This chapter introduces a conceptual framework for qualitative risk assessment of AI, particularly in the context of the EU AI Act. The framework addresses the complexities of legal compliance and fundamental rights protection by itegrating definitional balancing and defeasible reasoning. Definitional balancing employs proportionality analysis to resolve conflicts between competing rights, while defeasible reasoning accommodates the dynamic nature of legal decision-making. Our approach stresses the need for an analysis of AI deployment scenarios and for identifying potential legal violations and multi-layered impacts on fundamental rights. On the basis of this analysis, we provide philosophical foundations for a logical account of AI risk analysis. In particular, we consider the basic building blocks for conceptually grasping the interaction between AI deployment scenarios and fundamental rights, incorporating in defeasible reasoning definitional balancing and arguments about the contextual promotion or demotion of rights. This layered approach allows for more operative models of assessment of both high-risk AI systems and General Purpose AI (GPAI) systems, emphasizing the broader applicability of the latter. Future work aims to develop a formal model and effective algorithms to enhance AI risk assessment, bridging theoretical insights with practical applications to support responsible AI governance.

This paper argues that a dataset's legal risk cannot be accurately assessed by its license terms alone; instead, tracking dataset redistribution and its full lifecycle is essential. However, this process is too complex for legal experts to handle manually at scale. Tracking dataset provenance, verifying redistribution rights, and assessing evolving legal risks across multiple stages require a level of precision and efficiency that exceeds human capabilities. Addressing this challenge effectively demands AI agents that can systematically trace dataset redistribution, analyze compliance, and identify legal risks. We develop an automated data compliance system called NEXUS and show that AI can perform these tasks with higher accuracy, efficiency, and cost-effectiveness than human experts. Our massive legal analysis of 17,429 unique entities and 8,072 license terms using this approach reveals the discrepancies in legal rights between the original datasets before redistribution and their redistributed subsets, underscoring the necessity of the data lifecycle-aware compliance. For instance, we find that out of 2,852 datasets with commercially viable individual license terms, only 605 (21%) are legally permissible for commercialization. This work sets a new standard for AI data governance, advocating for a framework that systematically examines the entire lifecycle of dataset redistribution to ensure transparent, legal, and responsible dataset management.

Hugging Face has found that data sets have better documentation when they are open, consistently used, and shared, said Yacine Jernite, leader of its machine learning and society team. The open source company has prioritized efforts, like automatically suggesting meta data, to improve documentation. Even with imperfect annotation, openly accessible data sets are the first meaningful step toward more transparency in the field, he said.

Algorithmic bias never comes from nowhere, of course; it begins with biased data. Human data is naturally skewed, with conscious and unconscious biases leaving their fingerprints all over datasets. The trick comes in spotting – and removing – any biases from your data and apps before it's too late. Despite the fact innovation often outpaces legislation, are organisations getting twitchy about the legal risks of AI? "The legal risk is simple but serious," Simon Carroll, dispute resolution partner at legal firm, BP Collins, tells IT Pro. "Biased algorithmic decisions could breach the Equality Act 2010, which aims to protect from unlawful discrimination by automated systems as well as by people."

As AI is making its way into more companies, the board and senior executives need to mitigate the risk of their AI-based systems. One area of risk includes the reputational, regulatory and legal risks of AI-led ethical decisions. AI-based systems are often faced with making decisions that were not built into their models--decisions representing ethical dilemmas. For example, suppose a company builds an AI-based system to optimize the number of advertisements we see. In that case, the AI may encourage incendiary content that causes users to get angry and comment and post their own opinions.

Parity is among a growing crop of startups promising organizations ways to develop, monitor, and fix their AI models. They offer a range of products and services from bias-mitigation tools to explainability platforms. Initially most of their clients came from heavily regulated industries like finance and health care. But increased research and media attention on issues of bias, privacy, and transparency have shifted the focus of the conversation. New clients are often simply worried about being responsible, while others want to "future proof" themselves in anticipation of regulation.

With artificial intelligence becoming all the rage across the HR world, there clearly appears to be rewards from using AI to find and land the best talent. Do AI-based tools in recruiting and hiring really outperform human decision-making? And if they do, could they potentially expose HR and employers to the same types of discrimination issues that can impact hiring driven by people, not algorithms? Right now, the legal landscape in the U.S. has yet to catch up those critical considerations. In Europe, for instance, the U.K.'s Information Commissioner's Office recently released guidance for organizations about transparency within AI decision-making.

Artificial intelligence (AI) could contribute up to $15.7 trillion to the global economy by 2030, according to PwC. Meanwhile, Forrester has warned that cybercriminals can weaponize and exploit AI to attack businesses. And we've all seen the worrisome headlines about how AI is going to take over our jobs. Toss in references to machine learning, artificial neural networks (ANN), and multilayer ANN (aka deep learning), and it's difficult to know what to think about AI and how CISOs can assess whether the emerging technology is right for their organizations. Gartner offers some suggestions on how to fight the FUD, as do Gartner security analysts Dr. Anton Chuvakin and Augusto Barros, who help demystifying AI in their blogs (not without a good note of sarcasm).

Businesses in a wide range of industry sectors are pursuing AI strategies. The Artificial Intelligence microsite includes contributions from our global team of experts, and provides in-depth perspectives on the complex ethical and related legal risks that must be managed by businesses developing, acquiring or implementing AI - across a number of industry sectors. A decision to adopt AI can raise fundamental ethical and moral issues for society. These complex issues are of vital importance to our future, but they are not typically the domain of lawyers.

Everyone now is learning, or claiming to learn, Deep Learning (DL), the only field of Artificial Intelligence (AI) that went viral. Paid and free DL courses count 100,000s of students of all ages. Too many startups and products are named "deep-something", just as buzzword: very few are using DL really. Most ignore that DL is the 1% of the Machine Learning (ML) field, and that ML is the 1% of the AI field. What's used in practice for most "AI" tasks is not DL. A "DL-only expert" is not a "whole AI expert".