Algorithmic fairness is becoming increasingly important in data mining and machine learning, and one of the most fundamental notions is group fairness. The vast majority of the existing works on group fairness, with a few exceptions, primarily focus on debiasing with respect to a single sensitive attribute, despite the fact that the co-existence of multiple sensitive attributes (e.g., gender, race, marital status, etc.) in the real-world is commonplace. As such, methods that can ensure a fair learning outcome with respect to all sensitive attributes of concern simultaneously need to be developed. In this paper, we study multi-group fairness in machine learning (MultiFair), where statistical parity, a representative group fairness measure, is guaranteed among demographic groups formed by multiple sensitive attributes of interest. We formulate it as a mutual information minimization problem and propose a generic end-to-end algorithmic framework to solve it. The key idea is to leverage a variational representation of mutual information, which considers the variational distribution between learning outcomes and sensitive attributes, as well as the density ratio between the variational and the original distributions. Our proposed framework is generalizable to many different settings, including other statistical notions of fairness, and could handle any type of learning task equipped with a gradient-based optimizer. Empirical evaluations in the fair classification task on three real-world datasets demonstrate that our proposed framework can effectively debias the classification results with minimal impact to the classification accuracy.

A famous example would be Amazon's AI recruitment screening program. They were forced to abandon it due to gender discrimination. Their machine learning model had been trained to recognize the patterns and similarities from ten years of resumes submitted to Amazon. Many of these resumes came from men, since the technology field has been traditionally male dominated. The AI tool'learned' to reject females based upon this historical data.

… consider future-facing investments for the even higher performing machines that … computing, artificial intelligence, and scientific machine learning.”.

For the past couple of years, renowned technologist and researcher Bruce Schneier has been researching how societal systems can be hacked, specifically the rules of financial markets, laws, and the tax code. That led him to his latest examination of the potential unintended consequences of artificial intelligence on society: how AI systems themselves, which he refers to as "AIs," could evolve such that they automatically - and inadvertently - actually abuse societal systems. "It's AIs as the hacker," he says, rather than hackers hacking AI systems. Schneier will discuss his AI hacker research in a keynote address on Monday at the 2021 RSA Conference, which, due to the pandemic, is being held online rather than in person in San Francisco. The AI topic is based on a recent essay he wrote for the Cyber Project and Council for the Responsible Use of AI at the Belfer Center for Science and International Affairs at Harvard Kennedy School.

A young man exiled to a reeducation camp for the "digitally unsafe" learns to keep his face blank, as cameras everywhere read expressions, and signs of anger and resistance are quickly punished. The elderly victim of an attack feels empty after winning justice from a "panel of metal judges" in a future courtroom beyond human biases. An online karate class is taught by artificial intelligence and robots, but over the decades, even as the sport thrives, much of its crucial human element is forgotten. These tales of AI and its effects on future life -- and many more, from points around the world -- are gathered in "Telling Stories: On Culturally Responsive Artificial Intelligence," presented by the University of Washington Tech Policy Lab. The lab is an interdisciplinary collaboration of the UW Paul G. Allen School of Computer Science & Engineering, Information School and School of Law, to "enhance technology policy through research, education and thoughtful leadership."

Advancements in artificial intelligence have enabled a number of technological solutions to emerge in the construction industry with the potential to improve worksite efficiency, data quality, and overall innovation. Early adoption of such technologies has inherent operational and competitive benefits, though legal risks must be evaluated and addressed prior to implementation. This article provides a deep dive into the legal implications of Artificial Intelligence and how attorneys in this discipline can prepare for the risks their clients may face. Artificial intelligence (AI) generally refers to technology that uses algorithms to process data and simulate human intelligence. Examples of AI technology include machine learning, image recognition and sensors-on-site, building information modeling (BIM), and "smart contracts" stored on a blockchain-based platform. This technology can be used in the construction industry by way of design, operations and asset management, and construction itself.

In February 2021 Travers Smith and techUK led a webinar which, amongst other issues, examined how the growing need to provide more transparency and possibly to formally report on the use of AI and algorithms could lead to the development of an effective audit and assurance framework. Since then, the EU has published its proposals, in April 2021, for a regulation to harmonise the rules on artificial intelligence across EU member states. "The draft AI regulation published by the European Union last week is significant because it's the first of its kind in the world – a comprehensive, cross-sectoral, supranational attempt to regulate artificial intelligence (AI) and algorithmic products across a range of'high-risk' sectors. While only a week old, the Commission's proposal has already achieved an impressive feat: it has shifted the policy window away from a conversation about whether to regulate artificial intelligence, opening up a new discourse about how to regulate artificial intelligence." In addition, in April 2021, the US Federal Trade Commission published a blog on "Aiming for truth, fairness, and equity in your company's use of AI" and the new Biden White House Administration has signalled its intent to take a lead on the global debate around AI, with a particular focus on not being outflanked by China – as laid out in the March 2021 final report of the US National Security Commission on AI.

On May 10, 40 advocacy groups sent an open letter demanding a permanent ban on the use of Amazon's facial recognition software, Rekognition, by US police. The letter was addressed to Jeff Bezos and Andy Jassy, the company's current and incoming CEOs, and came just weeks before Amazon's year-long moratorium on sales to law enforcement was set to expire. The letter contrasted Bezos's and Jassy's vocal support of Black Lives Matter campaigners during last summer's racial justice protests after the murder of George Floyd with reporting that other Amazon products have been used by law enforcement to identify protesters. On May 17, Amazon announced it would extend its moratorium indefinitely, joining competitors IBM and Microsoft in self-regulated purgatory. The move is a nod at the political power of the groups fighting to curb the technology--and recognition that new legislative battle grounds are starting to emerge.

On May 18, Google CEO Sundar Pichai announced an impressive new tool: an AI system called LaMDA that can chat to users about any subject. To start, Google plans to integrate LaMDA into its main search portal, its voice assistant, and Workplace, its collection of cloud-based work software that includes Gmail, Docs, and Drive. But the eventual goal, said Pichai, is to create a conversational interface that allows people to retrieve any kind of information--text, visual, audio--across all Google's products just by asking. LaMDA's rollout signals yet another way in which language technologies are becoming enmeshed in our day-to-day lives. But Google's flashy presentation belied the ethical debate that now surrounds such cutting-edge systems.

Machine learning applications have become ubiquitous. Their applications from machine embedded control in production over process optimization in diverse areas (e.g., traffic, finance, sciences) to direct user interactions like advertising and recommendations. This has led to an increased effort of making machine learning trustworthy. Explainable and fair AI have already matured. They address knowledgeable users and application engineers. However, there are users that want to deploy a learned model in a similar way as their washing machine. These stakeholders do not want to spend time understanding the model. Instead, they want to rely on guaranteed properties. What are the relevant properties? How can they be expressed to stakeholders without presupposing machine learning knowledge? How can they be guaranteed for a certain implementation of a model? These questions move far beyond the current state-of-the-art and we want to address them here. We propose a unified framework that certifies learning methods via care labels. They are easy to understand and draw inspiration from well-known certificates like textile labels or property cards of electronic devices. Our framework considers both, the machine learning theory and a given implementation. We test the implementation's compliance with theoretical properties and bounds. In this paper, we illustrate care labels by a prototype implementation of a certification suite for a selection of probabilistic graphical models.