Modern slavery affects millions of people worldwide, and regulatory frameworks such as Modern Slavery Acts now require companies to publish detailed disclosures. However, these statements are often vague and inconsistent, making manual review time-consuming and difficult to scale. While NLP offers a promising path forward, high-stakes compliance tasks require more than accurate classification: they demand transparent, rule-aligned outputs that legal experts can verify. Existing applications of large language models (LLMs) often reduce complex regulatory assessments to binary decisions, lacking the necessary structure for robust legal scrutiny. We argue that compliance verification is fundamentally a rule-matching problem: it requires evaluating whether textual statements adhere to well-defined regulatory rules. To this end, we propose a novel framework that harnesses AI for rule-level compliance verification while preserving expert oversight. At its core is the Compliance Alignment Judge (CA-Judge), which evaluates model-generated justifications based on their fidelity to statutory requirements. Using this feedback, we train the Compliance Alignment LLM (CALLM), a model that produces rule-consistent, human-verifiable outputs. CALLM improves predictive performance and generates outputs that are both transparent and legally grounded, offering a more verifiable and actionable solution for real-world compliance analysis.

Despite over a decade of legislative efforts to address modern slavery in the supply chains of large corporations, the effectiveness of government oversight remains hampered by the challenge of scrutinizing thousands of statements annually. While Large Language Models (LLMs) can be considered a well established solution for the automatic analysis and summarization of documents, recognizing concrete modern slavery countermeasures taken by companies and differentiating those from vague claims remains a challenging task. To help evaluate and fine-tune LLMs for the assessment of corporate statements, we introduce a dataset composed of 5,731 modern slavery statements taken from the Australian Modern Slavery Register and annotated at the sentence level. This paper details the construction steps for the dataset that include the careful design of annotation specifications, the selection and preprocessing of statements, and the creation of high-quality annotation subsets for effective model evaluations. To demonstrate our dataset's utility, we propose a machine learning methodology for the detection of sentences relevant to mandatory reporting requirements set by the Australian Modern Slavery Act. We then follow this methodology to benchmark modern language models under zero-shot and supervised learning settings.

It was believed that Hephaestus, the Greek god of metallurgy, created and programmed a giant bronze warrior named Talos to protect Crete. Talos is said to be a futuristic cybernetic creature that can think and feel. It was believed that Hephaestus created the creature as part of his project, combining neurological-computer interfaces and living and non-living components into one massive being. The mythology surrounding the creation of the warrior is also said to be the first example of people thinking about the potential of Artificial Intelligence and intelligent robots. Cut to the present, Artificial Intelligence is all around us, and data and algorithms have become more essential to our lives than we can fathom.

Position: Social Science Researcher, Senior Manager Department: Learning, Innovation, and Data Systems FLSA Status: Full-Time, Exempt Reports to: Director, Learning, Innovation and Data Systems Direct Reports: None Date Issued: October 2022 Date Revised: N/A Location: Washington, DC The Mission Polaris is leading a data-driven social justice movement to fight sex and labor trafficking at the massive scale of the problem – 25 million people worldwide deprived of the freedom to choose how they live and work. For more than a decade, Polaris has assisted thousands of victims and survivors through the U.S. National Human Trafficking Hotline, helped ensure countless traffickers were held accountable and built the largest known U.S. data set on actual trafficking experiences. With the guidance of survivors, we use that data to improve the way trafficking is identified, how victims and survivors are assisted, and how communities, businesses and governments can prevent human trafficking by transforming the underlying inequities and oppression that make it possible. The Learning, Innovation, and Data Systems team has the exciting task of utilizing research and data to inform and guide our approach to the fight against human trafficking with the ultimate end goal of eradicating the crime of modern-day slavery. About Opportunity The Social Science Researcher is a highly self-motivated, creative, and methodical professional.

There are 40 million people in slavery today. SDG 8.7 is a commitment to end modern slavery, with the ambition to reduce that number by 10,000 people every day. James Cockayne, Director, Centre for Policy Research, United Nations University, is confident that we are "nowhere near" that target. The reasons for this systemic and enduring failure are the result of the "mispricing" of labour, where true social costs are not quantified. Worse still, companies are rewarded for driving down their labour costs.

CHENNAI, India, Aug 9 (Thomson Reuters Foundation) - With more than 20 million humans working as modern slaves, a technology developer is hoping artificial intelligence will help clean up the world's supply chains and root out worker abuse. Developer Padmini Ranganathan said mobile phones, media reports and surveillance cameras can all be mined for real-time data, which can in turn be fed into machines to create artificial intelligence (AI) that helps companies see more clearly what is happening down the line. "The time to do this now is better than ever before, with so many countries and companies focusing on modern slavery," she said in an interview with the Thomson Reuters Foundation. "At the start of the decade, the driving force for compliance was fear of being penalised. Now companies are looking at social impact and saying they want to do this."