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Artificial algorithms are increasingly being deployed to inform, endorse, and govern various aspects of todays society. Their reach includes the domains of hiring, lending, medicine, criminal justice, insurance, allocation of public services, social and business interactions, and the dissemination of information and news. Through a synthesis of computational and statistical models for representing concepts, human-generated datasets that provide examples for training, and powerful optimization algorithms that can efficientlynavigate through vast and complex landscapes to infer concepts that explain data, such algorithms have taken big strides towards mimicking various aspects of natural intelligence.

These algorithms have led to tremendous economic and social impact but have also been shown to be biased they candiscriminate, reinforce prejudices, polarize opinions, and influence political processes. How can subjective human or societal biases emerge in the objective world of artificial algorithms? And how can we design algorithms free from these limitations?

The search for answers to these questions also leads us to some understanding of the bias in human decision-making algorithms.

Professor AleksanderMdry, who will lead the post-talk discussion on Monday, November 22, is theCadence Design Systems Professor of Computingin theMITElectrical Engineering and Computer Science Departmentand a member of the Computer Science and Artifical Intelligence Lab at MIT. Hereceived his Ph.D. fromMITin 2011 and prior to joining the universitys faculty, he spent a year as a postdoctoral researcher at Microsoft Research New England. He also was on the faculty ofEPFLuntil early 2015. ProfessorMdry is currently serving as theDirector of theMIT Center for Deployable Machine Learningand is the Faculty Lead of theCSAIL-MSR Trustworthy and Robust AI Collaboration. Hisresearch spans machine learning, optimization, and algorithmic graph theory, and he has a strong interest in building on the existing machine learning techniques to forge a decision-making toolkit that is reliable and well-understood enough to be safely and responsibly deployed in the real world.

Registerin advance for this webinar and the post-talk conversation:

Original post:

Bias in Algorithms | The Inference Project - Yale News