Daily Archives: August 4, 2020

How digitization must be harnessed to save jobs

A framework agreement between the social partners should ensure job security and worker involvement are prioritized across the European Union.
By Esther Lynch – The announcement of jobs losses around Europe as a result of the Covid-19 pandemic has become an almost daily occurrence, as all sectors struggle to cope with the impact of lockdown. Preliminary research indicates that restructuring job losses have doubled in the second quarter of 2020, compared with previous years. Some 60 million EU workers are at risk of unemployment as the consequences of the crisis play out.

Trade unions are at the forefront of efforts to protect workers’ livelihoods. At the end of June, the European Trade Union Confederation and the three major EU-level employers’ organizations—BusinessEurope, CEEP and SMEunited—signed an Autonomous Framework Agreement, to work together on the introduction of digitalisation in workplaces across Europe. In the context of Covid-19, the deal has much wider relevance, as it provides a blueprint for negotiating a ‘just transition’ and change in the world of work.

The priority is to encourage an approach that fully involves workers and their trade unions. This must apply to restructuring situations caused by the virus, as well as to planned change. The agreement sets out that, instead of making redundancies, employers need to look at other options for maintaining and investing in their workforces, creating new opportunities and enabling workers to adapt to change.

The agreement applies across the EU, covering both public and private sectors and all economic activities, including online platform workers. The right of trade unions to represent workers is recognized and the agreement specifies that, in preparing for negotiations, unions must be able to consult all employees and should have the facilities and information required to participate fully throughout.

The issues of digitization, restructuring and equipping different sectors to respond to the coronavirus crisis are all interlinked. The fact that so many workers have suddenly found themselves relying on digital technologies to carry out their tasks has created a step change in terms of work organization. One survey indicates that 74 per cent of companies expect some of their staff to continue working remotely in the long term. These workers must have full employee rights and representation, with no erosion of pay and working conditions. more>

Updates from Adobe

Drawing Fashion
By Kasia Smoczynska – Kasia Smoczynska finds her inspiration on the catwalk.

At the launch of Givenchy’s 2019 Spring Collection in Paris, it was a technicolor gown that took her breath away. Created by British fashion designer Clare Waight Keller, the dress is a kaleidoscope of moving ribbons topped with an Elizabethan ruffle. To Smoczynska, it looked like “thousands of colorful fringes moving in every direction.”

“I had to draw it,” she says.

A few days later, Smoczynska was back in Leeds, England, where she lives and works. She dropped her iPad Pro onto an easel she had once purchased to make gouache paintings. (“I did maybe two paintings, and now I use it only for my iPad,” she admits.) Then, firing up Adobe Fresco, Smoczynska started to draw the gown with sweeping digital brushstrokes of yellows, blues, and reds. “I knew it would be fun to illustrate all those fringes,” she recalls.

The finished piece is typical of Smoczynska’s work: expressive, spontaneous, and charged with energy. “In my eyes it’s a look for a modern queen,” she says, explaining why her model sits atop a throne instead of marching down the catwalk. more>

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Updates from Ciena

Planning for 5G Success: A Tale of Two Operators
The industry is moving forward with 5G deployments, motivated by differentiated service offerings. Blue Planet’s Soumen Chatterjee describes how 5G Automation is helping two mobile network operators plan their own path to 5G success.
By Soumen Chatterjee – In my earlier blog, I wrote about the promise of 5G network slicing, which opens the door to a variety of service offerings, to support differentiated requirements across industry sectors. In the interim, the current challenging economic time of the coronavirus pandemic has given mobile network operators (MNOs) a chance to re-assess their 5G strategies and double-down on pursuing new service opportunities.

The shift in consumer lifestyle patterns may have impacted the timing of some 5G use cases – industrial automation demand may slow, but interest for multi-media remote sporting experiences is anticipated. 5G brings unprecedented opportunities to provide customers with new services and an exceptional user experience, given performance of up to 100 Gbps and latency in the order of 1 millisecond. But 5G also brings additional operational complexity with network slicing technology, new radios, rearchitected transport, and a virtualized 5G core. 5G needs automation in the backend to manage this increased complexity and to contain associated operational costs. For MNOs, automation is a must, not an option.

In my discussions with MNOs, it is apparent that planning for 5G deployments is heavily influenced by an operator’s legacy infrastructure – infrastructure that exists in the field and systems that exist in the network operations center (NOC). However, no matter the starting point, it is essential to have dynamic planning capabilities that simplify and accelerate each phase of the process.

At one incumbent mobile operator, they are planning to roll-out small cell 5G radios alongside their 4G radios, in non-standalone (NSA) mode. However, they first need to get visibility of their current network assets. Their legacy inventory and operational support systems (OSS) are disjointed, so it is difficult to obtain an accurate and comprehensive view.

Furthermore, those OSS are not up to the task of modelling new 5G constructs. It would be an extremely heavy lift to shoehorn 5G data in, with very limited scope for extensibility. On the other hand, introduction of a new system could further fragment or duplicate operational data.

This is when Blue Planet’s federation capabilities prove to be a crucial step for 5G planning. With Blue Planet’s 5G Automation solution, data from existing systems is federated, reconciled, and synchronized into a new unified data model built on state-of-the-art graph database technology which can accommodate complex 5G relationships. There are also existing business processes – mostly manual – that rely on OSS, which need to be modernized to support automated 5G workflows.

Another MNO customer is a new entrant who is not encumbered by pre-existing infrastructure and OSS, has more flexibility in designing new systems and processes to support their 5G strategy, and can implement them more quickly. This MNO is planning to deploy tens of thousands of 5G cell sites in standalone mode (SA) within a few years. To scale expediently, they need to design-in automation of their business processes from the outset. Blue Planet’s 5G Automation solution is a natural fit, as it provides multi-vendor service orchestration and assurance founded on a unified inventory of hybrid physical and virtual infrastructure

Beyond the radio infrastructure, both MNOs are looking ahead to architecting customizable network slices end-to-end across the radio access network (RAN), transport and cloud domains, to satisfy their customers’ requirements. To this end, Blue Planet provides the holistic operational system to help determine the placement of 5G Core (5GC) virtualized network functions (VNFs) at the edge or in the core, with necessary compute capacity, to best support a variety of latency and bandwidth needs. more>

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Updates from Chicago Booth

Can regulation rein in algorithmic bias?
By Sendhil Mullainathan – Last year, you published a paper documenting how an algorithm used by health-care organizations generated racially biased results. What takeaways did that offer in terms of how algorithmic bias differs from human bias?

That paper might be, by some measures, among the strangest papers I’ve ever worked on. It’s a reminder of the sheer scale that algorithms can reach.

Exact numbers are hard to get, but about 80 million Americans are evaluated through this algorithm. And it’s not for some inconsequential thing: it is an algorithm used by many health-care systems to decide which patients should get put into what are called care-management programs. Care-management programs are for people who are going to be at the hospital a lot. If you have many conditions, you’re going to be in the system frequently, so you shouldn’t have to go through the normal front door, and maybe you should have a concierge who works just with you. You get additional resources to manage this complex care.

It costs a lot of money to put somebody in a care-management program. You really want to target these programs. So the question is, who should be in them?

Over the past five years, there have been algorithms developed using health records of people to figure out who is at highest risk of using health care a lot. These algorithms produce a risk score, and my coresearchers and I wanted to know if there was any racial bias in these scores.

The way we looked for it was to take two people given the same score by the algorithm—one white and one Black. Then we looked at those two people and asked whether, on average, the white person had the same level of sickness as the Black person. What we found is that he or she didn’t, that when the algorithm gives two people the same score, the white person tends to be much healthier than the Black person. And I mean much healthier, extremely so. If you said, “How many white people would I have to remove from the program, and how many Black people would I have to put in, until their sickness levels were roughly equalized?” you would have to double the number of Black patients. It is an enormous gap.

I say it’s one of the craziest projects I’ve worked on in part because of the sheer scale of this thing. But there are a lot of social injustices that happen at a large scale. What made it really weird was when we said, “Let’s figure out what’s causing it.” In the literature on algorithmic bias, everyone acts like algorithms are people, like they’re biased [in the sense that people are]. It’s just a little piece of code. What went wrong in the code?

What we found is something that we’re finding again and again in all of our A.I. work, that every time you see that an algorithm has done something really bad, there’s no engineering error. That’s very, very different than the traditional bugs in code that you’re used to: when your computer crashes, some engineering bug has shown up. I’ve never seen an engineering bug in A.I. The bug is in what people asked the algorithm to do. They just made a mistake in how they asked the question. more>

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