The application of big data analytics has already improved lives in innumerable ways. It has improved the way teachers instruct students, doctors diagnose and treat patients, lenders find creditworthy customers, financial service companies control money laundering and terrorist financing, and governments deliver services. It promises even more transformative benefits with self-driving cars and smart cities, and a host of other applications will drive fundamental improvements throughout society and the economy. Government policymakers have worked with developers and users of these advanced analytic techniques to promote and protect these publicly beneficial innovations, and they should continue to do so.
In many circumstances, current law and regulation provide an adequate framework for strong public protection. Most of the legal concerns that animate public discussions can be resolved through strong and vigorous enforcement of rules that apply to advanced and tradi ...
Institutions involved in predictive modeling are using ever more advanced techniques to predict outcomes of interest from credit scoring to facial recognition to spam detection. Institutions assess the performance of these models through standard measures such as accuracy (the number of correct predictions divided by the total number of predictions) or error rate (the number of incorrect predictions divided by the total number of predictions). They can in addition assess the fairness of their predictions with respect to vulnerable groups using measures such as predictive parity across groups, statistical parity, or equal error rates.
Institutions also face legal and ethical obligations to explain the basis of their consequential decisions to those who are affected, to regulators and to the general public. The idea is that people have rights based on autonomy and dignity to be able to understand why institutions make the decisions they do. When predictive models are ...
Suicide is ranked as the third leading cause of death among people ages 10 to 14 and second among people ages 15 to 24 according to the Center for Disease Control and Prevention. Obviously, suicide and depression are a serious problem facing society. People who are contemplating suicide often feel helpless and reach out, but hearing and acting on cries for help doesn’t always happen in time. Tragically, many people who have struggled with depression and/or suicidal thoughts have used social media to post notes about their intentions to take their own lives or even live stream their suicides.
In response, Facebook announced a few months ago that it would be taking more initiative in using its platform for social good. One of Facebook’s tools to aide with suicide prevention is artificial intelligence.
Facebook has developed algorithms that recognize patterns in user’s posts to flag them in case they are at risk of committing suicide. Critics hav ...
There’s good news from the scholarly community working on the assessment of fairness in algorithms. Computer scientists and statisticians are developing a host of new measures of fairness aimed at providing companies, policymakers, advocates with new tools to assess fairness in different contexts.
The essential insight of the movement is that the field needs many different measures of fairness to capture the variety of normative concepts that are used in different business and legal contexts. Alexandra Chouldechova, Assistant Professor of Statistics and Public Policy at Heinz College, says “There is no single notion of fairness that will work for every decision context or for every goal.”
To find the right measure for the job at hand, she advises, “Start with the context in which you’re going to apply [your decision], and work backwards from there.”
This issue came to a head in the controversy surrounding the COMPAS score. This s ...
The public must be confident in the fairness of algorithms, or a backlash will threaten their very real and substantial benefits.
A recent article in the New York Times raises the question: do algorithms discriminate? The question is legitimate, but the emphasis is wrong. Instead of thinking of data analytics as a problem, we need to welcome the new opportunities for improved decisionmaking that they enable. And we need to cooperate to identify and address any disparate impacts data-driven decisionmaking might have.