topic is Open Feedback and Joyous Co-founder Mike Carden is in the driver’s seat. Open Feedback with Mike Carden (article and Podcast).



 

Bias in AI: Are People the Problem or the Solution?

On December 17, 2020, in AI, AI Ethics, Artificial Intelligence, HR Tech, HR Technology, John Sumser, Machine Learning, by John Sumser
“All tools contain embedded biases. Bias can be introduced long before the data is examined and at other parts of the process. Meanwhile, one group says people are the problem; the other sees them as the solution.” - John Sumser
 

Modern HR Data Types and Attributes (from text to machine generated and monitored data)

“Data has a funny property. It wants to make more data. There’s a saying, ‘data makes its own gravy.’ Using data creates data about usage. Interestingly, the metadata created by data is often more useful than the data itself.” - John Sumser