Generating domain-specific content such as legal clauses based on minimal user-provided information can be of significant benefit in automating legal contract generation. In this paper, we propose a controllable graph-based mechanism that can generate legal clauses using only the topic or type of the legal clauses. Our pipeline consists of two stages involving a graph-based planner followed by a clause generator. The planner outlines the content of a legal clause as a sequence of keywords in the order of generic to more specific clause information based on the input topic using a controllable graph-based mechanism. The generation stage takes in a given plan and generates a clause. The pipeline consists of a graph-based planner followed by text generation. We illustrate the effectiveness of our proposed two-stage approach on a broad set of clause topics in contracts.

The "world's first robot lawyer" will take a case in court next month -- with the artificial intelligence (AI) legal assistant helping a defendant fight a traffic ticket. The AI, billed as "the world's first robot lawyer" by the startup that created it, DoNotPay, will run on a smartphone and listen to court arguments in real-time before telling the defendant what to say via headphones. The unprecedented hearing is slated to take place sometime next month, but the makers of the robot lawyer are not disclosing the location of the court or the name of the defendant. Science and technology publication New Scientist reported that the ticket at the center of the trailblazing case was issued for speeding, and the defendant will only say in court what the AI instructs them to say. Should they lose the case, DoNoPay has agreed to cover any fines, according to the company's founder and CEO, Joshua Browder.

MERL is looking for a highly motivated individual to work on hierarchical reinforcement learning for robotic applications. The research will develop novel algorithms for hierarchical reinforcement learning and evaluate them on challenging long horizon robotic problems. The ideal candidate must have experience in either one or multiple of the following topics: (Deep) Reinforcement learning, Hierarchical RL, policy optimization and Markov Decision Processes (MDPs). Senior PhD students in machine learning and engineering with a focus on Reinforcement Learning are encouraged to apply. Prior experience working with physics engines like Mujoco, Bullet, etc. is required.

Riding the generative AI wave, Picsart, the developer behind various photo and video editing apps for the web and mobile devices, is introducing a new iOS app that transforms photos and drawings into digital art. Called SketchAI, the app lets users sketch a picture or upload an existing image and apply different artistic styles to it. SketchAI is easy enough to use. It features several pre-selected styles that can applied to creations, including ink drawing, pencil sketch, and the artist-inspired "Da Vinci" and "Van Gogh." In addition to sketching or uploading a photo, users can add a prompt describing an image (e.g.

Researchers and developers working on AI projects may find it easier to use open source software because it is typically less expensive to use than proprietary software. This may help to lower the price of creating AI solutions, which may boost the field's advancement. Over the past few years, the importance of open source software in the realm of AI has increased significantly. Open source software has several advantages, one of which is the possibility for programmers to work together and exchange information. AI developers can build on the work of others and share their own contributions by adopting open-source software, which promotes innovation and growth in the field of AI more quickly. Since a result, the subject may advance more quickly as programmers can collaborate and benefit from one another's contributions.

A court hearing scheduled for February will set a terrifying precedent. An individual will be represented in court using artificial intelligence for the first time ever. The defendant will use a smartphone app and earpiece to hear advice from a'robot lawyer.' The robot lawyer will advise the defendant on what to say in court. DoNotPay, a company founded in 2015 by a then-Stanford University freshman to appeal parking tickets, developed the technology.

Automated decision-making systems are being increasingly deployed and affect the public in a multitude of positive and negative ways. Governmental and private institutions use these systems to process information according to certain human-devised rules in order to address social problems or organizational challenges. Both research and real-world experience indicate that the public lacks trust in automated decision-making systems and the institutions that deploy them. The recreancy theorem argues that the public is more likely to trust and support decisions made or influenced by automated decision-making systems if the institutions that administer them meet their fiduciary responsibility. However, often the public is never informed of how these systems operate and resultant institutional decisions are made. A ``black box'' effect of automated decision-making systems reduces the public's perceptions of integrity and trustworthiness. The result is that the public loses the capacity to identify, challenge, and rectify unfairness or the costs associated with the loss of public goods or benefits. The current position paper defines and explains the role of fiduciary responsibility within an automated decision-making system. We formulate an automated decision-making system as a data science lifecycle (DSL) and examine the implications of fiduciary responsibility within the context of the DSL. Fiduciary responsibility within DSLs provides a methodology for addressing the public's lack of trust in automated decision-making systems and the institutions that employ them to make decisions affecting the public. We posit that fiduciary responsibility manifests in several contexts of a DSL, each of which requires its own mitigation of sources of mistrust. To instantiate fiduciary responsibility, a Los Angeles Police Department (LAPD) predictive policing case study is examined.

Previous papers presented Witscript and Witscript 2, AI systems for improvising jokes in a conversation. Witscript generates jokes that rely on wordplay, whereas the jokes generated by Witscript 2 rely on common sense. This paper extends that earlier work by presenting Witscript 3, which generates joke candidates using three joke production mechanisms and then selects the best candidate to output. Like Witscript and Witscript 2, Witscript 3 is based on humor algorithms created by an expert comedy writer. Human evaluators judged Witscript 3's responses to input sentences to be jokes 44% of the time. This is evidence that Witscript 3 represents another step toward giving a chatbot a humanlike sense of humor.

This paper presented an XAI system specification and an interactive dialogue model to facilitate the creation of Explanation Experiences (EE). Such specifications combine the knowledge of XAI, domain and system experts of a use case to formalise target user groups and their explanation needs and to implement explanation strategies to address those needs. Formalising the XAI system promotes the reuse of existing explainers and known explanation needs that can be refined and evolved over time using user evaluation feedback. The abstract EE dialogue model formalised the interactions between a user and an XAI system. The resulting EE conversational chatbot is personalised to an XAI system at run-time using the knowledge captured in its XAI system specification. This seamless integration is enabled by using Behaviour Trees (BT) to conceptualise both the EE dialogue model and the explanation strategies. In the evaluation, we discussed several desirable properties of using BTs over traditionally used STMs or FSMs. BTs promote the reusability of dialogue components through the hierarchical nature of the design. Sub-trees are modular, i.e. a sub-tree is responsible for a specific behaviour, which can be designed in different levels of granularity to improve human interpretability. The EE dialogue model consists of abstract behaviours needed to capture EE, accordingly, it can be implemented as a conversational, graphical or text-based interface which caters to different domains and users. There is a significant computational cost when using BTs for modelling dialogue, which we mitigate by using memory. Overall, we find that the ability to create robust conversational pathways dynamically makes BTs a good candidate for designing and implementing conversation for creating explanation experiences.

People are interacting with online systems all the time. In order to use the services being provided, they give consent for their data to be collected. This approach requires too much human effort and is impractical for systems like Internet-of-Things (IoT) where human-device interactions can be large. Ideally, privacy assistants can help humans make privacy decisions while working in collaboration with them. In our work, we focus on the identification and representation of privacy requirements in IoT to help privacy assistants better understand their environment. In recent years, more focus has been on the technical aspects of privacy. However, the dynamic nature of privacy also requires a representation of social aspects (e.g., social trust). In this survey paper, we review the privacy requirements represented in existing IoT ontologies. We discuss how to extend these ontologies with new requirements to better capture privacy, and we introduce case studies to demonstrate the applicability of the novel requirements.