The Problem with Machines? They Aren’t Human Enough

Humans are full of conscious and unconscious biases. For example, a 2012 study in Quebec showed that in considering equally qualified and skilled candidates, those with last names like Ben Saïd were 35 per cent less likely to be called back for an interview than those with last names like Bélanger.

Our machines are learning from this data. They are being taught through AI systems that in fact “Bélangers” are more qualified than “Ben Saïds.” So, as we use AI to predict recidivism in the criminal justice system, to determine loan eligibility or for job application screening, we are further embedding systemic discrimination in our institutions. This is unfair and unethical. It is also a great economic loss. One solution is to teach machines in a similar way to the human brain.

Source: Montreal Gazette

Linking Distributed Data for a Human-Friendly Web

At the Linked Data-driven company of the near future:

1. You will find it curiously difficult to distinguish between “traditional” data workers (analysts, data scientists, etc.) and those in other functional areas who, at other companies, are less reliant on data. The agent of change here is the unambiguous way that Linked Data represents the world.

2. You will marvel at the volume and variety of data accruing from disparate sources, flowing from team to team, integrating with other data, producing unexpected insights, available to anyone at any time.

The data, for example, would be browseable and searchable by humans, crawlable and queryable by machines. Additionally, just like the Web, Linked Data enjoys a remarkable network effect in that each data set added to the network increases the incremental value of every data set in the network.

3. You will be inspired by the rapid creation and adjustment of models and automated processes in response to real-time data. Much of this agility is fueled by machine learning models being deployed at a far faster pace than can be achieved without the aid of Linked Data.

This is because the output of machine learning is tightly correlated with the quality of input data. People who work in this area spend much of their time cleaning and preparing input data, whereas semantically linked data has been “pre-understood” and embedded with knowledge.

[Now,] the energy devoted to the costliest, slowest phase of data work — preparation — can finally be reallocated to more productive activities like analysis.

Source: Techonomy

LinkedIn’s George Anders to Nomads: ‘Network with Affection’

Does that phrase startle you? It floored me the first time I heard it from Mara Zepeda, a thriving Portland, OR entrepreneur. From my big company days, I’d regarded networking as a pretty relentless, sterile exercise. Go to a conference, collect business cards. Call 20 “contacts” and be satisfied if anyone engages at all.

The comforts of a big company logo and a shared contact management system could keep me going  forever. Flying solo, however, that hard-nosed old system falls apart.

I found myself swapping favors with other strivers, hoping that time and trust would take us to a good place. We started with trifles like restaurant recommendations or a few minutes of editing advice on a blog post; eventually, we teamed up on everything from high-profile speaking engagements to a hike across the Grand Canyon. We owned up to our vulnerabilities and created opportunities for each other.

The result: friendships across America (and England!) that straddled work and our off-duty identities in ways I hadn’t expected.

Source: George Anders via LinkedIn Pulse

‘Silver Tsunami’ of Open Data Makes for Millennial Innovators

The number of fed, state and local civilian employees eligible for retirement has risen sharply. Meanwhile, new talent isn’t flocking to fill open government positions.

Massachusetts Comptroller Tom Shack suggests technology as a solution. “No one is going to hire their way out of the Silver Tsunami. We’re going to have to tech our way out of it.” Shack launched CTHRU, a cloud-based, open records platform that eliminates hundreds if not thousands of hours of work by his staff to access and share data. Rather than keep the state’s financial information locked in PDFs, individual computers, or in the customized, cumbersome, legacy finance systems, CTHRU shows payroll, budget, and spending data to anyone on a mobile device.

Shack understands the urgency of unearthing as much data as possible before employees with valuable institutional knowledge of programs retire from state service. Governments produce vast amounts of data. Of all the ways technology can reduce staff workloads, making data standardized and accessible in the cloud is one of the most impactful. Unlocking “tribal knowledge” trapped in employees’ minds and their computers opens up nearly endless avenues for process improvement.

With automated data flows, agencies can give the new workforce the empowerment of analyzing and learning from the data, not just the job of collecting and storing it.

Source: StateScoop

Portable Reputations Would Fix Gig Ratings Mess

The platform economy has the potential to be wildly democratizing, because more transparent networks for finding work should mean larger numbers of people getting new opportunities. But many of these platforms don’t let workers have any control over their reputations.

Those of us striving to organize workers in the online economy have to build a theory for reputation portability and protection into our other work. We can’t let reputation management become disaggregated from the platforms on which workers get work. We should build organizations that can evolve as the tech work evolves.

Source: Kati Sipp via Wired

How to Make Ideas, Innovation Count in Today’s ‘Tomorrow Workplace’

Simply deploying a social network and expecting automatic engagement and a culture of social collaboration from employees is an optimistic laden exercise in futility. Social software is only effective if your targeted users (employees or customers) are actually using it for communication.

  1. Seek out diversity
  2. Understand the motivations of the crowd to participate and engage your audience
  3. Successfully identify and pursue the right ideas for business outcomes
  4. Get the outcomes you want through rewards and recognition
  5. Measure effectiveness and usage

Employees’ cognitive surplus is the most valuable, most under-utilized asset organizations have. Tap into that surplus and encourage the best ideas to come to the forefront through more targeted, specific innovation management platforms.

Source: Wired

The Four Worlds of Work in 2030

Digitization, the rise of automation, and shifting demographics are disrupting the way we work, and the way companies relate to workers. The dizzying pace of change makes it difficult to plan for the long-term. With so many complex forces at play, making linear predictions based on recent trends is too simplistic.

We at PwC envision four alternative future worlds of work, each named with a color. These admittedly extreme examples of how work could look in 2030 are shaped by the ways people and organizations respond to the forces of collectivism and individualism, on one axis, and integration and fragmentation on the other.

Source: Strategy+Business

Cross-Sector Partnerships Focus of NJ Chamber Committee

This is the first committee in the organization’s history that has been formed for an age demographic, rather than an industry sector, and that is deliberate. From speaking to employees who have just entered the workforce, it became clear that there are not currently any cross-sector business networking groups for young business minds that can offer quite the same access to fellow professionals afforded by the Chamber. Careers are likely to involve working for multiple companies and may well span different industries, and we hope that critical cross-sector connections made early on will prove invaluable in the future.

Source: Eliot Lincoln via Jersey Evening Post