Featured Post from Georgia Tech College of Engineering

Meet Your Newest Job Recruiter, the Algorithm – let our experts explain

Sep 4, 2019

2 min

Swati Gupta

Equal employment opportunities may not be part of a computer’s calculations, but one engineer from is trying to change that.


When you apply for a job, chances are your resume has been through numerous automated screening processes powered by hiring algorithms before it lands in a recruiter’s hands. These algorithms look at things like work history, job title progression and education to weed out resumes. There are pros and cons to this – employers are eager to harness the artificial intelligence (AI) and big data captured by the algorithms to speed up the hiring process. But depending on the data used, automated hiring decisions can be very biased.


“Algorithms learn based on data sets, but the data is generated by humans who often exhibit implicit bias,” explains Swati Gupta, an industrial engineering researcher at Georgia Tech who’s work focuses on algorithmic fairness. “Our hope is that we can use machine learning with rigorous mathematical analysis to fix the bias in areas like hiring, lending and school admissions.”


But as algorithms harness speed and efficiency – how can they be adjusted to include and consider race, gender and other human factors?  It’s an area Dr. Gupta has been researching and refining. If you are a reporter or journalist looking to cover this topic – that’s where our experts can help.


Dr. Swati Gupta is an Assistant Professor in the H. Milton Stewart School of Industrial and Systems Engineering at Georgia Tech. Dr. Gupta is an expert in the areas of optimization, machine learning, and bias and fairness within the AI sphere. She is available to speak with media regarding this topic - simply click on her icon to arrange an interview.



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Swati Gupta

Swati Gupta

Assistant Professor, Industrial and Systems Engineering

Gupta's research focuses on optimization, machine learning, and bias and fairness within the AI sphere.

Data-driven Decision-making Under Partial InformationOnline and Machine LearningCombinatorial, Convex and Robust OptimizationFairness and Bias in Decisions

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