Jon Whittle – författare

AI systems are solving real-world challenges and transforming industries, but there are serious concerns about how responsibly they operate on behalf of the humans that rely on them. Many ethical principles and guidelines have been proposed for AI systems, but they're often too 'high-level' to be translated into practice. Conversely, AI/ML researchers often focus on algorithmic solutions that are too 'low-level' to adequately address ethics and responsibility. In this timely, practical guide, pioneering AI practitioners bridge these gaps. The authors illuminate issues of AI responsibility across the entire system lifecycle and all system components, offer concrete and actionable guidance for addressing them, and demonstrate these approaches in three detailed case studies.

Writing for technologists, decision-makers, students, users, and other stake-holders, the topics cover:

Governance mechanisms at industry, organisation, and team levels Development process perspectives, including software engineering best practices for AI System perspectives, including quality attributes, architecture styles, and patterns Techniques for connecting code with data and models, including key tradeoffs Principle-specific techniques for fairness, privacy, and explainability A preview of the future of responsible AI

If we could know in 2020 what we will know in 2025 (only five foreseeable years into the future), how would we change our attitudes, actions, and the way in which we practice law, the services we offer, the clients we target, and the ways in which we choose to deliver our services? Indeed - if we could have known a year ago the events of the first three months in 2020, what might we have done to prepare? The American writer and humorist, Mark Twain, advised: "When everybody is out digging for gold, the business to be in is selling shovels!" So, what foreseeable trend may represent the figurative "shovel" that every client will need tomorrow?

The COVID-19 pandemic has undoubtedly had a seismic and lasting impact on how the business of law is conducted. Whilst 2020 certainly expedited changes that were already trending – flexible work schedules, fully-remote offices, revised resource allocations, new client expectations – it also forced firms to adopt practices, methodologies, and strategies that pre-COVID they insisted they could not.These changes are not only here to stay but have become the expectation. Law firm attorneys, staff, and indeed clients are no longer interested in a traditional office, nor the practices that typically occurred within them. The modern law firm needs to evolve with both employee and consumer expectations in order to stay abreast with the post-pandemic world.The Post-Pandemic Law Firm looks at how law firms can make a paradigm shift, adopting an entirely new business model that focuses on providing outcomes, outputs, and results to their clients and internally places the wellbeing of their team as a cornerstone to the future long-term success and sustainability of the legal profession. Chapters include changes to business models, virtual and remote working, how the pandemic has affected women in the profession, the future of dispute resolution, M&A activity and changes to pricing models – all authored by highly respected practitioners in the modern legal system.For those law firms leaders and lawyers that realize a ‘return to normal’ is exactly the wrong approach, there is an opportunity to create a brighter future where work–life balance, market innovation, and smart use of technology will define the law firm of the future.

This book constitutes the refereed proceedings of the 6th International Conference on the Unified Modelling Language, UML 2003, held in San Francisco, CA, USA in October 2003.The 25 revised full papers, 4 tool papers, and 1 experience paper presented together with the abstracts of 3 invited talks and summaries on the UML 2003 workshop and tutorials were carefully reviewed and selected from initially 168 submissions. The papers are organized in topical sections on practical model management, time and quality of service, tools, composition and architecture, transformation, Web related issues, testing and validation, improving UML/OCL, consistency, and methodology.

This book constitutes the refereed proceedings of the 9th International Conference on Model Driven Engineering Languages and Systems (formerly the UML series of conferences), MoDELS 2006, held in Genova, Italy, in October 2006. The 51 revised full papers and 2 invited papers presented were carefully reviewed and selected from 178 initial submissions. The papers are organized in topical sections on evaluating UML, MDA in software development, concrete syntax, applying UML to interaction and coordination, aspects, model integration, formal semantics of UML, security, model transformation tools and implementation, analyzing dynamic models, specifying transformations, MOF, bridging models, risk, trust and dependability, tool environments, OCL, roundtrip engineering, real time and embedded systems, as well as workshops, tutorials and panels.

This book constitutes the refereed proceedings of the 14th International Conference on Model Driven Engineering Languages and Systems, MODELS 2011, held in Wellington, New Zealand, in October 2011. The papers address a wide range of topics in research (foundations track) and practice (applications track). For the first time a new category of research papers, vision papers, are included presenting "outside the box" thinking. The foundations track received 167 full paper submissions, of which 34 were selected for presentation. Out of these, 3 papers were vision papers. The application track received 27 submissions, of which 13 papers were selected for presentation. The papers are organized in topical sections on model transformation, model complexity, aspect oriented modeling, analysis and comprehension of models, domain specific modeling, models for embedded systems, model synchronization, model based resource management, analysis of class diagrams, verification and validation, refactoring models, modeling visions, logics and modeling, development methods, and model integration and collaboration.