Other difficult tasks, more generally, are how to obtain a robust performance in teamworks (Cohen and Levesque 1990); how to prevent agents from dropping their commitments; or better, how to regulate agents dropping their commitments to a joint action to not disrupt the common activity and preclude the common goal being achieved (Jennings 1995; Singh 1995; Kinny and Georgeff 1991). These tasks have now entered the MAS field's common knowledge. Other problems are perhaps less obvious. The Second International Conference on Multiagent Systems (ICMAS '96) Workshop on Norms, Obligations, and Conventions was held in Kyoto, Japan, from 10 to 13 December 1996. Participants included scientists from deontic logic, database framework, decision theory, agent architecture, cognitive modeling, and legal expert systems.
Profiling is done when your personal aspects are being evaluated in order to make predictions about you, even if no decision is taken. For example, if a company or organisation assesses your characteristics (such as your age, sex, height) or classifies you in a category, this means you are being profiled. Decision-making based solely on automated means happens when decisions are taken about you by technological means and without any human involvement. They can be taken even without profiling. The data protection law establishes that you have the right not to be subject to a decision based solely on automated means, if the decision produces legal effects concerning you or significantly affects you in a similar way.
The rise of artificial intelligence is one of the most disruptive developments of this age. Already of rising importance, AI is considered a make-or-break technology for companies across the board, with 75% of C-level executives believing that employing AI will decide if their business will prosper or fail. The legal sector is an industry that is rich in documents but poor in data -- a stark contrast to many other business sectors, where data is at the heart of everything. This means that most AI solutions thus far are not adapted to a law practice's kind of work, which mostly consists of sifting through thousands of documents relevant to a specific case, with specialized teams hired particularly for this purpose. No surprise then, that law firms have been slow to embrace new tech such as AI.
In 2005, after spending some 25 years in and around the law, I set about writing down what I had learned and valued most. What emerged as of central importance in my career and for the firm I created was relationships: with colleagues, clients and indeed anyone I dealt with in the course of practice and business. Professional, working, relationships are different to personal relationships, and perhaps a little simpler in some respects, and they deserve our deliberate attention. My book was published by the American Bar Association in 2007. In 2015 I completed a second edition which has just been published, again by the ABA, in North America.
Complexity science has spread from its origins in the physical sciences into biological and social sciences (1). Increasingly, the social sciences frame policy problems from the financial system to the food system as complex adaptive systems (CAS) and urge policy-makers to design legal solutions with CAS properties in mind. What is often poorly recognized in these initiatives is that legal systems are also complex adaptive systems (2). Just as it seems unwise to pursue regulatory measures while ignoring known CAS properties of the systems targeted for regulation, so too might failure to appreciate CAS qualities of legal systems yield policies founded upon unrealistic assumptions. Despite a long empirical studies tradition in law, there has been little use of complexity science.