Kaal claims by topic: education-and-practice

75 atomic, individually citable claims from the published work of Wulf A. Kaal tagged education-and-practice.

European lawyers may advise clients to incorporate at home simply because those lawyers do not want to deal with the courts and lawyers of another Member State, which suppresses cross border incorporation independently of statute quality. 2004
Defendants owe a duty to use reasonable care in conducting financial due diligence consistent with the standards of care in the profession. 2016
Business, administrative and legal services that consist of keeping ledgers, such as notary and registry services, legal motions practice, and title companies, are likely to be among the first services eliminated by blockchain adoption. 2017
Because the public blockchain is public and immutable, the technology increases transparency while significantly reducing transaction costs, and intermediaries including lawyers are replaced by code, connectivity, crowd, and collaboration. 2017
Diversity training, diversity performance evaluations, and grievance procedures did not change workforce composition. 2017
The discriminatory effect of existing corporate hierarchies may originate in the education system, because children in struggling school systems lack access to the resources available in better functioning districts. 2017
The core lawyer characteristics and skillsets produced by the existing legal education and regulatory framework are incompatible with what the practice of law in the 21st century demands. 2017
Disruptive innovation in law renders obsolete many and probably most of the traditional legal skills and characteristics that law schools currently cultivate. 2017
Even the law firms that are best at finding innovative solutions for clients remain reluctant to fully adopt Legal Tech innovations, so quality of client service does not predict willingness to adopt. 2017
Law schools that invest early in artificial intelligence, machine learning, and blockchain will gain a comparative advantage over peer schools irrespective of ranking, because demand for lawyers trained in those technologies is likely to spike once law firm adoption crosses a threshold. 2017
The curriculum of American law schools has changed only marginally over the past thirty plus years, even as the practice environment has been transformed. 2017
The challenges created by Legal Tech, the new economy, and platform technologies justify a fundamentally more creative and innovative approach to legal education in the 21st century. 2017
Legal Tech startups will force the legal profession to innovate perpetually, a demand that overextended and cumbersome legal organizations which have lost the capacity for agile reinvention cannot easily meet. 2017
Redesigning legal doctrine around sharing and decentralized peer to peer platforms demands out of the box thinking from a profession whose members were trained, both in law school and across their careers, to think inside the box. 2017
The counseling, deal making, matchmaking, gatekeeping, and enforcement roles historically performed by lawyers are increasingly performed by technology, and blockchain technology and smart contracting will accelerate that substitution. 2017
Intermediaries, lawyers among them, are replaced by code, connectivity, crowd, and collaboration. 2017
Contrary to the widespread belief among legal professionals that code can only handle very simple transactions, blockchain enabled smart contracts generally do not require legal involvement across the spectrum of transactions. 2017
Exponentially increasing disruptive innovation will lead clients to routinely bring legal professionals problems that those lawyers cannot fully understand, inside a legal framework that does not always supply clear or helpful answers. 2017
Law schools should educate lawyers who add value by helping clients and society adjust to the technological environment rather than lawyers who impose unnecessary or unwise restrictions on it, since such restrictions will not stop technological development anyway. 2017
The traditional legal tool kit worked adequately when innovation cycles were long, but where innovation is exponential it is regularly out of touch with the radically different needs of a decentralized world and often produces disastrous outcomes. 2017
Most lawyers and law industry representatives underestimate the implications of emerging Legal Tech. 2017
Advising on blockchain contracts requires that law students and lawyers become familiar with the technology and learn at least basic coding as it pertains to Ethereum smart contracts. 2017
Law schools must enable students to work in interdisciplinary teams with software engineers, and a greater appreciation of how code can be used and integrated in legal contexts is essential to that capability. 2017
The performance of an AI neural network's learning algorithm during supervised training rises with the quality and quantity of the labelled datasets it is trained on, which ties AI progress directly to micro task work. 2018
Existing centralized micro task marketplaces cannot adequately meet the rising demand for high quality labelled AI training data. 2018
Lawyers have historically been most effective and most socially useful when acting as transaction engineers who facilitate new business and social relationships, and the engineering of the near future will largely be code based. 2018
The ability to understand and communicate with coders, as distinct from professional coding competence, is a necessary skill for the lawyer of the future, so law students benefit from grasping the basic concepts and power of coding. 2018
The traditional knowledge transmission model of education is ill suited to a world of fast paced change and easy access to information, because prior experience may not be relevant to a fast changing reality and information is always one search away. 2018
That the technologies driving social change remain a mystery to most people is itself a problem, so practical technical knowledge must be integrated into many fields of education, with coding and data analysis as the starting point. 2018
A transaction engineer is a crucial intermediary who brings together, in a safe environment, parties holding different but mutually compatible interests and expertise. 2018
Lawyers have often failed to perform the function of active transaction engineer and have instead become a hindrance or obstacle to transactions. 2018
The major cause of lawyers obstructing transactions is the tendency to standardize or proceduralize legal solutions and to use standard form templates, because fixed standardized solutions imposed on complex, dynamic transactions produce frustration and difficulties. 2018
The result of this pattern is reputational: lawyers have developed a reputation as the least trusted of professions. 2018
Legal tech will profoundly disrupt the legal profession, and because these technologies are code based, lawyers must be able to understand and talk about code in order to participate in designing them. 2018
Since companies are increasingly managed by and run on software code, facilitating transactions, that is, acting as an active transaction engineer, now involves coding. 2018
Junior legal professionals and legal support staff are the first casualties of the Legal Tech evolution, because applications will soon perform most junior lawyer work without the human elements that create imprecision, flaws, inaccuracies, possible lawsuits and delay. 2018
Contrary to commentators who predict the end of lawyers, the authors reject that claim but hold that lawyers of the future can only function as effective transaction engineers if they understand the power of code. 2018
Lawyers of the future will need to act as project managers or at least as active participants in the multi-disciplinary teams that design the solutions and transactions of the future. 2018
Law programs have been slow to adapt to these technological developments and most students are still being prepared for a hierarchical, centralized and proceduralized world. 2018
Because the new solutions rewarded by the future labor market will be code based, an understanding of code and coding will be essential to participate effectively in the digital world. 2018
Cybersecurity should not be addressed by introducing more law in books; law students should instead look for technology based solutions and at minimum acquire the knowledge needed to evaluate such solutions against the alternatives. 2018
Building lawyers' capacity to think about the social and ethical implications of code is both essential and inevitable, but saying anything sensible about the ethics of technology first requires understanding coding and coders. 2018
The Coding for Lawyers course is not about teaching students how to code but about making them realize how important it is to think about their relationship with new technology and with technology experts. 2018
Coders and developers do not always understand the industry or business environment they target with their software solutions, nor do they always consider the trust or ethical issues raised by the technology based business solutions they implement. 2018
Developers are not trained to consider business context or ethics because most tech education focuses almost exclusively on technical training and skills, so a parallel reconfiguration of technical education is required alongside the reform of legal education. 2018
Co-creation partnerships between developers and non-developers will be crucial to building a better digital future, and understanding coding is what enables lawyers to engage constructively with coders, programmers and other software developers. 2018
If standardized legal work and legal research can be performed by algorithms, the result is an opportunity rather than a threat, because it frees lawyer time for assisting clients with the new and very specific challenges of the digital world. 2018
RegLegalTech startups will force the legal profession to innovate, but that task is not easily accomplished by overextended and cumbersome legal organizations that have lost the capacity for agile reinvention. 2019
Currency stability should be used as the consumer facing proxy for interoperability, because teaching the public to value stability is easier than educating it about the blockchain technology that produces technical interoperability. 2019
Although the Shasper Network itself is permissionless, the SDAO deliberately imposes a prior registration requirement in order to push validator candidates to educate themselves about network requirements and to attend to the documentation. 2021
Tracking and aggregating reputations across DAOs can build a more accurate picture of a person's expertise and experiential gaps than traditional educational institutions provide, which would quickly reveal meaningless certification and licensure programs as worthless. 2021
Bias in AI systems arises when algorithms incorporate discriminatory practices carried in their training data, and the resulting outputs reveal a profound misalignment between AI operations and societal values, ethics, and norms. 2024
Routing proposals through the Forum and then through Validation Pool review is what allows the input parameters and learning data of AI systems to be governed by expert community consensus, because only vetted and consensus backed data and parameters reach AI development. 2024
AI learning is degraded by Web2 platforms because their engagement driven algorithms amplify extreme viewpoints and negativity, so the human sentiment and ethics the models absorb from that data are systematically distorted. 2024
A critical unsolved challenge for AI governance is bias mitigation, because biases enter inadvertently when algorithms incorporate discriminatory practices carried in the data used for training. 2024
Under the proposed model the input parameters and learning data of AI systems are themselves governed by expert community consensus, through submission of proposals to the Forum and review by Validation Pool, so that only vetted and consensus backed data and parameters enter AI development. 2024
Transformer neural network architecture removes the scale constraint on training data but not the quality constraint, so data quality continues to be a major unsolved issue for large language models even where internet scale corpora are available. 2024
The move by AI developers toward smaller training datasets raises the risk of overfitting, especially with complex models, which forces LLM developers to rely on regularization to counteract overfitting of the model to the training data. 2024
The quality and quantity of labeled datasets directly governs the performance of the neural network's learning algorithm during supervised training, so the evolution of AI is correlated with the evolution of micro task work. 2024
Deep learning models inadvertently learn and amplify whatever biases exist in their training data, so the composition of the training corpus, not the architecture, is the source of unfair or discriminatory outcomes. 2024
Most GNN architectures assume homogeneous graph structures, so adapting them to heterogeneous graphs with diverse node and edge types remains an unsolved research challenge, and full batch training on large graphs suffers memory overflow. 2024
Requiring community members to stake reputation tokens in order to validate data quality is what produces robust and reliable training datasets, and this participatory validation improves annotation accuracy while reducing bias. 2024
Preparing a workforce for the quantum economy requires universities and training institutions to build specialized curricula that combine quantum mechanics, computer science, and information theory, paired with industry partnerships that supply hands on experience. 2024
The accessible reserves of publicly available human-created text usable for training large language models could be exhausted by 2028 at current usage trajectories. 2025
Data exhaustion is caused primarily by the exponential growth in the size of datasets needed to build increasingly sophisticated AI models, not by any sudden loss of existing text. 2025
The total effective stock of human-generated text is estimated at roughly 300 trillion tokens, with a plausible range from 100 trillion to 1 quadrillion tokens. 2025
The apparent abundance of internet text overstates the usable supply, because much of it fails quality thresholds for model training due to redundancy, noise, or irrelevance. 2025
As AI-generated content proliferates online it dilutes the diversity and originality of the text pool available for later training, producing performance degradation across successive model generations. 2025
Latency, throughput limits, and the absence of fully automated continuous training and validation together defeat the timeliness advantage that real-time data is supposed to deliver. 2025
Because blockchain records a verifiable and immutable history of data provenance and alterations, it mitigates data poisoning risk and supports the claim that AI models were trained on genuine datasets. 2025
Anomaly detection and behavioral analysis models trained on agent data may replicate the biases in that data and fail to detect novel deviations absent from the training set. 2025
Bias in judicial AI arises because models are trained on historical data that reflect past inequities, and the standard remedy of fairness through unawareness, meaning the omission of protected characteristics such as race, fails because proxy variables continue to correlate with the omitted attribute. 2025
Because AI systems are predominantly developed in the West and trained mostly on Western data, their outputs are liable to carry cultural biases that inadequately represent non-Western cultures and the values inherent in them. 2025
Computational abundance does not eliminate information asymmetry; it transforms its locus, since traditional informational advantages such as knowledge of market conditions, contract terms, and domain expertise become accessible at negligible cost. 2026
The legal profession faces a structural problem that incremental reform cannot solve, because its obsolescence curve is steeper than the curriculum adaptation curve. 2026