The Oakland Unified School District this week issued an apology for sending out a survey that included a historically racist term for people of Asian descent. However, a movement is underway to prevent bad word choices. "I think that words do matter, so I think that you do have to be very mindful of the words that you use," says Jaye Bailey, Valley Transportation Authority's head of civil rights and employee relations. Whether it's a transit agency like VTA or a private company, attention to messaging has never been greater as a result of the social justice movement. RELATED: Oakland Unified School District apologizes after'historically racist' term used in survey "You really work hard to normalize the language within your organization so that everybody is aware of it so that it becomes second, second nature," she added.

Sundar Pichai, chief executive of Alphabet, Google's parent company, is a mild-mannered software engineer who is not good at games of verbal fisticuffs with US politicians. He received a drubbing last month during the "big tech" congressional hearing. Pichai can, however, summon lawyers and lobbyists galore as soon as the game gets more serious, which it definitely has. The US Department of Justice (DoJ) last week launched a huge and historic antitrust case against Google, accusing the tech company of abusing its position to maintain an illegal monopoly over internet searches and search advertising. In response, Kent Walker, Google's chief lawyer, published an indignant blogpost that signalled how the firm will fight this.

The importance of asymmetries in prediction problems arising in economics has been recognized for a long time. In this paper, we focus on binary choice problems in a data-rich environment with general loss functions. In contrast to the asymmetric regression problems, the binary choice with general loss functions and high-dimensional datasets is challenging and not well understood. Econometricians have studied binary choice problems for a long time, but the literature does not offer computationally attractive solutions in data-rich environments. In contrast, the machine learning literature has many computationally attractive algorithms that form the basis for much of the automated procedures that are implemented in practice, but it is focused on symmetric loss functions that are independent of individual characteristics. One of the main contributions of our paper is to show that the theoretically valid predictions of binary outcomes with arbitrary loss functions can be achieved via a very simple reweighting of the logistic regression, or other state-of-the-art machine learning techniques, such as boosting or (deep) neural networks. We apply our analysis to racial justice in pretrial detention.

After developer adjustments to a machine learning (ML) algorithm, how can the results of an old hyperparameter optimization (HPO) automatically be used to speedup a new HPO? This question poses a challenging problem, as developer adjustments can change which hyperparameter settings perform well, or even the hyperparameter search space itself. While many approaches exist that leverage knowledge obtained on previous tasks, so far, knowledge from previous development steps remains entirely untapped. In this work, we remedy this situation and propose a new research framework: hyperparameter transfer across adjustments (HT-AA). To lay a solid foundation for this research framework, we provide four simple HT-AA baseline algorithms and eight benchmarks changing various aspects of ML algorithms, their hyperparameter search spaces, and the neural architectures used. The best baseline, on average and depending on the budgets for the old and new HPO, reaches a given performance 1.2--2.6x faster than a prominent HPO algorithm without transfer. As HPO is a crucial step in ML development but requires extensive computational resources, this speedup would lead to faster development cycles, lower costs, and reduced environmental impacts. To make these benefits available to ML developers off-the-shelf and to facilitate future research on HT-AA, we provide python packages for our baselines and benchmarks.

When you harness the power and potential of machine learning, there are also some drastic downsides that you've got to manage. Deploying machine learning, you face the risk that it be discriminatory, biased, inequitable, exploitative, or opaque. In this article, I cover six ways that machine learning threatens social justice – linking to short videos that dive deeply into each one – and reach an incisive conclusion: The remedy is to take on machine learning standardization as a form of social activism. When you use machine learning, you aren't just optimizing models and streamlining business. In essence, the models embody policies that control access to opportunities and resources for many people.

Contract life cycle management systems have been around for decades, but the latest generation of AI-enabled tools can help elevate the contracting function. In recent years, organizations that have struggled to understand and manage the entirety of their obligations to customers and suppliers have shown increasing interest in their company's contract life cycle management (CLM). Specifically, organizations seem to be focused on CLM operating models, processes, and enabling technologies to manage these critical obligations. That appetite has increased in the wake of COVID-19, as many companies wrestle with a lack of visibility into their contracts across the enterprise. In the past, some organizations have standardized their processes within certain silos or even implemented CLM technology.

Despite being a poor and unequal country, Costa Rica has managed to close the gap in access to technology for its citizens, and it is now leading the way in the region. The country started the process of admission for the Organization for Economic Cooperation and Development (OECD) several years ago with reforms on laws, the creation of policies and the use of Computer Technologies to improve education, information access, financial markets, competitiveness, and a more open government. In May 2020, Costa Rica became the first Central American or Caribbean country invited to become an OECD member. The OECD has almost 60 years of existence, and its members are many of the world's more developed countries that work together to shape policies that foster prosperity, equality, opportunity, and well-being for their citizens. Costa Rica will become the 38th member, the fourth of Latin America.

Transaction data is like a friendship tie: both parties must respect the relationship and if one party exploits it the relationship sours. As data becomes increasingly valuable, firms must take care not to exploit their users or they will sour their ties. Ethical uses of data cover a spectrum: at one end, using patient data in healthcare to cure patients is little cause for concern. At the other end, selling data to third parties who exploit users is serious cause for concern.2 Between these two extremes lies a vast gray area where firms need better ways to frame data risks and rewards in order to make better legal and ethical choices.

That's key, since inconvenience is the enemy of sales. The pandemic wreaked havoc on supply chains, which, coupled with consumer reluctance to buy nondiscretionary items, reduced data earlier this year. Retailers that could afford AI could adjust, often by tapping nontraditional data. "Mobile is the new mall," says Cowen analyst Oliver Chen, who notes that machine learning allows brands to build one-on-one relationships with consumers at scale. That's part of the rationale behind Walmart's bid for TikTok, which provides data on how younger shoppers engage with brands via social media.

The availability of massive amounts of data, coupled with high-performance cloud computing platforms, has driven significant progress in artificial intelligence and, in particular, machine learning and optimization. It has profoundly impacted several areas, including computer vision, natural language processing, and transportation. However, the use of rich data sets also raises significant privacy concerns: They often reveal personal sensitive information that can be exploited, without the knowledge and/or consent of the involved individuals, for various purposes including monitoring, discrimination, and illegal activities. The second AAAI Workshop on Privacy-Preserving Artificial Intelligence (PPAI-21) held at the Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI-21) builds on the success of last year's AAAI PPAI to provide a platform for researchers, AI practitioners, and policymakers to discuss technical and societal issues and present solutions related to privacy in AI applications. The workshop will focus on both the theoretical and practical challenges related to the design of privacy-preserving AI systems and algorithms and will have strong multidisciplinary components, including soliciting contributions about policy, legal issues, and societal impact of privacy in AI. Finally, the workshop will welcome papers that describe the release of privacy-preserving benchmarks and data sets that can be used by the community to solve fundamental problems of interest, including in machine learning and optimization for health systems and urban networks, to mention but a few examples.