'San Fransicko' author Michael Shellenberger discusses the homeless crisis in California and how to solve it. A federal judge has issued a temporary ban on San Francisco clearing most homeless encampments amid an ongoing lawsuit against the city filed by advocacy groups seeking to stop police sweeps of homeless encampments. Last week, Magistrate Judge Donna M. Ryu in the U.S. District Court in Oakland questioned the tactics used by the city of San Francisco in its homeless encampment cleanups, suggesting that the city is not adhering to its own policies of providing shelter beds to individuals who are being asked to vacate a public area. In her decision, Ryu stated that the city did not offer shelter to homeless individuals before clearing encampments and confiscating their property. The judge also found the city's justification for taking enforcement actions to be "wholly unconvincing," stating that the defendants did not adequately dispute that they cleared people without first providing shelter.

The tech giants that have shaped our lives, online and off, over the course of the 21st century have at last hit a wall. Amazon, Alphabet, Microsoft, Meta, and Apple all saw their valuations fall, sometimes precipitously. Many slashed their workforces; at least 120,000 tech workers lost their jobs this year. The myth of the genius founder, which insulated so many of these giants from so much criticism for so long, was debunked before our eyes. These companies, launched with promises to connect the world, to think different, to make information free to all, to democratize technology, have spent much of the past decade making the sorts of moves that large corporations trying to grow ever larger have historically made--embracing profit over safety, market expansion over product integrity, and rent seeking over innovation--but at much greater scale, speed, and impact.

In an era of mass personalisation and technological innovation, organisations increasingly need to make consideration of the way they use consumer data a part of their organisational culture. Since the GDPR's inception back in May 2018, there have been some encouraging findings (as I have discussed before) indicating that consumers are increasingly willing to share their data in exchange for personalised services and improved experiences. In addition, marketers are more confident about their reputation in the eyes of consumers. However, there is still a long way to go to improve consumer trust in marketing and highlight how data can be used as a force for good. Recent Adobe research reveals that over 75 per cent of UK consumers are concerned about how companies use their data.

The entrepreneurial disruption phase of "move fast and break things" is being replaced with a mantra of "move fast and keep up" when it comes to applying ethical frameworks and leading practices to emerging technologies, according to a new study by Deloitte. The firm's first-ever State of Ethics and Trust in Technology annual report defines emerging technologies, identifies trustworthy and ethical standards, explains different approaches to operationalizing standards, and encourages actions that can be taken in the short term. Many companies want to be on the cutting edge of emerging technologies to stay competitive and gain benefits such as improved customer experience, operational efficiencies and newly-enabled use cases, according to Deloitte. "But these technologies are often being developed at such breakneck speeds that few companies are pausing to consider the ethical implications,'' the report noted. "With great power comes great responsibility.

Nearly all jurisdictions in the United States require a professional license exam, commonly referred to as "the Bar Exam," as a precondition for law practice. To even sit for the exam, most jurisdictions require that an applicant completes at least seven years of post-secondary education, including three years at an accredited law school. In addition, most test-takers also undergo weeks to months of further, exam-specific preparation. Despite this significant investment of time and capital, approximately one in five test-takers still score under the rate required to pass the exam on their first try. In the face of a complex task that requires such depth of knowledge, what, then, should we expect of the state of the art in "AI?" In this research, we document our experimental evaluation of the performance of OpenAI's `text-davinci-003` model, often-referred to as GPT-3.5, on the multistate multiple choice (MBE) section of the exam. While we find no benefit in fine-tuning over GPT-3.5's zero-shot performance at the scale of our training data, we do find that hyperparameter optimization and prompt engineering positively impacted GPT-3.5's zero-shot performance. For best prompt and parameters, GPT-3.5 achieves a headline correct rate of 50.3% on a complete NCBE MBE practice exam, significantly in excess of the 25% baseline guessing rate, and performs at a passing rate for both Evidence and Torts. GPT-3.5's ranking of responses is also highly-correlated with correctness; its top two and top three choices are correct 71% and 88% of the time, respectively, indicating very strong non-entailment performance. While our ability to interpret these results is limited by nascent scientific understanding of LLMs and the proprietary nature of GPT, we believe that these results strongly suggest that an LLM will pass the MBE component of the Bar Exam in the near future.

The commercialisation of artificial intelligence (AI) is taking a similar route, which is unsurprising. But, given AI's innate ability to adapt and learn at an exponential rate, it may not be a bad thing. What needs to be done to ensure the use of AI in hiring is unbiased and equitable? To avoid legal wrangling, the data being used to train AI must be sufficiently representative of all groups. This is especially crucial in hiring because many professional work settings – particularly in industries like computing, finance and media – are dominated by white and/or male employees.

The European Union is set to create the world's first broad standards for regulating artificial intelligence. As well as determining how the technology affects the lives of almost 450 million citizens in the 27 countries of the EU, the rules are likely to influence how AI is used elsewhere in the world. "The idea is that you have a harmonised system, which is really good," says Sandra Wachter at the University of Oxford.

Banks hold a societal responsibility and regulatory requirements to mitigate the risk of financial crimes. Risk mitigation primarily happens through monitoring customer activity through Transaction Monitoring (TM). Recently, Machine Learning (ML) has been proposed to identify suspicious customer behavior, which raises complex socio-technical implications around trust and explainability of ML models and their outputs. However, little research is available due to its sensitivity. We aim to fill this gap by presenting empirical research exploring how ML supported automation and augmentation affects the TM process and stakeholders' requirements for building eXplainable Artificial Intelligence (xAI). Our study finds that xAI requirements depend on the liable party in the TM process which changes depending on augmentation or automation of TM. Context-relatable explanations can provide much-needed support for auditing and may diminish bias in the investigator's judgement. These results suggest a use case-specific approach for xAI to adequately foster the adoption of ML in TM.

In multi-agent systems, norm enforcement is a mechanism for steering the behavior of individual agents in order to achieve desired system-level objectives. Due to the dynamics of multi-agent systems, however, it is hard to design norms that guarantee the achievement of the objectives in every operating context. Also, these objectives may change over time, thereby making previously defined norms ineffective. In this paper, we investigate the use of system execution data to automatically synthesise and revise conditional prohibitions with deadlines, a type of norms aimed at prohibiting agents from exhibiting certain patterns of behaviors. We propose DDNR (Data-Driven Norm Revision), a data-driven approach to norm revision that synthesises revised norms with respect to a data set of traces describing the behavior of the agents in the system. We evaluate DDNR using a state-of-the-art, off-the-shelf urban traffic simulator. The results show that DDNR synthesises revised norms that are significantly more accurate than the original norms in distinguishing adequate and inadequate behaviors for the achievement of the system-level objectives.

Agarwal asserts that Poly's generative AI is superior to most in terms of the quality of assets it produces.