Regulatory frameworks for the use of AI are emerging. However, they trail behind the fast-evolving malicious AI technologies that can quickly cause lasting societal damage. In response, we introduce a pioneering Assistive AI framework designed to enhance human decision-making capabilities. This framework aims to establish a trust network across various fields, especially within legal contexts, serving as a proactive complement to ongoing regulatory efforts. Central to our framework are the principles of privacy, accountability, and credibility. In our methodology, the foundation of reliability of information and information sources is built upon the ability to uphold accountability, enhance security, and protect privacy. This approach supports, filters, and potentially guides communication, thereby empowering individuals and communities to make well-informed decisions based on cutting-edge advancements in AI. Our framework uses the concept of Boards as proxies to collectively ensure that AI-assisted decisions are reliable, accountable, and in alignment with societal values and legal standards. Through a detailed exploration of our framework, including its main components, operations, and sample use cases, the paper shows how AI can assist in the complex process of decision-making while maintaining human oversight. The proposed framework not only extends regulatory landscapes but also highlights the synergy between AI technology and human judgement, underscoring the potential of AI to serve as a vital instrument in discerning reality from fiction and thus enhancing the decision-making process. Furthermore, we provide domain-specific use cases to highlight the applicability of our framework.

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.

Over recent years, advancements in artificial intelligence (AI) have greatly increased the safety of our communities. The technology helps emergency managers predict and mitigate flooding, wildfires, and other natural disasters. It improves image and video analysis, saving investigators' valuable time and reducing errors. It aids crime analysts by pouring through vast amounts of data and making connections that can empower policing. While AI has expanded its role in our everyday lives, many cities are only just beginning to scratch the surface when it comes to the benefits that the technology can provide.

Artificial intelligence (AI) is the most disruptive innovation in a generation. It is quickly becoming an essential component in many industries, including public safety. However, these are still the nascent stages of AI adoption, and with that, come challenges. One is the so-called “black box,” problem, where human operators overseeing a system do not fully understand why the algorithms recommend a particular action. Data goes in one side and results come out the other, but it is not always clear what happens in the interim. . . .

Project InnerEye is a new AI product targeted at improving the productivity of oncologists, radiologists, and surgeons when working with radiological images. The project's main focus is in the treatment of tumors and monitoring the progression of cancer in temporal studies. InnerEye builds upon many years of research in computer vision and machine learning. It employs decision forests (as used already in Kinect and Hololens) to help radiation oncologists and radiologists deliver better care, more efficiently and consistently to their cancer patients.

This month saw the European Conference on AI (ECAI 2020) go digital. Included in the programme were five plenary talks. In this article we summarise the talk by Professor Carme Torras who gave an overview of her group's work on assistive AI, and talked about the ethics of this field. Carme is based at the Institut de Robòtica i Informàtica Industrial (CSIC-UPC) in Barcelona. Her lab includes an assisted living facility where the team can test their robots in real-life situations.

Many industry experts have made predictions about the future of artificial intelligence (AI), machine learning, and automation in the workplace--but little research exists on how skilled and unskilled employees view the coming technology disruptions. Gartner surveyed more than 2,700 employees in the US and the UK across multiple industries and skill levels, including unskilled workers, skilled manual workers, clerical workers, and professionals, to determine how different groups view AI in the workplace. The majority of employees (52%) said they would prefer AI to be deployed as an on-demand helper--essentially, acting as their own employee--rather than as their manager (9%), coworker (11%), or proactive assistant (32%), the report found. In this role, AI could help by limiting their routine work tasks, as well as reducing mistakes, respondents said. SEE: IT leader's guide to the future of artificial intelligence (Tech Pro Research) Workers differ significantly in their opinions about how AI will impact jobs, the report found.

Many industry experts have made predictions about the future of artificial intelligence (AI), machine learning, and automation in the workplace--but little research exists on how skilled and unskilled employees view the coming technology disruptions. Gartner surveyed more than 2,700 employees in the US and the UK across multiple industries and skill levels, including unskilled workers, skilled manual workers, clerical workers, and professionals, to determine how different groups view AI in the workplace. The majority of employees (52%) said they would prefer AI to be deployed as an on-demand helper--essentially, acting as their own employee--rather than as their manager (9%), coworker (11%), or proactive assistant (32%), the report found. In this role, AI could help by limiting their routine work tasks, as well as reducing mistakes, respondents said. SEE: IT leader's guide to the future of artificial intelligence (Tech Pro Research) Workers differ significantly in their opinions about how AI will impact jobs, the report found.

The theory of mind is an important human capability that allows us to understand and predict the goals, intents, and beliefs of other individuals. We present an approach to designing intelligent communicative agents based on modeling theories of mind. This can be tricky because other agents may also have their own theories of mind of the first agent, meaning that these mental models are naturally nested in layers. So, to look for intuitive communicative acts, we recursively apply a planning algorithm in each of these nested layers, looking for possible plans of action as well as their hypothetical consequences, which include the reactions of other agents; we propose that truly intelligent communicative acts are the ones which produce a state of maximum decision theoretic utility according to the entire theory of mind. We implement these ideas using Java and OpenCyc in an attempt to create an assistive AI we call MARTHA. We demonstrate MARTHA's capabilities with two motivating examples: helping the user buy a sandwich and helping the user search for an activity. We see that, in addition to being a personal assistant, MARTHA can be extended to other assistive fields, such as finance, research, and government.

The theory of mind is an important human capability that allows us to understand and predict the goals, intents, and beliefs of other individuals. We present an approach to designing intelligent communicative agents based on modeling theories of mind. This can be tricky because other agents may also have their own theories of mind of the first agent, meaning that these mental models are naturally nested in layers. So, to look for intuitive communicative acts, we recursively apply a planning algorithm in each of these nested layers, looking for possible plans of action as well as their hypothetical consequences, which include the reactions of other agents; we propose that truly intelligent communicative acts are the ones which produce a state of maximum decision theoretic utility according to the entire theory of mind. We implement these ideas using Java and OpenCyc in an attempt to create an assistive AI we call MARTHA. We demonstrate MARTHA's capabilities with two motivating examples: helping the user buy a sandwich and helping the user search for an activity. We see that, in addition to being a personal assistant, MARTHA can be extended to other assistive fields, such as finance, research, and government.