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Wednesday, August 19, 2015

Programming and prejudice: Computer scientists discover how to find bias in algorithms

Software may appear to operate without bias because it strictly uses computer code to reach conclusions. That's why many companies use algorithms to help weed out job applicants when hiring for a new position.
But a team of computer scientists from the University of Utah, University of Arizona and Haverford College in Pennsylvania have discovered a way to find out if an algorithm used for hiring decisions, loan approvals and comparably weighty tasks could be biased like a human being.
The researchers, led by Suresh Venkatasubramanian, an associate professor in the University of Utah's School of Computing, have discovered a technique to determine if such software programs discriminate unintentionally and violate the legal standards for fair access to employment, housing and other opportunities. The team also has determined a method to fix these potentially troubled algorithms.
Venkatasubramanian presented his findings Aug. 12 at the 21st Association for Computing Machinery's Conference on Knowledge Discovery and Data Mining in Sydney, Australia.
"There's a growing industry around doing resume filtering and resume scanning to look for job applicants, so there is definitely interest in this," says Venkatasubramanian. "If there are structural aspects of the testing process that would discriminate against one community just because of the nature of that community, that is unfair."
Machine-learning algorithms
Many companies have been using algorithms in software programs to help filter out job applicants in the hiring process, typically because it can be overwhelming to sort through the applications manually if many apply for the same job. A program can do that instead by scanning resumes and searching for keywords or numbers (such as school grade point averages) and then assigning an overall score to the applicant.
These programs also can learn as they analyze more data. Known as machine-learning algorithms, they can change and adapt like humans so they can better predict outcomes. Amazon uses similar algorithms so they can learn the buying habits of customers or more accurately target ads, and Netflix uses them so they can learn the movie tastes of users when recommending new viewing choices.
But there has been a growing debate on whether machine-learning algorithms can introduce unintentional bias much like humans do.
"The irony is that the more we design artificial intelligence technology that successfully mimics humans, the more that A.I. is learning in a way that we do, with all of our biases and limitations," Venkatasubramanian says.
Disparate impact
Venkatasubramanian's research determines if these software algorithms can be biased through the legal definition of disparate impact, a theory in U.S. anti-discrimination law that says a policy may be considered discriminatory if it has an adverse impact on any group based on race, religion, gender, sexual orientation or other protected status.
Venkatasubramanian's research revealed that you can use a test to determine if the algorithm in question is possibly biased. If the test -- which ironically uses another machine-learning algorithm -- can accurately predict a person's race or gender based on the data being analyzed, even though race or gender is hidden from the data, then there is a potential problem for bias based on the definition of disparate impact.
"I'm not saying it's doing it, but I'm saying there is at least a potential for there to be a problem," Venkatasubramanian says.
If the test reveals a possible problem, Venkatasubramanian says it's easy to fix. All you have to do is redistribute the data that is being analyzed -- say the information of the job applicants -- so it will prevent the algorithm from seeing the information that can be used to create the bias.
"It would be ambitious and wonderful if what we did directly fed into better ways of doing hiring practices. But right now it's a proof of concept," Venkatasubramanian says.

University of Utah. "Programming and prejudice: Computer scientists discover how to find bias in algorithms." ScienceDaily. ScienceDaily, 14 August 2015. <>.

New Report Casts Blame for Widespread Cyberattacks on Iranian Hackers

A new report contends Iranian hackers stole confidential information from government agencies and major companies in 16 countries during at least the last two years. Security vendor Cylance says the ongoing attacks, which it calls  “Operation Cleaver," stole documents and wrested control of computer networks of organizations located in nations including Canada, China, India, Israel, Mexico, Pakistan, South Korea, Turkey, the United Arab Emirates, and the US. The organizations were in the military, energy, transportation, telecommunications, technology, and other industry sectors. Cylance says it has evidence these intrusions were made by the same Iran-based group responsible for a 2013 attack on the US Navy computer network. Hamid Babaei, spokesperson for Iran's mission to the United Nations, said these claims are “a baseless and unfounded allegation fabricated to tarnish the Iranian government image, particularly aimed at hampering current nuclear talks.”. According to Cylance’s report, the hackers used a combination of off-the-shelf and custom tools to infiltrate target computer systems. “We discovered the scope and damage of these operations during investigations of what we thought were separate cases,” said company CEO Stuart McClure. “Due to the choice of critical infrastructure victims and the Iranian team’s quickly improving skillset, we are compelled to publish this report.” Although based in Tehran, the company said, the hackers receive help from people in Canada, the Netherlands, and the UK. Cylance said it has traced the attacks to June 2012, although they may have begun as early as 2010. Cylance shared its findings with the victims and the US Federal Bureau of Investigation. (PC Mag)(USA Today)(Reuters)