We consider the task of auditing ML models for individual bias/unfairness. We formalize the task in an optimization problem and develop a suite of inferential tools for the optimal value. Our tools permit us to obtain asymptotic confidence intervals and hypothesis tests that cover the target/control the Type I error rate exactly. To demonstrate the utility of our tools, we use them to reveal the gender and racial biases in Northpointe's COMPAS recidivism prediction instrument.

The Fairness, Accountability, and Transparency in Machine Learning (FAT-ML) literature proposes a varied set of group fairness metrics to measure discrimination against socio-demographic groups that are characterized by a protected feature, such as gender or race. Such a system can be deemed as either fair or unfair depending on the choice of the metric. Several metrics have been proposed, some of them incompatible with each other. We present here a framework to navigate the tensions between various group-wise metrics and to study fairness in data-driven decision making without the constraint of choosing a single metric. We do so empirically, by observing that several of these metrics cluster together in two or three main clusters for the same groups and machine learning methods. In addition, we propose a robust way to visualize multidimensional fairness in two dimensions through a Principal Component Analysis (PCA) of the group fairness metrics. Experimental results on multiple datasets show that the PCA decomposition explains the variance between the metrics with one to three components.

Machine learning using behavioral and text data can result in highly accurate prediction models, but these are often very difficult to interpret. Linear models require investigating thousands of coefficients, while the opaqueness of nonlinear models makes things even worse. Rule-extraction techniques have been proposed to combine the desired predictive behaviour of complex "black-box" models with explainability. However, rule-extraction in the context of ultra-high-dimensional and sparse data can be challenging, and has thus far received scant attention. Because of the sparsity and massive dimensionality, rule-extraction might fail in their primary explainability goal as the black-box model may need to be replaced by many rules, leaving the user again with an incomprehensible model. To address this problem, we develop and test a rule-extraction methodology based on higher-level, less-sparse "metafeatures". We empirically validate the quality of the rules in terms of fidelity, explanation stability and accuracy over a collection of data sets, and benchmark their performance against rules extracted using the original features. Our analysis points to key trade-offs between explainability, fidelity, accuracy, and stability that Machine Learning researchers and practitioners need to consider. Results indicate that the proposed metafeatures approach leads to better trade-offs between these, and is better able to mimic the black-box model. There is an average decrease of the loss in fidelity, accuracy, and stability from using metafeatures instead of the original fine-grained features by respectively 18.08%, 20.15% and 17.73%, all statistically significant at a 5% significance level. Metafeatures thus improve a key "cost of explainability", which we define as the loss in fidelity when replacing a black-box with an explainable model.

Sometimes it takes something unexpected to shift people's perspectives. That's what a group of MIT and Harvard Law School researchers were aiming for when they set out to reframe fairness in AI by studying its use on the powerful rather than the powerless. They presented the results of their research in January at the ACM Conference on Fairness, Accountability and Transparency in Barcelona. In the US, over half a million people are locked up despite not yet having been convicted or sentenced--a result of pretrial detention policies. Ninety-nine percent of the jail growth since 2002 has been in the pre-trial population, much of this because of an increased reliance on bail money, according to a report by the Prison Policy Initiative.

Foley Hoag is in the Seaport West building (155 Seaport Blvd, Boston, MA - main entrance is on B Street).

On International Women's Day, weapons development won't be the first thing that springs to mind for achieving global gender equality. But banning autonomous weapons systems AKA "killer robots" is needed to strengthen global peace, advance human security and ensure a feminist future. Technology could be a benevolent force in our increasingly integrated society. The potential benefits of innovative advancements in the fields of artificial intelligence, robotics, and machine learning could secure our future. As United Nations Secretary General Antonio Guterres said: "…these new capacities can help us to lift millions of people out of poverty, achieve the Sustainable Development Goals and enable developing countries to leap‑frog into a better future."

What do we need to consider about a future where artificial intelligence (AI) and tech have transformed the way we live? That was exactly what we pondered when I recently spoke with Jamie Susskind, barrister, speaker and award-winning author of Future Politics: Living Together in a World Transformed by Tech. Digitization is challenging the way we live. These changes create conveniences and ways of problem-solving that were never possible before. Along with the positives, there are also challenges that need to be overcome.

In the early 1970s, the fledgling credit card industry routinely and shortsightedly held cardholders liable for fraudulent transactions, even if their cards had been lost or stolen. In response, Congress passed the 1974 Fair Credit Billing Act to limit cardholder liability. This protection increased public trust in the new payment system and spurred growth and innovation. Because they could no longer just pass fraud losses on to cardholders, payment networks devised one of the first commercial applications of neural networks to detect out-of-pattern card usage and reduce their fraud losses. Smart regulation, like the above example, that gets out in front of emerging technology can protect consumers and drive innovation.

What do we need to consider about a future where artificial intelligence (AI) and tech have transformed the way we live? That was exactly what we pondered when I recently spoke with Jamie Susskind, barrister, speaker and award-winning author of Future Politics: Living Together in a World Transformed by Tech. Digitization is challenging the way we live. These changes create conveniences and ways of problem-solving that were never possible before. Along with the positives, there are also challenges that need to be overcome.

MLOps (from Machine Learning and Operations) refers to the process of managing the production lifecycle of Machine Learning models, including also the concept of collaboration between data scientists, data engineers and IT professionals. The objective is to define recommendations and best practices to automate the process, comply with regulatory requirements as well as provide agility to react to changing business requirements. Even though this procedure is mainly of technical nature, companies where MLOps practices are not implemented efficiently face also a number of business and financial challenges. In this blog post I would like to describe the findings and challenges due to inefficient MLOps we have encountered in several customer engagements and to describe how those challenges can be addressed with SAP Data Intelligence, hoping to provide guidance for others in similar situations. It is a common practice in data science teams to develop on local machines and distribute the developed models via shared drives or even email.