Influencer marketing remains largely undisclosed or inappropriately disclosed on social media. Non-disclosure issues have become a priority for national and supranational authorities worldwide, who are starting to impose increasingly harsher sanctions on them. This paper proposes a transparent methodology for measuring whether and how influencers comply with disclosures based on legal standards. We introduce a novel distinction between disclosures that are legally sufficient (green) and legally insufficient (yellow). We apply this methodology to an original dataset reflecting the content of 150 Dutch influencers publicly registered with the Dutch Media Authority based on recently introduced registration obligations. The dataset consists of 292,315 posts and is multi-language (English and Dutch) and cross-platform (Instagram, YouTube and TikTok). We find that influencer marketing remains generally underdisclosed on social media, and that bigger influencers are not necessarily more compliant with disclosure standards.

Artificial Intelligence (AI) is taking on increasingly autonomous roles, e.g., browsing the web as a research assistant and managing money. But specifying goals and restrictions for AI behavior is difficult. Similar to how parties to a legal contract cannot foresee every potential "if-then" contingency of their future relationship, we cannot specify desired AI behavior for all circumstances. Legal standards facilitate robust communication of inherently vague and underspecified goals. Instructions (in the case of language models, "prompts") that employ legal standards will allow AI agents to develop shared understandings of the spirit of a directive that generalize expectations regarding acceptable actions to take in unspecified states of the world. Standards have built-in context that is lacking from other goal specification languages, such as plain language and programming languages. Through an empirical study on thousands of evaluation labels we constructed from U.S. court opinions, we demonstrate that large language models (LLMs) are beginning to exhibit an "understanding" of one of the most relevant legal standards for AI agents: fiduciary obligations. Performance comparisons across models suggest that, as LLMs continue to exhibit improved core capabilities, their legal standards understanding will also continue to improve. OpenAI's latest LLM has 78% accuracy on our data, their previous release has 73% accuracy, and a model from their 2020 GPT-3 paper has 27% accuracy (worse than random). Our research is an initial step toward a framework for evaluating AI understanding of legal standards more broadly, and for conducting reinforcement learning with legal feedback (RLLF).

This paper examines and extends the legal microdirectives Recent research by legal scholars suggests that the law theories in three crucial respects: might inevitably be transformed into legal microdirectives consisting of legal rules that are derived (1) By indicating that legal micro-directives are from legal standards or that are otherwise produced likely to be AIenabled and evolve over time in automatically or via the consequent derivations of scope and velocity across the autonomous levels of legal goals and then propagated via automation for AI Legal Reasoning [20] [22], everyday use as readily accessible lawful directives throughout society. This paper examines and extends (2) By exploring the tradeoffs between legal the legal micro-directives theories in three crucial standards and legal rules as the imprinters of the respects: (1) By indicating that legal micro-directives micro-directives, and are likely to be AIenabled and evolve over time in scope and velocity across the autonomous levels of AI (3) By illuminating a set of brittleness exposures Legal Reasoning, (2) By exploring the tradeoffs that can undermine legal micro-directives and between legal standards and legal rules as the proffering potential mitigating remedies to seek imprinters of the micro-directives, and (3) By greater robustness in the instantiation and illuminating a set of brittleness exposures that can promulgation of such AIenabled lawful directives.

Is your artificial intelligence fair? Thanks to the increasing adoption of AI, this has become a question that data scientists and legal personnel now routinely confront. Despite the significant resources companies have spent on responsible AI efforts in recent years, organizations still struggle with the day-to-day task of understanding how to operationalize fairness in AI. So what should companies do to steer clear of employing discriminatory algorithms? They can start by looking to a host of legal and statistical precedents for measuring and ensuring algorithmic fairness.

This article is a guest post on NoCamels and has been contributed by a third party. NoCamels assumes no responsibility for the content, including facts, visuals, and opinions presented by the author(s). Ryan E. Long is a non-residential fellow of Stanford Law School's Center for Internet and Society and Vice-Chair of the CA Lawyers Association, IP Licensing Interest Group. In addition, he has written for or been interviewed by publications such as The Nordic Blockchain Association, El Pais, Cognitive Times, and Digital Trends about new tech subjects such as artificial intelligence, blockchain and "deep fake" videos. Currently, he is an adjunct professor of media law at Pepperdine Law School in Malibu, California.

Technological advancements can enhance human development and contribute to creating optimal conditions for the exercise of human rights. At the same time, we need to address questions of fairness, of the risk of perpetuating bias and stereotypes, of discriminatory decision-making patterns, and of challenges related to interpretability, privacy, security and oversight. And we should ask ourselves: what can countries and international organisations do to address the challenge of "algocracy"? The discussion Artificial Intelligence – Technology to Serve Humankind will engage the audience in critical reflection on the challenges and opportunities that AI carries for individuals and societies, and for the viability of institutional frameworks, with a special emphasis on the use of the technology for public policies that enhance the quality of life and progress of humankind. The event will address the legal and ethical questions that accompany the current and potential use of AI in our society and identify potential ways forward.

It is predicted that, by 2025, robots and machines driven by artificial intelligence (AI) will perform half of all productive functions in the workplace – companies already use robots across many industries, but the sheer scale is likely to prompt some new moral and legal questions. Machines currently have no protected legal rights but, as they become more intelligent and act more like humans, will the legal standards at play need to change? To answer this question, we need to take a good hard look at the nature of robotics and our own system of ethics, tackling a situation unlike anything the human race has ever known. The state of robotics at the moment is so comparatively underdeveloped that most of these questions will just be hypotheticals that will be nearly impossible to answer. Can, and should, robots be compensated for their work, and could they be represented by unions (and, if so, could a human union truly stand up for robot working rights, or would there always be an inherent tension)?

There are significant, real-world benefits to having an accepted and recognized identity. That's why the concept of a digital identity is being pursued around the world, from Australia to India. From airports to health records systems, technologists and policy makers with good intentions are digitizing our identities, making modern life more efficient and streamlined. Governments seek to digitize their citizens in an effort to universalize government services, while the banking, travel, and insurance industries aim to create more seamless processes for their products and services. In places like Syria and Jordan, refugees are often displaced without an identity.

After escalating one of the most lethal covert operations in U.S. history, President Obama finally made a public estimate of the civilian cost of the nation's secret drone program, which has targeted Islamic militants in remote corners of the globe. Human rights groups immediately challenged the estimate and the amount of transparency from the administration, saying both were too limited. The White House said that 64 to 116 civilians had been wrongly killed in 473 strikes launched by the U.S. government from the time Obama was inaugurated and the end of last year. The vast majority of the attacks were launched by drones, officials said, but the estimate also covers some strikes using manned aircraft. Monitoring organizations estimate the number of civilians killed in U.S. strikes ranges from 200 to more than 1,000.