Text Analysis Systems Mine Workplace Emails to Measure Staff Sentiments

Curated on Posted on by Stefaan Verhulst

Alan Rothman at LLRX: “…For all of these good, bad or indifferent workplaces, a key question is whether any of the actions of management to engage the staff and listen to their concerns ever resulted in improved working conditions and higher levels of job satisfaction?

The answer is most often “yes”. Just having a say in, and some sense of control over, our jobs and workflows can indeed have a demonstrable impact on morale, camaraderie and the bottom line. As posited in the Hawthorne Effect, also termed the “Observer Effect”, this was first discovered during studies in the 1920’s and 1930’s when the management of a factory made improvements to the lighting and work schedules. In turn, worker satisfaction and productivity temporarily increased. This was not so much because there was more light, but rather, that the workers sensed that management was paying attention to, and then acting upon, their concerns. The workers perceived they were no longer just cogs in a machine.

Perhaps, too, the Hawthorne Effect is in some ways the workplace equivalent of the Heisenberg’s Uncertainty Principle in physics. To vastly oversimplify this slippery concept, the mere act of observing a subatomic particle can change its position.¹

Giving the processes of observation, analysis and change at the enterprise level a modern (but non-quantum) spin, is a fascinating new article in the September 2018 issue of The Atlantic entitled What Your Boss Could Learn by Reading the Whole Company’s Emails, by Frank Partnoy.  I highly recommend a click-through and full read if you have an opportunity. I will summarize and annotate it, and then, considering my own thorough lack of understanding of the basics of y=f(x), pose some of my own physics-free questions….

Today the text analytics business, like the work done by KeenCorp, is thriving. It has been long-established as the processing behind email spam filters. Now it is finding other applications including monitoring corporate reputations on social media and other sites.²

The finance industry is another growth sector, as investment banks and hedge funds scan a wide variety of information sources to locate “slight changes in language” that may point towards pending increases or decreases in share prices. Financial research providers are using artificial intelligence to mine “insights” from their own selections of news and analytical sources.

But is this technology effective?

In a paper entitled Lazy Prices, by Lauren Cohen (Harvard Business School and NBER), Christopher Malloy (Harvard Business School and NBER), and Quoc Nguyen (University of Illinois at Chicago), in a draft dated February 22, 2018, these researchers found that the share price of company, in this case NetApp in their 2010 annual report, measurably went down after the firm “subtly changes” its reporting “descriptions of certain risks”. Algorithms can detect such changes more quickly and effectively than humans. The company subsequently clarified in its 2011 annual report their “failure to comply” with reporting requirements in 2010. A highly skilled stock analyst “might have missed that phrase”, but once again its was captured by “researcher’s algorithms”.

In the hands of a “skeptical investor”, this information might well have resulted in them questioning the differences in the 2010 and 2011 annual reports and, in turn, saved him or her a great deal of money. This detection was an early signal of a looming decline in NetApp’s stock. Half a year after the 2011 report’s publication, it was reported that the Syrian government has bought the company and “used that equipment to spy on its citizen”, causing further declines.

Now text analytics is being deployed at a new target: The composition of employees’ communications. Although it has been found that workers have no expectations of privacy in their workplaces, some companies remain reluctant to do so because of privacy concerns. Thus, companies are finding it more challenging to resist the “urge to mine employee information”, especially as text analysis systems continue to improve.

Among the evolving enterprise applications are the human resources departments in assessing overall employee morale. For example, Vibe is such an app that scans through communications on Slack, a widely used enterprise platform. Vibe’s algorithm, in real-time reporting, measures the positive and negative emotions of a work team….(More)”.

What is machine learning?

Curated on Posted on by Stefaan Verhulst

Chris Meserole at Brookings: “In the summer of 1955, while planning a now famous workshop at Dartmouth College, John McCarthy coined the term “artificial intelligence” to describe a new field of computer science. Rather than writing programs that tell a computer how to carry out a specific task, McCarthy pledged that he and his colleagues would instead pursue algorithms that could teach themselves how to do so. The goal was to create computers that could observe the world and then make decisions based on those observations—to demonstrate, that is, an innate intelligence.

The question was how to achieve that goal. Early efforts focused primarily on what’s known as symbolic AI, which tried to teach computers how to reason abstractly. But today the dominant approach by far is machine learning, which relies on statistics instead. Although the approach dates back to the 1950s—one of the attendees at Dartmouth, Arthur Samuels, was the first to describe his work as “machine learning”—it wasn’t until the past few decades that computers had enough storage and processing power for the approach to work well. The rise of cloud computing and customized chips has powered breakthrough after breakthrough, with research centers like OpenAI or DeepMind announcing stunning new advances seemingly every week.

The extraordinary success of machine learning has made it the default method of choice for AI researchers and experts. Indeed, machine learning is now so popular that it has effectively become synonymous with artificial intelligence itself. As a result, it’s not possible to tease out the implications of AI without understanding how machine learning works—as well as how it doesn’t….(More)”.

Senators introduce the ‘Artificial Intelligence in Government Act’

Curated on Posted on by Stefaan Verhulst

Tajha Chappellet-Lanier at FedScoop: “A cadre of senators is looking to prompt the federal government to be a bit more proactive in utilizing artificial intelligence technologies.

To this end, the bipartisan group including Sens. Brian Schatz, D-Hawaii, Cory Gardner, R-Colo., Rob Portman, R-Ohio, and Kamala Harris, D-Calif., introduced the Artificial Intelligence in Government Acton Wednesday. Per a news release, the bill would seek to “improve the use of AI across the federal government by providing resources and directing federal agencies to include AI in data-related planning.”

The bill aims to do a number of things, including establishing an AI in government advisory board, directing the White House Office of Management and Budget to look into AI as part of the federal data strategy, getting the Office of Personnel Management to look at what kinds of employee skills are necessary for AI competence in government and expanding “an office” at the General Services Administration that will provide expertise, do research and “promote U.S. competitiveness.”

“Artificial intelligence has the potential to benefit society in ways we cannot imagine today,” Harris said in a statement. “We already see its immense value in applications as diverse as diagnosing cancer to routing vehicles. The AI in Government Act gives the federal government the tools and resources it needs to build its expertise and in partnership with industry and academia. The bill will help develop the policies to ensure that society reaps the benefits of these emerging technologies, while protecting people from potential risks, such as biases in AI.”

The proposed legislation is supported by a bunch of companies and advocacy groups in the tech space including BSA, the Center for Democracy and Technology, the Information Technology and Innovation Foundation, Intel, the Internet Association, the Lincoln Network, Microsoft, the Niskanen Center, and the R Street Institute.

The senators are hardly alone in their conviction that AI will be a powerful tool for government. At a summit in May, the White House Office of Science and Technology Policy created a Select Committee on artificial intelligence, comprised of senior research and development officials from across the government….(More)”.

Future Politics: Living Together in a World Transformed by Tech

Curated on Posted on by Stefaan Verhulst

Book by Jamie Susskind: “Future Politics confronts one of the most important questions of our time: how will digital technology transform politics and society? The great political debate of the last century was about how much of our collective life should be determined by the state and what should be left to the market and civil society. In the future, the question will be how far our lives should be directed and controlled by powerful digital systems — and on what terms?

Jamie Susskind argues that rapid and relentless innovation in a range of technologies — from artificial intelligence to virtual reality — will transform the way we live together. Calling for a fundamental change in the way we think about politics, he describes a world in which certain technologies and platforms, and those who control them, come to hold great power over us. Some will gather data about our lives, causing us to avoid conduct perceived as shameful, sinful, or wrong. Others will filter our perception of the world, choosing what we know, shaping what we think, affecting how we feel, and guiding how we act. Still others will force us to behave certain ways, like self-driving cars that refuse to drive over the speed limit.

Those who control these technologies — usually big tech firms and the state — will increasingly control us. They will set the limits of our liberty, decreeing what we may do and what is forbidden. Their algorithms will resolve vital questions of social justice, allocating social goods and sorting us into hierarchies of status and esteem. They will decide the future of democracy, causing it to flourish or decay.

A groundbreaking work of political analysis, Future Politics challenges readers to rethink what it means to be free or equal, what it means to have power or property, what it means for a political system to be just or democratic, and proposes ways in which we can — and must — regain control….(More)”.

Google is using AI to predict floods in India and warn users

Curated on Posted on by Stefaan Verhulst

James Vincent at The Verge: “For years Google has warned users about natural disasters by incorporating alerts from government agencies like FEMA into apps like Maps and Search. Now, the company is making predictions of its own. As part of a partnership with the Central Water Commission of India, Google will now alert users in the country about impending floods. The service is only currently available in the Patna region, with the first alert going out earlier this month.

As Google’s engineering VP Yossi Matias outlines in a blog post, these predictions are being made using a combination of machine learning, rainfall records, and flood simulations.

“A variety of elements — from historical events, to river level readings, to the terrain and elevation of a specific area — feed into our models,” writes Matias. “With this information, we’ve created river flood forecasting models that can more accurately predict not only when and where a flood might occur, but the severity of the event as well.”

The US tech giant announced its partnership with the Central Water Commission back in June. The two organizations agreed to share technical expertise and data to work on the predictions, with the Commission calling the collaboration a “milestone in flood management and in mitigating the flood losses.” Such warnings are particularly important in India, where 20 percent of the world’s flood-related fatalities are estimated to occur….(More)”.

How AI Addresses Unconscious Bias in the Talent Economy

Curated on Posted on by Stefaan Verhulst

Announcement by Bob Schultz at IBM: “The talent economy is one of the great outcomes of the digital era — and the ability to attract and develop the right talent has become a competitive advantage in most industries. According to a recent IBM study, which surveyed over 2,100 Chief Human Resource Officers, 33 percent of CHROs believe AI will revolutionize the way they do business over the next few years. In that same study, 65 percent of CEOs expect that people skills will have a strong impact on their businesses over the next several years. At IBM, we see AI as a tremendous untapped opportunity to transform the way companies attract, develop, and build the workforce for the decades ahead.

Consider this: The average hiring manager has hundreds of applicants a day for key positions and spends approximately six seconds on each resume. The ability to make the right decision without analytics and AI’s predictive abilities is limited and has the potential to create unconscious bias in hiring.

That is why today, I am pleased to announce the rollout of IBM Watson Recruitment’s Adverse Impact Analysis capability, which identifies instances of bias related to age, gender, race, education, or previous employer by assessing an organization’s historical hiring data and highlighting potential unconscious biases. This capability empowers HR professionals to take action against potentially biased hiring trends — and in the future, choose the most promising candidate based on the merit of their skills and experience alone. This announcement is part of IBM’s largest ever AI toolset release, tailor made for nine industries and professions where AI will play a transformational role….(More)”.

The role of corporations in addressing AI’s ethical dilemmas

Curated on Posted on by Stefaan Verhulst

Darrell M. West at Brookings: “In this paper, I examine five AI ethical dilemmas: weapons and military-related applications, law and border enforcement, government surveillance, issues of racial bias, and social credit systems. I discuss how technology companies are handling these issues and the importance of having principles and processes for addressing these concerns. I close by noting ways to strengthen ethics in AI-related corporate decisions.

Briefly, I argue it is important for firms to undertake several steps in order to ensure that AI ethics are taken seriously:

  1. Hire ethicists who work with corporate decisionmakers and software developers
  2. Develop a code of AI ethics that lays out how various issues will be handled
  3. Have an AI review board that regularly addresses corporate ethical questions
  4. Develop AI audit trails that show how various coding decisions have been made
  5. Implement AI training programs so staff operationalizes ethical considerations in their daily work, and
  6. Provide a means for remediation when AI solutions inflict harm or damages on people or organizations….(More)”.

We hold people with power to account. Why not algorithms?

Curated on Posted on by Stefaan Verhulst

Hannah Fry at the Guardian: “…But already in our hospitals, our schools, our shops, our courtrooms and our police stations, artificial intelligence is silently working behind the scenes, feeding on our data and making decisions on our behalf. Sure, this technology has the capacity for enormous social good – it can help us diagnose breast cancer, catch serial killers, avoid plane crashes and, as the health secretary, Matt Hancock, has proposed, potentially save lives using NHS data and genomics. Unless we know when to trust our own instincts over the output of a piece of software, however, it also brings the potential for disruption, injustice and unfairness.

If we permit flawed machines to make life-changing decisions on our behalf – by allowing them to pinpoint a murder suspect, to diagnose a condition or take over the wheel of a car – we have to think carefully about what happens when things go wrong…

I think it’s time we started treating machines as we would any other source of power. I would like to propose a system of regulation for algorithms, and perhaps a good place to start would be with Tony Benn’s five simple questions, designed for powerful people, but equally applicable to modern AI:

“What power have you got?
“Where did you get it from?
“In whose interests do you use it?
“To whom are you accountable?
“How do we get rid of you?”
Because, ultimately, we can’t just think of algorithms in isolation. We have to think of the failings of the people who design them – and the danger to those they are supposedly designed to serve.

Swarm AI Outperforms in Stanford Medical Study

Curated on Posted on by Stefaan Verhulst

Press Release: “Stanford University School of Medicine and Unanimous AI presented a new study today showing that a small group of doctors, connected by intelligence algorithms that enable them to work together as a “hive mind,” could achieve higher diagnostic accuracy than the individual doctors or machine learning algorithms alone.  The technology used is called Swarm AI and it empowers networked human groups to combine their individual insights in real-time, using AI algorithms to converge on optimal solutions.

As presented at the 2018 SIIM Conference on Machine Intelligence in Medical Imaging, the study tasked a group of experienced radiologists with diagnosing the presence of pneumonia in chest X-rays. This is one of the most widely performed imaging procedures in the US, with more than 1 million adults hospitalized with pneumonia each year. But, despite this prevalence, accurately diagnosing X-rays is highly challenging with significant variability across radiologists. This makes it both an optimal task for applying new AI technologies, and an important problem to solve for the medical community.

When generating diagnoses using Swarm AI technology, the average error rate was reduced by 33% compared to traditional diagnoses by individual practitioners.  This is an exciting result, showing the potential of AI technologies to amplify the accuracy of human practitioners while maintaining their direct participation in the diagnostic process.

Swarm AI technology was also compared to the state-of-the-art in automated diagnosis using software algorithms that do not employ human practitioners.  Currently, the best system in the world for the automated diagnosing of pneumonia from chest X-rays is the CheXNet system from Stanford University, which made headlines in 2017 by significantly outperforming individual practitioners using deep-learning derived algorithms.

The Swarm AI system, which combines real-time human insights with AI technology, was 22% more accurate in binary classification than the software-only CheXNet system.  In other words, by connecting a group of radiologists into a medical “hive mind”, the hybrid human-machine system was able to outperform individual human doctors as well as the state-of-the-art in deep-learning derived algorithms….(More)”.

Don’t forget people in the use of big data for development

Curated on Posted on by Stefaan Verhulst

Joshua Blumenstock at Nature: “Today, 95% of the global population has mobile-phone coverage, and the number of people who own a phone is rising fast (see ‘Dialling up’)1. Phones generate troves of personal data on billions of people, including those who live on a few dollars a day. So aid organizations, researchers and private companies are looking at ways in which this ‘data revolution’ could transform international development.

Some businesses are starting to make their data and tools available to those trying to solve humanitarian problems. The Earth-imaging company Planet in San Francisco, California, for example, makes its high-resolution satellite pictures freely available after natural disasters so that researchers and aid organizations can coordinate relief efforts. Meanwhile, organizations such as the World Bank and the United Nations are recruiting teams of data scientists to apply their skills in statistics and machine learning to challenges in international development.

But in the rush to find technological solutions to complex global problems there’s a danger of researchers and others being distracted by the technology and losing track of the key hardships and constraints that are unique to each local context. Designing data-enabled applications that work in the real world will require a slower approach that pays much more attention to the people behind the numbers…(More)”.