Labor & Employment Law

How Human-Centric AI Can Help Your Employees Love Mondays Again


Companies have an expensive retention problem. Identifying, sourcing and training the right talent, which can range from $4,000 to nearly $60,000, is simply the first step in a journey of the employee experience. Yet, this investment does not always bear fruit, as the average time an employee spends at a company is only 4.2 years, according to the Bureau of Labor Statistics. Estimates in Silicon Valley are much lower. What makes people stay at their place of work?

Use Cases of AI for Customer Service - What's Working Now -


Artificial Intelligence is currently being deployed in customer service to both augment and replace human agents – with the primary goals of improving the customer experience and reducing human customer service costs. While the technology is not yet able to perform all the tasks a human customer service representative could, many consumer requests are very simple ask that sometimes be handled by current AI technologies without human input. In this article we'll shed light on the current trends and use-cases that business leaders should be considering today. We've broken this article on AI for customer service into the following four sections: According to the Bureau of Labor Statistics there were just over 2.7 million Americans employed as customer service representatives with a mean wage of $35,170. Any technology that could improve the efficiency of customer service representatives or make some of these positions redundant would potentially produce significant business savings.

How Will Artificial Intelligence (AI) Impact Employee Benefits?


There is a lot of buzz about Artificial Intelligence (AI) and how it will revolutionize our world for the better. The question for human resource professionals is: What sort of impact will AI have on employee benefits?

WEF Global Risk Report 2017


The Fourth Industrial Revolution is fundamentally changing the ways that people work and live in three main ways. First, it is untethering some types of work from a physical location, making it easier to remotely connect workers in one region or country to jobs in another – but also making it less clear which set of employment laws and taxes apply, creating greater global competition for workers, potentially weakening employment protections and draining public social protection coffers. Second, human labour is being displaced by automation, robotics and artificial intelligence. Opinions differ on the extent of what is possible: Frey and Osborne's (2013) study found that 47% of US employment is at high risk of being automated over the next two decades, while a 2016 study of 21 Organisation for Economic Co-operation and Development (OECD) countries, using a different methodology, concluded that only 9% of jobs are automatable. In general, lower-skilled workers are more likely to see their jobs disappear to automation, increasing their vulnerability and exacerbating societal inequality. Finally, the nature of the contract between employer and employee is changing, at the same time that the move to a sharing and collaborative economy increases the prevalence of jobs that fall outside the standard employment contract model. The shift has some positive implications for workers, as it potentially offers more control over when and whether to work and opportunities to supplement their incomes – renting out a room through Airbnb, for example, or driving part-time for a service such as Uber.

A Lot of "Ethical Consumers" Are Going to Make Really Unethical Shopping Choices

Mother Jones

As a person living in the 21st century, it's almost inevitable that you've had the seamless, fast, and hassle-free experience of shopping online: a few clicks and you're done without ever needing to interact with anyone, and then your items can show up at your door in as little as a day. But as the holiday season ramps up, it's a good time to remember that there's actually a whole lot of human labor behind that fast and easy click. While we at Mother Jones recently reported on how robots will one day take these jobs, they haven't taken over just yet. Just consider a great story last week from Gizmodo's Bryan Menegus shedding light on a mysterious program known as Amazon Flex: a "nearly invisible workforce" of independent contractors charged with delivering the "last mile" of Amazon orders from a local storage facility to the customer's door. As Menegus explains, "It's a network of supposedly self-employed, utterly expendable couriers enrolled in an app-based program which some believe may violate labor laws."

Pymetrics attacks discrimination in hiring with AI and recruiting games


Identify the traits of your top performing employees and hire people like them, but without the discrimanatory bias of traditional recruiting. That's the promise of Pymetrics, an artificial intelligence startup that today announced $8 million in new funding onstage at TechCrunch Disrupt SF. Pymetrics' goal is "making the world a fairer place" by dismantling hiring discrimination like sexism, racism, ageism and classism. Anyone can play the Pymetrics test games and get scored on different hireable traits, plus see suggestions for job types they'd be great at. You can watch my interview with Pymetrics' CEO Frida Polli below: A company's all-star employees play Pymetrics' set of games that assess things like memory, emotion detection, risk-taking, fairness and focus.

Human machine: A new era of automation in manufacturing


New technologies are opening a new era in automation for manufacturers--one in which humans and machines will increasingly work side by side. Over the past two decades, automation in manufacturing has been transforming factory floors, the nature of manufacturing employment, and the economics of many manufacturing sectors. Today, we are on the cusp of a new automation era: rapid advances in robotics, artificial intelligence, and machine learning are enabling machines to match or outperform humans in a range of work activities, including ones requiring cognitive capabilities. Industry executives--those whose companies have already embraced automation, those who are just getting started, and those who have not yet begun fully reckoning with the implications of this new automation age--need to consider the following three fundamental perspectives: what automation is making possible with current technology and is likely to make possible as the technology continues to evolve; what factors besides technical feasibility to consider when making decisions about automation; and how to begin thinking about where--and how much--to automate in order to best capture value from automation over the long term. To understand the scope of possible automation in the manufacturing sector as a whole, we conducted a study of manufacturing work in 46 countries in both the developed and developing worlds, covering about 80 percent of the global workforce.

Hiring Algorithms Are Not Neutral


More and more, human resources managers rely on data-driven algorithms to help with hiring decisions and to navigate a vast pool of potential job candidates. These software systems can in some cases be so efficient at screening resumes and evaluating personality tests that 72% of resumes are weeded out before a human ever sees them. But there are drawbacks to this level of efficiency. Man-made algorithms are fallible and may inadvertently reinforce discrimination in hiring practices. Any HR manager using such a system needs to be aware of its limitations and have a plan for dealing with them.

Automation May Be Creating Jobs--in Retail, at Least


Since 2007, 140,000 brick-and-mortar retail jobs have vanished in America. Meanwhile, according to the Bureau of Labor Statistics defintions, e-commerce has created just 126,000 over the same perioud. The takeaway, it seems: automation, here in the form of the software and robots that power online retail, is eating jobs. But according to a new analysis by the Progressive Policy Institute, those figures miss the point. If you actually include all the fulfillment-center jobs that e-tail has created, which wouldn't have otherwise needed to exist, the figure rises from 126,000 to 400,000, far outweighing physical-store losses.

The Affirmative Action of Vocabulary – Alistair Croll – Medium


Most machine learning is literally prejudice--telling a machine, "based on what you've seen in the past, predict the future." But what do we do when it's also correct? Many of the most popular examples of "artificial intelligence" today are actually about classification. For example, we can show a computer past pictures of dogs, and have it predict whether a new picture is a dog (or a pastry.) We can also use those predictions to do things like mimic an art style, or try and anticipate what word will come next in a sentence, or suggest people with whom you might want to connect.