Shortly after MIT Technology Review contacted iRobot for comment on the photos last fall, the company terminated its contract with Scale AI. Nevertheless, in a LinkedIn post in response to our story, iRobot CEO Colin Angle did not acknowledge the mere fact that these images, and the faces of test users, were visible to human gig workers was a reason for concern. Rather, he wrote, making such images available was actually necessary to train iRobot's object recognition algorithms: "How do our robots get so smart? It starts during the development process, and as part of that, through the collection of data to train machine learning algorithms." Besides, he pointed out, the images came not from customers but from "paid data collectors and employees" who had signed consent agreements.

Call it the Cyber-ano de Bergerac Defense. The eerie new capabilities of artificial intelligence are about to show up inside a courtroom -- in the form of an AI chatbot lawyer that will soon argue a case in traffic court. That's according to Joshua Browder, the founder of a consumer-empowerment startup who conceived of the scheme. Sometime next month, Browder is planning to send a real defendant into a real court armed with a recording device and a set of earbuds. Browder's company will feed audio of the proceedings into an AI that will in turn spit out legal arguments; the defendant, he says, has agreed to repeat verbatim the outputs of the chatbot to an unwitting judge.

Network structure provides critical information for understanding the dynamic behavior of networks. However, the complete structure of real-world networks is often unavailable, thus it is crucially important to develop approaches to infer a more complete structure of networks. In this paper, we integrate the configuration model for generating random networks into an Expectation-Maximization-Aggregation (EMA) framework to reconstruct the complete structure of multiplex networks. We validate the proposed EMA framework against the random model on several real-world multiplex networks, including both covert and overt ones. It is found that the EMA framework generally achieves the best predictive accuracy compared to the EM framework and the random model. As the number of layers increases, the performance improvement of EMA over EM decreases. The inferred multiplex networks can be leveraged to inform the decision-making on monitoring covert networks as well as allocating limited resources for collecting additional information to improve reconstruction accuracy. For law enforcement agencies, the inferred complete network structure can be used to develop more effective strategies for covert network interdiction.

Parallel search algorithms harness the multithreading capability of modern processors to achieve faster planning. One such algorithm is PA*SE (Parallel A* for Slow Expansions), which parallelizes state expansions to achieve faster planning in domains where state expansions are slow. In this work, we propose ePA*SE (Edge-based Parallel A* for Slow Evaluations) that improves on PA*SE by parallelizing edge evaluations instead of state expansions. This makes ePA*SE more efficient in domains where edge evaluations are expensive and need varying amounts of computational effort, which is often the case in robotics. On the theoretical front, we show that ePA*SE provides rigorous optimality guarantees. In addition, ePA*SE can be trivially extended to handle an inflation weight on the heuristic resulting in a bounded suboptimal algorithm w-ePA*SE (Weighted ePA*SE) that trades off optimality for faster planning. On the experimental front, we validate the proposed algorithm in two different planning domains: 1) motion planning for 3D humanoid navigation and 2) task and motion planning for a dual-arm robotic assembly task. We show that ePA*SE can be significantly more efficient than PA*SE and other alternatives. The open-source code for ePA*SE along with the baselines is available here: https://github.com/shohinm/parallel_search

Since 2005, there has been an increasing development of digitization within the public administration that sees the introduction of the use of technology as a privileged tool in the management of administrative activities. The main objective is to promote digitization in administrations in order to achieve greater efficiency in their activities in internal relations, between different administrations, and between the latter and private individuals. The entry of artificial intelligence into public action, however, needs to be accompanied by an adequate regulatory framework to guarantee the rights of those administered. The notion of digital transformation has gained significant attention in the literature[1]. Although approaches to the definition of digital transformation vary[2], most authors suggest that digital transformation involves the use of ICT technology to create fundamentally new capabilities in business, public administration[3] and people's lives[4].

Recent years have seen the development of many open-source ML fairness toolkits aimed at helping ML practitioners assess and address unfairness in their systems. However, there has been little research investigating how ML practitioners actually use these toolkits in practice. In this paper, we conducted the first in-depth empirical exploration of how industry practitioners (try to) work with existing fairness toolkits. In particular, we conducted think-aloud interviews to understand how participants learn about and use fairness toolkits, and explored the generality of our findings through an anonymous online survey. We identified several opportunities for fairness toolkits to better address practitioner needs and scaffold them in using toolkits effectively and responsibly. Based on these findings, we highlight implications for the design of future open-source fairness toolkits that can support practitioners in better contextualizing, communicating, and collaborating around ML fairness efforts.

Biases in existing datasets used to train algorithmic decision rules can raise ethical and economic concerns due to the resulting disparate treatment of different groups. We propose an algorithm for sequentially debiasing such datasets through adaptive and bounded exploration in a classification problem with costly and censored feedback. Exploration in this context means that at times, and to a judiciously-chosen extent, the decision maker deviates from its (current) loss-minimizing rule, and instead accepts some individuals that would otherwise be rejected, so as to reduce statistical data biases. Our proposed algorithm includes parameters that can be used to balance between the ultimate goal of removing data biases -- which will in turn lead to more accurate and fair decisions, and the exploration risks incurred to achieve this goal. We analytically show that such exploration can help debias data in certain distributions. We further investigate how fairness criteria can work in conjunction with our data debiasing algorithm. We illustrate the performance of our algorithm using experiments on synthetic and real-world datasets.

Various legislators and policy experts have recently shared their views on the imminent future of artificial intelligence (AI) legislation. Speaking at CES 2023, as quoted by IoT World Today, Laura Caroli, a parliamentary assistant currently leading negotiations on the AI Act, says an agreement is expected by March. Regarding biometrics, the Council of the EU tweaked the remote biometric identification system definition in December 2022 by clarifying that such systems can be used only in cases where it is "strictly necessary for law enforcement purposes." Caroli also adds that while the legislation has now been approved by the Council of the European Union and should be fully approved by the end of 2023, the law will only come into force two years later. "It takes a long time, but it's a complex piece of legislation and a lot of tension politically."

A legal services company says it's willing to pay $1 million to fuck around and find out. On Sunday, DoNotPay CEO Joshua Browder made a wild proposition to any lawyer slated to argue an upcoming case in front of the U.S. Supreme Court. Let DoNotPay's AI lawyer, which is built on OpenAI's viral GPT-3 API, argue the case before the court, Browder said, in exchange for $1 million. All the human lawyer would need to do is wear AirPods and repeat to the court what DoNotPay's robot lawyer argues. "DoNotPay will pay any lawyer or person $1,000,000 with an upcoming case in front of the United States Supreme Court to wear AirPods and let our robot lawyer argue the case by repeating exactly what it says," Browder wrote on Twitter on Sunday night.

For the first time ever, a robot with artificial intelligence is prepared to represent a person in court. Eventually, history will be made next month when "the world's first robot lawyer" represents a defendant fighting a traffic ticket in court. The DoNotPay Artificial Intelligence (AI) the bot will function on the defendant's smartphone. It will actively listen to court proceedings while guiding the defendant's remarks through an earbud. SEE ALSO: Here's How Apple Watch Saved A 16-Year Old Skier's Life According to reports, the defendant will only testify in court according to what the AI tells them to say.