Real-time crime center (RTCC) is a term that is seen more and more in the public safety industry. RTCCs are essentially centralized technology hubs used by law enforcement agencies to track data in real time, identify patterns and to prevent and reduce crime. According to the United States Department of Justice (DOJ) Bureau of Justice Assistance (BJA), the mission of an RTCC is "to provide a law enforcement agency with the ability to capitalize on a wide and expanding range of technologies for efficient and effective policing." Many major cities have established RTCCs in order to better protect their residents, officers and communities. RTCCs can house members of one or multiple agencies and receive large amounts of data from many sources across their jurisdictions, including video cameras, sensors, license plate readers (LPR), gunshot detection, drones, facial recognition, computer-aided dispatch (CAD) systems, records management systems (RMS), electronic monitoring, the National Crime Information Center (NCIC) and more.

Patent prosecutors should consider drafting claims to avoid the situation where the AI is the only entity providing an inventive contribution. Artificial intelligence (AI) is making an impact in our everyday life. AI helps us cut grass and vacuum living rooms. It helps us identify images for applications from waste sorting to medical diagnosis. AI also is making an impact in research and development.

As automated decision making and decision assistance systems become common in everyday life, research on the prevention or mitigation of potential harms that arise from decisions made by these systems has proliferated. However, various research communities have independently conceptualized these harms, envisioned potential applications, and proposed interventions. The result is a somewhat fractured landscape of literature focused generally on ensuring decision-making algorithms "do the right thing". In this paper, we compare and discuss work across two major subsets of this literature: algorithmic fairness, which focuses primarily on predictive systems, and ethical decision making, which focuses primarily on sequential decision making and planning. We explore how each of these settings has articulated its normative concerns, the viability of different techniques for these different settings, and how ideas from each setting may have utility for the other.

An individualized treatment regime (ITR) is a decision rule that assigns treatments based on patients' characteristics. The value function of an ITR is the expected outcome in a counterfactual world had this ITR been implemented. Recently, there has been increasing interest in combining heterogeneous data sources, such as leveraging the complementary features of randomized controlled trial (RCT) data and a large observational study (OS). Usually, a covariate shift exists between the source and target population, rendering the source-optimal ITR unnecessarily optimal for the target population. We present an efficient and robust transfer learning framework for estimating the optimal ITR with right-censored survival data that generalizes well to the target population. The value function accommodates a broad class of functionals of survival distributions, including survival probabilities and restrictive mean survival times (RMSTs). We propose a doubly robust estimator of the value function, and the optimal ITR is learned by maximizing the value function within a pre-specified class of ITRs. We establish the $N^{-1/3}$ rate of convergence for the estimated parameter indexing the optimal ITR, and show that the proposed optimal value estimator is consistent and asymptotically normal even with flexible machine learning methods for nuisance parameter estimation. We evaluate the empirical performance of the proposed method by simulation studies and a real data application of sodium bicarbonate therapy for patients with severe metabolic acidaemia in the intensive care unit (ICU), combining a RCT and an observational study with heterogeneity.

Generative AI technologies are growing in power, utility, and use. As generative technologies are being incorporated into mainstream applications, there is a need for guidance on how to design those applications to foster productive and safe use. Based on recent research on human-AI co-creation within the HCI and AI communities, we present a set of seven principles for the design of generative AI applications. These principles are grounded in an environment of generative variability. Six principles are focused on designing for characteristics of generative AI: multiple outcomes & imperfection; exploration & control; and mental models & explanations. In addition, we urge designers to design against potential harms that may be caused by a generative model's hazardous output, misuse, or potential for human displacement. We anticipate these principles to usefully inform design decisions made in the creation of novel human-AI applications, and we invite the community to apply, revise, and extend these principles to their own work.

Jazmia Henry July 18, 2022 Summary Discussions about reducing the bias present in algorithms have been on the rise since the mid 2010s. AI ethicists, DEI practitioners, Sociologists, Data Scientists and Social Justice Advocates have decried the lack of understanding of the harms that algorithms pose to people who belong to historically marginalized groups. These cries have become increasingly accepted in industry since 2020, but little is understood of how algorithm and Machine Learning (ML) model builders should go about mitigating bias in models that are intended for deployment. This chapter is written with the Data Scientist or MLOps professional in mind but can be used as a resource for policy makers, reformists, AI Ethicists, sociologists, and others interested in finding methods that help reduce bias in algorithms. I will take a deployment centered approach with the assumption that the professionals reading this work have already read the amazing work on the implications of algorithms on historically marginalized groups by Gebru, Buolamwini, Benjamin and Shane to name a few. If you have not read those works, I refer you to the "Important Reading for Ethical Model Building " list at the end of this paper as it will help give you a framework on how to think about Machine Learning models more holistically taking into account their effect on marginalized people. In the Introduction to this chapter, I root the significance of their work in real world examples of what happens when models are deployed without transparent data collected for the training process and are deployed without the practitioners paying special attention to what happens to models that adapt to exploit gaps between their training environment and the real world. The rest of this chapter builds on the work of the aforementioned researchers and discusses the reality of models performing post production and details ways ML practitioners can identify bias using tools during the MLOps lifecycle to mitigate bias that may be introduced to models in the real world. Introduction "Whether AI will help us reach our aspirations or reinforce the unjust inequalities is ultimately up to us." - Joy Buolowini, 'Facing the Coded Gaze' AI: More than Human Whether you're driving your car using a GPS system, call on Alexa or Siri to turn on your favorite tune, go on social media to perform a well-earned scroll down memory lane, or go to Google search to find a gift to buy for a friend, you have encountered a Machine Learning model.

Meta said Monday that the Variance Reduction System, the machine learning-powered technology it initially discussed last June to ensure the equitable distribution of ads on its platforms, is now live for housing ads in the U.S. Vice president of civil rights and deputy general counsel Roy L. Austin Jr. said in a Newsroom post Monday that the plan is to extend VRS to credit and employment ads in the U.S. at some point in 2023. The Department of Justice reached a settlement with Meta last June regarding a complaint filed in August 2018 with the Department of Housing and Urban Development over discriminatory uses of ad targeting options from then-Facebook. The HUD complaint was related to housing ads, but the same issues were raised regarding credit and employment ads, and Meta said at the time that it would apply plans it shared in the settlement to all three categories. DOJ civil rights division assistant attorney general Kristen Clarke said in a statement, "This development marks a pivotal step in the Justice Department's efforts to hold Meta accountable for unlawful algorithmic bias and discriminatory ad delivery on its platforms. The Justice Department will continue to hold Meta accountable by ensuring that the Variance Reduction System addresses and eliminates discriminatory delivery of advertisements on its platforms. Federal monitoring of Meta should send a strong signal to other tech companies that they, too, will be held accountable for failing to address algorithmic discrimination that runs afoul of our civil rights laws."

Across the technology industry, artificial intelligence (AI) has boomed over the last year. Lensa went viral creating artistic avatar artwork generated from real-life photos. The OpenAI chatbot ChatGPT garnered praise as a revolutionary leap in generative AI with the ability to provide answers to complex questions in natural language text. Such innovations have ignited an outpouring of investments even as the tech sector continues to experience major losses in stock value along with massive job cuts. And there is no indication the development of these AI-powered capabilities will slow down from their record pace.

Supermarkets are calling for new laws that let them use AI to verify a customer is over-18 when buying alcohol. The British Retail Consortium said age estimation technology would make stores'a safer place to work and shop'. Shop assistants face over 1,300 incidents of violence and abuse every day, with staff asking to check a customer's age one of the most common triggers. The call comes after successful trials by the Home Office at several UK retailers including Tesco, Asda and Morrisons over the past year. To prove their age, shoppers buying alcohol are asked to look at a camera installed in the self-checkout to undertake a facial scan.

We present a novel Transformer-based multi-agent system for simulating the judicial rulings of the 2010-2016 Supreme Court of the United States. We train nine separate models with the respective authored opinions of each supreme justice active ca. 2015 and test the resulting system on 96 real-world cases. We find our system predicts the decisions of the real-world Supreme Court with better-than-random accuracy. We further find a correlation between model accuracy with respect to individual justices and their alignment between legal conservatism & liberalism. Our methods and results hold significance for researchers interested in using language models to simulate politically-charged discourse between multiple agents.