One of the most important issues that Congress will face in 2018 is how and when to regulate our growing dependence on artificial intelligence (AI). During the U.S. National Governors Association summer meetings, Elon Musk urged the group to push forward with regulation "before it's too late," stating that AI was an "existential threat to humanity." Hyperbole aside, there are legitimate concerns about the technology and its use. But a rush to regulation could exacerbate current issues, or create new issues that we're not prepared to deal with along the way. More specifically, the solution to the issues posed by AI -- described by Musk and by others -- doesn't lie in AI itself, but the data it uses.

PREMIUM The debate regarding the use of artificial intelligence (AI) in modern life continues to divide opinion. On one hand, many believe that automation through AI will increase productivity and competitiveness in the business industry.

PREMIUM The debate regarding the use of artificial intelligence (AI) in modern life continues to divide opinion. On one hand, many believe that automation through AI will increase productivity and competitiveness in the business industry.

Recently, David Bader visited India to give a keynote talk at IEEE International Conference on Machine Learning and Data Science at Bennett University, Greater Noida. David A. Bader is Professor and Chair of the School of Computational Science and Engineering, College of Computing, at Georgia Institute of Technology. He is a fellow of the IEEE and AAAS and served on the White House's National Strategic Computing Initiative (NSCI) panel. He was in conversation with Prof. Deepak Garg, Chair, of Computer Science Engineering at Bennett University. Question: Big data and data analytics have made a huge impact on businesses in 2017, with trends like artificial intelligence and cloud services being used for their advantage.

To the editor: I read "Do L.A. Unified's daily random searches keep students safe, or do they go too far?" and shuddered. Los Angeles Unified School District officials should be ashamed and embarrassed over the dehumanizing way they treat students. How can anyone connected with this fiasco condone such an assault on their students' personhood or create such a threatening environment so adverse to learning? Parents and students have complained, community groups have protested, the American Civil Liberties Union has become involved, but nothing changes. Perhaps now with the curtain pulled back for the broader public to see, this mean-spirited breach of trust in the name of safety will be stopped.

The end of a politician's time in office often inspires a turn toward the existential, but few causes are as quixotic as the one chosen by James Vacca, who this month hits his three-term limit as a New York City Council member, representing the East Bronx. Vacca's nearly four decades in local government could well be defined by a bill that he introduced in August, and that passed last Monday by a unanimous vote. Once signed into law by Mayor Bill de Blasio, the legislation will establish a task force to examine the city's "automated decision systems"--the computerized algorithms that guide the allocation of everything from police officers and firehouses to public housing and food stamps--with an eye toward making them fairer and more open to scrutiny. In mid-October, I and some of my colleagues from a group at Cornell Tech that works on algorithmic accountability attended a hearing of the Council's technology committee to offer testimony on the bill. As Vacca, who chairs the committee, declared at the time, "If we're going to be governed by machines and algorithms and data, well, they better be transparent."

If ever there was an industry ripe for disruption it is surely the legal profession. Unlike many other sectors, however, it has tended to be a little reticent about embracing technology to innovate. After all, the traditional way of doing business for legal firms has been extremely profitable. The model typically involves a bunch of low-paid minions doing grunt work while a few partners earn eye-wateringly high sums. Moreover, many legal professionals look upon technology with fear and who could blame them when a forecast from Deloitte published last year predicted that more than 100,000 jobs in the sector could be automated within the next 20 years.

Recent work on fairness in machine learning has focused on various statistical discrimination criteria and how they trade off. Most of these criteria are observational: They depend only on the joint distribution of predictor, protected attribute, features, and outcome. While convenient to work with, observational criteria have severe inherent limitations that prevent them from resolving matters of fairness conclusively. Going beyond observational criteria, we frame the problem of discrimination based on protected attributes in the language of causal reasoning. This viewpoint shifts attention from "What is the right fairness criterion?" to "What do we want to assume about our model of the causal data generating process?" Through the lens of causality, we make several contributions. First, we crisply articulate why and when observational criteria fail, thus formalizing what was before a matter of opinion. Second, our approach exposes previously ignored subtleties and why they are fundamental to the problem. Finally, we put forward natural causal non-discrimination criteria and develop algorithms that satisfy them.

Equipping machine learning models with ethical and legal constraints is a serious issue; without this, the future of machine learning is at risk. This paper takes a step forward in this direction and focuses on ensuring machine learning models deliver fair decisions. In legal scholarships, the notion of fairness itself is evolving and multi-faceted. We set an overarching goal to develop a unified machine learning framework that is able to handle any definitions of fairness, their combinations, and also new definitions that might be stipulated in the future. To achieve our goal, we recycle two well-established machine learning techniques, privileged learning and distribution matching, and harmonize them for satisfying multi-faceted fairness definitions. We consider protected characteristics such as race and gender as privileged information that is available at training but not at test time; this accelerates model training and delivers fairness through unawareness. Further, we cast demographic parity, equalized odds, and equality of opportunity as a classical two-sample problem of conditional distributions, which can be solved in a general form by using distance measures in Hilbert Space. We show several existing models are special cases of ours. Finally, we advocate returning the Pareto frontier of multi-objective minimization of error and unfairness in predictions. This will facilitate decision makers to select an operating point and to be accountable for it.

Machine learning is now being used to make crucial decisions about people's lives. For nearly all of these decisions there is a risk that individuals of a certain race, gender, sexual orientation, or any other subpopulation are unfairly discriminated against. Our recent method has demonstrated how to use techniques from counterfactual inference to make predictions fair across different subpopulations. This method requires that one provides the causal model that generated the data at hand. In general, validating all causal implications of the model is not possible without further assumptions. Hence, it is desirable to integrate competing causal models to provide counterfactually fair decisions, regardless of which causal "world" is the correct one. In this paper, we show how it is possible to make predictions that are approximately fair with respect to multiple possible causal models at once, thus mitigating the problem of exact causal specification. We frame the goal of learning a fair classifier as an optimization problem with fairness constraints entailed by competing causal explanations. We show how this optimization problem can be efficiently solved using gradient-based methods. We demonstrate the flexibility of our model on two real-world fair classification problems. We show that our model can seamlessly balance fairness in multiple worlds with prediction accuracy.