Methods for building fair predictors often involve tradeoffs between fairness and accuracy and between different fairness criteria, but the nature of these tradeoffs varies. Recent work seeks to characterize these tradeoffs in specific problem settings, but these methods often do not accommodate users who wish to improve the fairness of an existing benchmark model without sacrificing accuracy, or vice versa. These results are also typically restricted to observable accuracy and fairness criteria. We develop a flexible framework for fair ensemble learning that allows users to efficiently explore the fairness-accuracy space or to improve the fairness or accuracy of a benchmark model. Our framework can simultaneously target multiple observable or counterfactual fairness criteria, and it enables users to combine a large number of previously trained and newly trained predictors. We provide theoretical guarantees that our estimators converge at fast rates. We apply our method on both simulated and real data, with respect to both observable and counterfactual accuracy and fairness criteria. We show that, surprisingly, multiple unfairness measures can sometimes be minimized simultaneously with little impact on accuracy, relative to unconstrained predictors or existing benchmark models.

Look, we all know that algorithms are biased. What matters is how they are biased. What data helped train the algorithm? What weights and preferences did the data scientist ascribe to different features when designing the algorithm? As an end user, it's often impossible to know. But when we blame the algorithms, more often than not we're abdicating our basic responsibility to articulate and pursue a specific policy goal.

The Transform Technology Summits start October 13th with Low-Code/No Code: Enabling Enterprise Agility. This week, a piece from The Makeup uncovered biases in U.S. mortgage-approval algorithms that lead lenders to turn down people of color more often than white applicants. A decisioning model called Classic FICO didn't consider everyday payments -- like on-time rent and utility checks, among others -- and instead rewarded traditional credit, to which Black, Native American, Asian, and Latino Americans have less access than white Americans. The findings aren't revelatory: back in 2018, researchers at the University of California, Berkeley found that mortgage lenders charge higher interest rates to these borrowers compared to white borrowers with comparable credit scores. But they do point to the challenges in regulating companies that riskily embrace AI for decision-making, particularly in industries with the potential to inflict real-world harms.

In July, the California Department of Fair Employment and Housing sued video-game giant Activision Blizzard, alleging, more or less, that the company has a workplace environment from hell. Regulators said a two-year investigation into the company revealed an alcohol-drenched "frat boy" culture that included inappropriate conduct by executives, men openly joking about rape, and a general "breeding ground for harassment and discrimination against women." The company called the lawsuit "truly meritless and irresponsible" (though it seemed to have some trouble figuring out how to respond), and more than 2,000 current and former employees responded by putting their names on an open letter that said, "We no longer trust that our leaders will place employee safety above their own interests." In early August, employees shared their salaries en masse, Bloomberg reported, to pressure the company into confronting pay inequities. One executive, Blizzard head J. Allen Brack, resigned.

Priorly tech-friendly governments around the globe are now cracking down on big tech companies, which is putting the economies through a wind whirl. Since tech companies are forced to comply with the government, it gives the latter more power to manage their image. China has made good progress in the AI arms race in the last decade and is running almost in lockstep with the US. Recent research based on metrics such as patents and research publications ranked China as the top country for AI development, followed by the US and Japan. China's big-tech friendly policies were instrumental in putting the country on the AI map.

There is a fair bit of uncertainty over the impacts that AI will have on people and their jobs. Multiple future pathways beckon, different eventualities exist, and humans embody the ultimate wildcard: The freedom to choose. But in the assessment of AI expert Neil Sahota, significant changes are already baked into the equation, and there is little time to lose. Sahota has lots of eggs in lots of baskets. As the Chief Innovation Officer and a lecturer at the University of California Irvine School of Law, Sahota studies the intersection of AI and legal matters.

The Transform Technology Summits start October 13th with Low-Code/No Code: Enabling Enterprise Agility. Artificial intelligence (AI) and machine learning (ML) have become ubiquitous in our everyday lives. From self-driving cars to our social media feeds, AI has helped our world operate faster than it ever has, and that's a good thing -- for the most part. As these technologies integrate into our everyday lives, so too have the many questions around the ethics of using and creating these technologies. AI tools are models and algorithms that have been built on real-world data, so they reflect real-world injustices like racism, misogyny, and homophobia, along with many others.

As interest in A.I. patents grew and the definition of A.I. broadened in recent years, it's difficult to keep an accurate tab of every A.I. patent going through the system and track how the trend changes over time. To tackle that problem, the USPTO recently utilized its own A.I. capabilities to identify A.I.-related patents from as early as the 1970s in a sea of documents. Earlier this month, the USPTO's Office of the Chief Economist (OCE) released a pair of data files called the Artificial Intelligence Patent Dataset (AIPD), generated using a machine learning approach that analyzed patent text and citations in all U.S. patent documents recorded since 1976. One of the files identifies patents issued and pre-grant publications (PGPubs) published between 1976 and 2020 that contain one or more of eight A.I. technology components under the USPTO's definition. Those components include knowledge processing, speech, A.I. hardware, evolutionary computation, natural language processing, machine learning, vision and planning and control.

In the current era, people and society have grown increasingly reliant on artificial intelligence (AI) technologies. AI has the potential to drive us towards a future in which all of humanity flourishes. It also comes with substantial risks for oppression and calamity. Discussions about whether we should (re)trust AI have repeatedly emerged in recent years and in many quarters, including industry, academia, healthcare, services, and so on. Technologists and AI researchers have a responsibility to develop trustworthy AI systems. They have responded with great effort to design more responsible AI algorithms. However, existing technical solutions are narrow in scope and have been primarily directed towards algorithms for scoring or classification tasks, with an emphasis on fairness and unwanted bias. To build long-lasting trust between AI and human beings, we argue that the key is to think beyond algorithmic fairness and connect major aspects of AI that potentially cause AI’s indifferent behavior. In this survey, we provide a systematic framework of Socially Responsible AI Algorithms that aims to examine the subjects of AI indifference and the need for socially responsible AI algorithms, define the objectives, and introduce the means by which we may achieve these objectives. We further discuss how to leverage this framework to improve societal well-being through protection, information, and prevention/mitigation. This article appears in the special track on AI & Society.

USC's Pat Haden and now two "Varsity Blues" defendants want to file briefs in the college admissions scam case under seal. What they want to share, they argue, is "sensitive, confidential, and personally identifiable information." Haden, the former athletic director at the University of Southern California, has filed a motion in federal court in Boston to "quash a trial subpoena for testimony issued by counsel for defendants," as the Herald has reported. He was just granted permission to state his case in private. Defendants Gamal Abdelaziz and John Wilson are seeking that same protection to keep their arguments out of the public eye -- for now.