Law enforcement's use of facial recognition technology was always controversial. Amazon's surprise announcement that it would put a moratorium on police use of its facial recognition software for the next year underscores the big questions surrounding the technology as protests spark a nationwide debate about police brutality and surveillance tactics. Amazon's brief news release never mentioned the words George Floyd, but my Post colleague Jay Greene notes the company hinted that recent events drove this decision. "We've advocated that governments should put in place stronger regulations to govern the ethical use of facial recognition technology, and in recent days, Congress appears ready to take on this challenge," the company said in a statement. "We hope this one-year moratorium might give Congress enough time to implement appropriate rules, and we stand ready to help if requested."

When even the makers of face recognition refuse to sell this surveillance technology because it is so dangerous, lawmakers can no longer deny the threats to our rights and liberties,

That's why the announcements by IBM, Amazon and Microsoft were a success for activists -- a rare retreat by some of Silicon Valley's biggest names over a key new technology. This came from years of work by researchers including Joy Buolamwini to make the case that facial recognition software is biased. A test commissioned by the ACLU of Northern California found Amazon's software called Rekognition misidentified 28 lawmakers as people arrested in a crime. That happens in part because the systems are trained on data sets that are themselves skewed.

On the latest episode of Last Week Tonight, John Oliver turned his attention to the increasingly widespread use of facial recognition technology by law enforcement. Heavily citing Kashmir Hill's investigation of Clearview AI in the New York Times, Oliver explained how a then little-known company created a groundbreaking app where users can take a picture of a person, upload it, and cross-reference it against 3 billion images that the company has scraped from Facebook, Twitter, Venmo, and other websites. Since Hill's investigation was published, Twitter, Facebook, Google, and others have sent cease-and-desist letters to Clearview for violating the websites' terms of service. Still, the company maintains that harvesting the personal information of millions for a secretive database is within the company's First Amendment rights. "You might as well argue that you have an Eighth Amendment right to dress up rabbits like John Lennon," Oliver said.

Our world is undergoing an information Big Bang, in which the universe of data doubles every two years and quintillions of bytes of data are generated every day.1 For decades, Moore's Law on the doubling of computing power every 18-24 months has driven the growth of information technology. Now–as billions of smartphones and other devices collect and transmit data over high-speed global networks, store data in ever-larger data centers, and analyze it using increasingly powerful and sophisticated software–Metcalfe's Law comes into play. It treats the value of networks as a function of the square of the number of nodes, meaning that network effects exponentially compound this historical growth in information. As 5G networks and eventually quantum computing deploy, this data explosion will grow even faster and bigger. The impact of big data is commonly described in terms of three "Vs": volume, variety, and velocity.2

In this paper we propose a causal modeling approach to intersectional fairness, and a flexible, task-specific method for computing intersectionally fair rankings. Rankings are used in many contexts, ranging from Web search results to college admissions, but causal inference for fair rankings has received limited attention. Additionally, the growing literature on causal fairness has directed little attention to intersectionality. By bringing these issues together in a formal causal framework we make the application of intersectionality in fair machine learning explicit, connected to important real world effects and domain knowledge, and transparent about technical limitations. We experimentally evaluate our approach on real and synthetic datasets, exploring its behaviour under different structural assumptions.

Like any technology, AI systems come with inherent risks and potential benefits. It comes with potential disruption of established norms and methods of work, societal impacts and externalities. One may think of the adoption of technology as a form of social contract, which may evolve or fluctuate in time, scale, and impact. It is important to keep in mind that for AI, meeting the expectations of this social contract is critical, because recklessly driving the adoption and implementation of unsafe, irresponsible, or unethical AI systems may trigger serious backlash against industry and academia involved which could take decades to resolve, if not actually seriously harm society. For the purpose of this paper, we consider that a social contract arises when there is sufficient consensus within society to adopt and implement this new technology. As such, to enable a social contract to arise for the adoption and implementation of AI, developing: 1) A socially accepted purpose, through 2) A safe and responsible method, with 3) A socially aware level of risk involved, for 4) A socially beneficial outcome, is key.

Machine common sense remains a broad, potentially unbounded problem in artificial intelligence (AI). There is a wide range of strategies that can be employed to make progress on this challenge. This article deals with the aspects of modeling commonsense reasoning focusing on such domain as interpersonal interactions. The basic idea is that there are several types of commonsense reasoning: one is manifested at the logical level of physical actions, the other deals with the understanding of the essence of human-human interactions. Existing approaches, based on formal logic and artificial neural networks, allow for modeling only the first type of common sense. To model the second type, it is vital to understand the motives and rules of human behavior. This model is based on real-life heuristics, i.e., the rules of thumb, developed through knowledge and experience of different generations. Such knowledge base allows for development of an expert system with inference and explanatory mechanisms (commonsense reasoning algorithms and personal models). Algorithms provide tools for a situation analysis, while personal models make it possible to identify personality traits. The system so designed should perform the function of amplified intelligence for interactions, including human-machine.

Organizations that own data face increasing legal liability for its discriminatory use against protected demographic groups, extending to contractual transactions involving third parties access and use of the data. This is problematic, since the original data owner cannot ex-ante anticipate all its future uses by downstream users. This paper explores the upstream ability to preemptively remove the correlations between features and sensitive attributes by mapping features to a fair representation space. Our main result shows that the fairness measured by the demographic parity of the representation distribution can be certified from a finite sample if and only if the chi-squared mutual information between features and representations is finite. Empirically, we find that smoothing the representation distribution provides generalization guarantees of fairness certificates, which improves upon existing fair representation learning approaches. Moreover, we do not observe that smoothing the representation distribution degrades the accuracy of downstream tasks compared to state-of-the-art methods in fair representation learning.

To build a flexible and interpretable model for document analysis, we develop deep autoencoding topic model (DATM) that uses a hierarchy of gamma distributions to construct its multi-stochastic-layer generative network. In order to provide scalable posterior inference for the parameters of the generative network, we develop topic-layer-adaptive stochastic gradient Riemannian MCMC that jointly learns simplex-constrained global parameters across all layers and topics, with topic and layer specific learning rates. Given a posterior sample of the global parameters, in order to efficiently infer the local latent representations of a document under DATM across all stochastic layers, we propose a Weibull upward-downward variational encoder that deterministically propagates information upward via a deep neural network, followed by a Weibull distribution based stochastic downward generative model. To jointly model documents and their associated labels, we further propose supervised DATM that enhances the discriminative power of its latent representations. The efficacy and scalability of our models are demonstrated on both unsupervised and supervised learning tasks on big corpora.