data analytics

Confronting and combatting algorithm bias at CoNECD conference

Anonymous — Wed, 24 Mar 2021 12:19:31 +0000

Confronting and combatting algorithm bias at CoNECD conference Anonymous (not verified) Wed, 03/24/2021 - 08:19

In This Story

Algorithms help us make hard decisions every day. Credit card companies, job boards, and more use fast-thinking algorithms to fairly decipher who fits their chosen criteria. But sometimes, they aren’t always as fair as they appear.

At the annual Collaborative Network for Engineering and Computing Diversity (CoNECD) conference in January, the Director of the MS Data Analytics Engineering program, James Baldo, presented how algorithm biases arise and where data analysts and algorithm creators could make changes to increase fairness.

“I looked at algorithm bias from a high-level technical perspective to show the audience that yes, algorithms can be biased, but that there is more to it than a yes or no analysis,” says Baldo.

There are numerous aspects of algorithms that could hold bias. The data used for the algorithm, the core of the algorithm itself, and even the people interpreting the algorithm’s data could be where bias sneaks into the decision-making process, says Baldo.

“Algorithms use artificial intelligence and are designed by computational data that may have an inherent and unintentional bias,” he says. “Employment decisions are a good example. If an algorithm is using a sample of data to sift through applicants for a software engineering job that doesn’t include many women, the algorithm could unintentionally sift women out.”

Baldo says there have been numerous studies on algorithm bias. Still, he shared his thoughts at the conference because he felt it was important for conference attendees to understand algorithm bias’s root causes.

“One slide I presented discussed how we achieve fairness with algorithms. We need to look at the data and try and detect biases in it. We can train people who interpret the data and educate them on the best practices,” says Baldo. “I mainly wanted my presentation to raise awareness, and I wanted to take some of the mystery out of algorithms.”

From a data perspective, Baldo works to embed these best practices into the data analytics engineering program. “We are trying to embed education on the effects of algorithm bias into the MS program. Since it is an interdisciplinary program, we have to work together to figure out how to do that collaboratively.”

Baldo sees algorithm bias awareness and prevention as crucial for building the data analytics workforce. “The social fabric of this is very important, and we have a responsibility as engineers to address it, and this was a start.”

Topics

Data Analytics Engineering

data analytics

algorithm bias

Mason offers new Data Analytics Credential to prepare students for careers in big data

Anonymous — Thu, 11 Mar 2021 16:47:33 +0000

Mason offers new Data Analytics Credential to prepare students for careers in big data Anonymous (not verified) Thu, 03/11/2021 - 11:47

In This Story

Liza Wilson Durant

Brett Hunter

George Mason University is offering a new Data Analytics Credential to help undergraduates hone their skills in handling big data.

The new credential is the second one offered in partnership with the Greater Washington Partnership’s Capital CoLAB. Mason launched the Digital Technology Credential in 2019 to support additional skills in data analysis, visualization, and cybersecurity for non-engineers. More than 150 students are currently enrolled in that program.

“The new Data Analytics Credential will enable hundreds of Mason students to offer their specialized data analytics skills to employers who are seeking to meet the talent shortfall in data science and analytics,” says Liza Wilson Durant, associate dean for strategic initiatives and community engagement for the Volgenau School of Engineering.

Liza Wilson Durant says the new credential is an exciting showcase of our industry partners engaging with students from multiple disciplines. Photo by Evan Cantwell.

The credential focuses on data storage and management and will make students more marketable, including for jobs as data analysts and data scientists, says Brett Hunter, associate chair of the Department of Statistics.

The credential is designed for undergraduates in statistics, computing, information technology, and data science who want to acquire the data analytics skills needed by high-profile employers in the metropolitan areas of Baltimore, Washington, D.C., and Richmond.

Developed with the Greater Washington Partnership’s Capital CoLAB, the credential equips students across disciplines with the specialized data analytics skills that the Greater Washington Partnership employers have specified to be most important to their operations. The Capital CoLAB (Collaborative of Leaders in Academia and Business) is an action-oriented partnership of employers and academic institutions that executes initiatives to develop the talent needed for the jobs of today and tomorrow.

By enrolling in either the Digital Technology Credential or Data Analytics Credential programs, students will have direct access to opportunities and engagement with some of the largest employers in the region, including Amazon, Capital One, and Northrop Grumman.

“It is exciting to see our industry partners directly engaged with our students and reinforcing the demand for the skills they are acquiring by offering them internships and other experiential learning opportunities,” says Durant.

Many students in statistics, computational data science, computer science, and information science and technology will only need to take one to three additional undergraduate courses to earn the credential, Hunter says. They can use some of their elective courses to do that.

“The credential is not another degree, but essentially a badge you can put on your LinkedIn profile or electronic resume,” he says. “I think most statistics undergraduates will take advantage of the opportunity to communicate their skills in this new way.”

The badge associated with the Data Analytics Credential also represents an innovation for students and employers. “Micro-credentials like the Data Analytics badge reflect the ‘new currency’ in denoting skill achievement and helps our students move more easily from college to career,” says Marc Austin, Executive Director of Professional Development and Academic Ventures who leads Mason’s Continuing and Professional Development unit which administers the new digital badge.

More than 50 students have already enrolled in the new Data Analytics Credential program.

Students who are working towards earning the credential will receive several exclusive benefits from area companies, including:

Access to a student portal with paid internship and event opportunities.
Invitations to participate in annual internship fairs with employers looking to recruit students.
Access to professional development webinars and other ad-hoc opportunities.

Mason students can learn more and enroll in the new Data Analytics Credential here.

Topics

data analytics

Digital Technology Credential Program

Biostatistics

Big Data

Daniel Carr

admin_alpha — Tue, 20 Oct 2015 23:24:01 +0000

Daniel Carr admin_alpha Tue, 10/20/2015 - 19:24

Professor Emeritus, Statistics

Contact Information

Email: dcarr@gmu.edu

Personal Websites

Professional Webpage

Biography

Professor Daniel Carr is known for his contributions to the field of statistical graphics. These contributions include the creation of new graphics designs and dynamic visualization software. The challenges that motivated the designs have come from a variety of fields such as genomics, proteomics, life sciences, and environmental monitoring.

Carr’s statistics career started in 1976 at Battelle Pacific Northwest National Laboratories. At first, he was involved in the analysis of animal experiments, calibration of measurement devices, and modeling of monitoring data. In the 1980s, he worked with a small team of statisticians, computer scientists, and computer engineers on the Analysis of Large Data Sets project. The research publications and presentations brought visibility.

Carr was appointed chair-elect when the Statistical Graphics Section of the American Statistical Association was created in 1986. He continued to serve the section for many years in roles such as Program Chair and editor of the graphics section in the Statistical Computing and Statistical Graphics newsletter. Carr was elected ASA Fellow in 1992.

After joining Mason in 1990, Carr interacted with visionary colleagues and researchers at federal agencies. A 1992 collaborative NSF proposal yielded a virtual reality laboratory in the Center for Computational Statistics. On sabbatical at the National Cancer Institute, he was a member of a team that developed the usability-assessed State Cancer Control Profiles website. Team members received NIH merit awards for their contributions to cancer control planning. Carr has created and taught statistical graphics and visualization courses at the 400 to 800 levels, served on numerous dissertation committees and seen 11 of his PhD students graduate.

Research

2014 - 2015: Uncertainty in Spatial Date: Identification Visualization and Utilization. Funded by the University of Texas at Dallas.

2008 - 2010: Curriculum Design in Data Sciences. Funded by National Science Foundation.

2005 - 2008: Visual Analytic Tools for Networks and Graphs with Attention Given to Data Synthesis and Evaluation. Funded by Office of Naval Research.

Research Interests

Data mining
Statistical graphics
Knowledge visualization
Data exploration
Analysis of massive data sets

Degrees

PhD, Statistics, University of Wisconsin-Madison
MS, Statistics, Oregon State University
MEd, Counselling, Idaho State University
BA, Mathematics, Whitman College