This Week
Bias is a part of life. When most vendors talk about bias and its management with AI, they are really talking about compliance. The bias correction that sells has to do with staying on the good side of the EEOC.
In particular, they are not on the lookout for the kinds of bias that causes a group of individuals to gel into a high-performance team. They’re not really looking for any kinds of bias except those that are (or should be) illegal. Reducing the bias that comes from dominating the industry (for example) isn’t on anyone’s radar just yet.
Bias is a part of life. I prefer Strawberry preserves; you favor Raspberry. I like 30’s Jazz; House music is your thing. I prefer Toyotas; you enjoy Hondas. Sox, no sox. Potato, Potahto.
The idea that technology can eliminate bias, solve racism, interrupt misogyny, reverse religious discrimination, or remove politics from the raise plan is very wishful thinking. Bias is just another way of saying ‘point of view.’
Emerging Intelligence is focusing on managing and/or reducing the level of illegal bias. Anytime you hear the term ‘bias’, assume that it means compliance.
Hopefully, the future of intelligent systems will about seeing the next layer as well.
This week’s bits and pieces look at the emergence of product liability, simple metaphors for machine learning, and metrics about team-ness.
– John Sumser
Big Picture
- Bias detectives: the researchers striving to make algorithms fair : As machine learning infiltrates society, scientists are trying to help ward off injustice. In particular, study the sidebar about fairness. As ML driven decision making comes to people decisions, understanding the ethical challenges will be critical.
- The Rise of Artificial Intelligence: Future Outlook and Emerging Risks. Seen from the perspective of Allianz, the huge global insurer. If you want a clear picture of the actual risks, ask an insurance company. Software Product liability insurance is coming. You can’t hold a robot accountable, can you?
HR’s View
- A brave new world: Can robots be sued? Yes, but: Exactly who counts as the manufacturer might not be immediately obvious, says John Kingston, a professor who studies AI and law at the University of Brighton. If a self-driving car kills a pedestrian, the car company might be considered the manufacturer, he said — but it could also be a subcontractor who wrote the software, or even a hardware supplier that produced a faulty camera.
- What makes a great team? Chasing the holy grail of baseball performance. From the Atlantic Monthly. Team performance is not simply the aggregate of individual performance. Something happens in the chemistry that makes the difference. Smart data scientists are hunting for the factors. No one has the answer yet.
Execution
- Amazon employees demand that company cut ties with ICE. The relationship between employer and employee has changed.
Tutorial
- MIT’s Intro to AI. All of the classroom lectures and great resources. Free
- Ways To Think About Machine Learning. Very simple and to the point description of ML realities. Take five minutes and digest this.
Quote of the Week
“I think one could propose a whole list of unhelpful ways of talking about current developments in machine learning. For example:
– Data is the new oil
– Google and China (or Facebook, or Amazon, or BAT) have all the data
– AI will take all the jobs
– And, of course, saying AI itself.
More useful things to talk about, perhaps, might be:
– Automation
– Enabling technology layers
– Relational databases.
Why relational databases? They were a new fundamental enabling layer that changed what computing could do. Before relational databases appeared in the late 1970s, if you wanted your database to show you, say, ‘all customers who bought this product and live in this city’, that would generally need a custom engineering project. Databases were not built with structure such that any arbitrary cross-referenced query was an easy, routine thing to do. If you wanted to ask a question, someone would have to build it. Databases were record-keeping systems; relational databases turned them into business intelligence systems.
This changed what databases could be used for in important ways, and so created new use cases and new billion dollar companies. Relational databases gave us Oracle, but they also gave us SAP, and SAP and its peers gave us global just-in-time supply chains – they gave us Apple and Starbucks. By the 1990s, pretty much all enterprise software was a relational database – PeopleSoft and CRM and SuccessFactors and dozens more all ran on relational databases. No-one looked at SuccessFactors or Salesforce and said “that will never work because Oracle has all the database” – rather, this technology became an enabling layer that was part of everything.”
Companies We Talked To Last Week
- HiQ Labs Still wrestling with the bear. LinkedIn appeal in progress.
- Woo. Gated opportunities for Silicon Valley developers.
- IBM. Career Assistant. IBM as the “middle layer of AI” for the TA industry.
- Traitify. Easiest full on assessment interface: 2 Minutes
- Crowded. Automated, staged, optimized refreshment of stale ATS data.
- HackerRank. Skills testing in software.
About
Curate means a variety of things: from the work of vicar entrusted with the care of souls to that of an exhibit designer responsible for clarity and meaning. At the core, it means something about the importance of empathy in organization. HRIntelligencer is an update on the comings and goings in the Human Resource experiment with Artificial Intelligence, Digital Employees, Algorithms, Machine Learning, Big Data and all of that stuff. We present a few critical links with some explanation. The goal is to give you a way to surf the rapidly evolving field without drowning in information. We offer a timeless curation of the intersection of HR and the machines that serve it. We curate the emergence of Machine Led Decision Making in HR.
