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 SIGKDD Conference on Knowledge Discovery and Data Mining in Sydney, Australia.
“There’s a growing industry around doing résumé filtering and résumé 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.”
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 résumés 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.
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.
Read more: PROGRAMMING AND PREJUDICE
Haverford College (/ˈhævərfərd/ ha-vər-fərd) is a private, coeducational liberal arts college located in Haverford, Pennsylvania, United States, a suburb of Philadelphia.
All students of the College are undergraduates, and nearly all reside on campus.
The college was founded in 1833 by area members of the Orthodox Philadelphia Yearly Meeting of the Religious Society of Friends (Quakers) to ensure an education grounded in Quaker values for young Quaker men. Although the college no longer has a formal religious affiliation, the Quaker philosophy still influences campus life. Originally an all-male institution, Haverford began admitting female transfer students in the 1970s and became fully co-educational in 1980. Currently, more than half of Haverford’s students are women. For most of the 20th century, Haverford’s total enrollment was kept below 300, but the school went through two periods of expansion after the 1970s, and its current enrollment is 1,190 students.
Haverford is a member of the Tri-College Consortium, which allows students to register for courses at both Bryn Mawr College and Swarthmore College. It is also a member of the Quaker Consortium (“Penn-Pal”) which allows students to cross-register at the College of General Studies and the Wharton School of Business at the University of Pennsylvania.
The college has produced 67 Fulbright Scholars, 62 Watson Fellows, 24 Goldwater Scholars, 20 Rhodes Scholars, 18 Guggenheim Fellows, 4 MacArthur Fellows, and 3 Nobel Prize Recipients.