Under: Data-Driven Innovation
Yesterday I had the opportunity to attend a screening of 21st Century Fox’s “Hidden Figures.” This is a great movie about the critical contributions made by three African-American women – Katherine Johnson, Dorothy Vaughn, and Mary Jackson - to put John Glenn into space in the early 1960s. The movie depicts the struggles these women faced to be treated equally as the consummate professionals they were at a time when the state of Virginia still enforced segregation laws. It is a wonderful and uplifting story about a mostly unexplored but important dimension of American history. Go see it!
There is an interesting sub-plot to the movie, which has to do with the usually somewhat dry – at least on the big screen - topic of automation and jobs. Johnson, Vaughn and Jackson were hired by NASA to be human “computers.” Part of Johnson’s job was to calculate John Glenn’s exact landing zone in ...
The labor economist David Autor opens a recent TED talk with a startling fact: in the 45 years since the introduction of the Automatic Teller Machine, bank teller jobs have roughly doubled, from a quarter of a million to half a million. Since 2000, financial institutions have created 100,000 new bank teller jobs.
This was first published as an InfoWorld IDG Contributor Network Tech Policy Perspectives column.
Occupations accounting for 47% of all U.S. employment are at risk of computerization, and this isn’t decades away: It could happen over the next 10 to 20 years.
Recent technological developments have led to the rise of “big data” analytics which include machine learning and artificial intelligence. These new technologies will without question provide ample opportunity for growth for consumers, businesses, and the global economy as a whole. As this technological evolution continues to take place, it does not come without some risk.
Over the last few years, algorithmic fairness, has become an issue of serious debate. Most recently, Cathy O’Neil released a book titled, “Weapons of Math Destruction,” and Frank Pasquale published “The Black Box Society,” in which they look at issues of discrimination and the role that algorithms play in exacerbating discrimination. SIIA responded to these works in a blog by saying that tech leaders must quickly act to ensure algorithmic fairness.
To go even further, on Friday, November 04, 2016, SIIA released an issue brief on the topic ...
Yesterday on October 10, 2016 in Berlin, the Aspen Institute/Germany launched “Into the Clouds: European SMEs and the Digital Age.” SIIA and Thomson Reuters supported the report, which was written by the Atlantic Council’s Tyson Barker. In connection with the launch, Aspen hosted a lively lunch discussion bringing together academics, politicians, and industry representatives.
The report finds lower than optimal cloud adoption rates in a number of European countries, most notably in Germany. It recommends six policies in order to increase cloud adoption, especially by small and medium sized enterprises (SMEs).
This week, SIIA published an issue brief assessing the use of data analytics in the criminal justice system. Not surprisingly, data analytics has helped to reduce crime and improve the criminal justice system, particularly through its application in predictive policing and criminal risk assessment. The report also explores critical questions and concerns raised about the effectiveness and unintended outcomes of the various tools in use today.
This report is timely, as it coincides with a critical decision handed down by the Wisconsin Supreme Court about the use of evidence-based risk assessment tools at sentencing. The Court supported the use of predictive tools, such as the COMPAS tool at the center of the trial, but it ruled that “risk scores may not be considered as the determinative factor in deciding whether the offender can be supervised safely and effectively in the community.” [emphasis added]
Essentially, the court’s decision confirme ...
After a failed attempt to improve their national healthcare website in 2010, the UK’s Government Digital Service (GDS) was formed in 2011 as an agency under the Cabinet Office responsible for the government’s single website, building new digital services, and tracking how people use government services in an effort to improve them. In five years, the agency expanded exponentially and now employs over 700 workers.
At the same time, 18F is facing many similar questions. At a recent House hearing, the Government Accountability Office (GAO) highlighted a series of concerns that range from a lack of outcome-oriented goals and performance measures to the cost-recovery capability of 18F’s programs.
On Friday, June 17th, SIIA hosted a panel that was co-sponsored by the Congressional High Tech Caucus and the Congressional Internet of Things Caucus on assessing the benefits, challenges, and policy implications of the Internet of Things.
SIIA has been active on the topic of IoT in recent months both filing comments to NTIA and releasing a white paper on the subject. David LeDuc, SIIA’s Senior Director for Public Policy gave opening remarks where he said SIIA defines the “Internet of Things” as ubiquitous connectivity where people are not only interacting with their devices, but devices are also interacting with each other. He also touched on the importance of regulatory humility cautioning against an overarching policy framework for IoT to accommodate IoT’s complex ecosystem.
The panel consisted of representatives from GE Digital, Qualcomm Inc., and the Center for Data Innovation. Each panelist touched on both public and private sector oppor ...
Two years ago, SIIA published a study on the economic effects of the software industry. The study found:
In the financial market, lenders such as banks and credit card companies use credit scores to assess the potential risk of issuing a loan to an individual, in an attempt to mitigate losses. In essence, a credit score determines the credit worthiness of an individual. As a result, an individual’s credit score can significantly impact his/her ability to obtain a loan.
To derive credit scores, credit scoring systems like FICO use complex and advanced algorithms that are evaluated and developed regularly. In fact, over 90% of all consumer lending decisions use the FICO score. Tweaking and double-checking the algorithms are essential as economic conditions change a consumer’s ability to repay loans and laws change the way in which consumer’s repay loans. These FICO scores range from 300-850; borrowers with lower FICO scores have to finance loans at higher interest rates. To control the risk that borrowers might not repay their loans, lenders divide borrowers into tiers based on this score. So, it is imperative to calculate credit scores accurately for both the lender and borrower in a financial transaction.