-
A pragmatic approach to regulating AI agents
Authors:
Philipp Hacker,
Matthias Holweg
Abstract:
The current advancement in and deployment of agentic AI systems has created a set of key challenges for the legal frameworks that govern their use. We cover two central components: first, the regulatory classification of agents under the EU AI Act, and second, the legal status and validity of autonomous actions within the established framework of EU contract law. We argue that the unique capacity…
▽ More
The current advancement in and deployment of agentic AI systems has created a set of key challenges for the legal frameworks that govern their use. We cover two central components: first, the regulatory classification of agents under the EU AI Act, and second, the legal status and validity of autonomous actions within the established framework of EU contract law. We argue that the unique capacity of agents to autonomously reason, plan, and execute tasks across disparate external systems necessitates a fundamental shift in oversight toward the orchestration layer, where multi-agent interactions introduce novel risks of misalignment. While agents generally utilise general-purpose AI models, we posit that their structural complexity and cross-system permeability require them to be regulated as "AI systems" with distinct obligations under the AI Act. Consequently, our proposals highlight the need for robust accountability mechanisms to manage this heightened autonomy. On the contractual side, we advocate for a "traffic light" system of staggered task authorization based on operational risk and the creation of a statutory list of non-delegable legal acts. By implementing these measures, we provide a pragmatic pathway to ensure that the increasing autonomy of AI agents remains firmly anchored in human accountability and existing legal standards
△ Less
Submitted 16 April, 2026;
originally announced April 2026.
-
The Global Landscape of Environmental AI Regulation: From the Cost of Reasoning to a Right to Green AI
Authors:
Kai Ebert,
Boris Gamazaychikov,
Philipp Hacker,
Sasha Luccioni
Abstract:
Artificial intelligence (AI) systems impose substantial and growing environmental costs, yet transparency about these impacts has declined even as their deployment has accelerated. This paper makes three contributions. First, we collate empirical evidence that generative Web search and reasoning models - which have proliferated in 2025 - come with much higher cumulative environmental impacts than…
▽ More
Artificial intelligence (AI) systems impose substantial and growing environmental costs, yet transparency about these impacts has declined even as their deployment has accelerated. This paper makes three contributions. First, we collate empirical evidence that generative Web search and reasoning models - which have proliferated in 2025 - come with much higher cumulative environmental impacts than previous generations of AI approaches. Second, we map the global regulatory landscape across eleven jurisdictions and find that the manner in which environmental governance operates (predominantly at the facility-level rather than the model-level, with a focus on training rather than inference, with limited AI-specific energy disclosure requirements outside the EU) limits its applicability. Third, to address this, we propose a three-pronged policy response: mandatory model-level transparency that covers inference consumption, benchmarks, and compute locations; user rights to opt out of unnecessary generative AI integration and to select environmentally optimized models; and international coordination to prevent regulatory arbitrage. We conclude with concrete legislative proposals - including amendments to the EU AI Act, Consumer Rights Directive, and Digital Services Act - that could serve as templates for other jurisdictions.
△ Less
Submitted 29 May, 2026; v1 submitted 10 February, 2026;
originally announced March 2026.
-
AI, Digital Platforms, and the New Systemic Risk
Authors:
Philipp Hacker,
Lilian Edwards,
Atoosa Kasirzadeh
Abstract:
As artificial intelligence (AI) becomes increasingly embedded in digital, social, and institutional infrastructures, and AI and platforms are merged into hybrid structures, systemic risk has emerged as a critical but undertheorized challenge. In this paper, we develop a rigorous framework for understanding systemic risk in AI, platform, and hybrid system governance, drawing on insights from financ…
▽ More
As artificial intelligence (AI) becomes increasingly embedded in digital, social, and institutional infrastructures, and AI and platforms are merged into hybrid structures, systemic risk has emerged as a critical but undertheorized challenge. In this paper, we develop a rigorous framework for understanding systemic risk in AI, platform, and hybrid system governance, drawing on insights from finance, complex systems theory, climate change, and cybersecurity - domains where systemic risk has already shaped regulatory responses. We argue that recent legislation, including the EU's AI Act and Digital Services Act (DSA), invokes systemic risk but relies on narrow or ambiguous characterizations of this notion, sometimes reducing this risk to specific capabilities present in frontier AI models, or to harms occurring in economic market settings. The DSA, we show, actually does a better job at identifying systemic risk than the more recent AI Act. Our framework highlights novel risk pathways, including the possibility of systemic failures arising from the interaction of multiple AI agents. We identify four levels of AI-related systemic risk and emphasize that discrimination at scale and systematic hallucinations, despite their capacity to destabilize institutions and fundamental rights, may not fall under current legal definitions, given the AI Act's focus on frontier model capabilities. We then test the DSA, the AI Act, and our own framework on five key examples, and propose reforms that broaden systemic risk assessments, strengthen coordination between regulatory regimes, and explicitly incorporate collective harms.
△ Less
Submitted 23 May, 2026; v1 submitted 22 September, 2025;
originally announced September 2025.
-
The Goldilocks zone of governing technology: Leveraging uncertainty for responsible quantum practices
Authors:
Miriam Meckel,
Philipp Hacker,
Lea Steinacker,
Aurelija Lukoseviciene,
Surjo R. Soekadar,
Jacob Slosser,
Gina-Maria Poehlmann
Abstract:
Emerging technologies challenge conventional governance approaches, especially when uncertainty is not a temporary obstacle but a foundational feature as in quantum computing. This paper reframes uncertainty from a governance liability to a generative force, using the paradigms of quantum mechanics to propose adaptive, probabilistic frameworks for responsible innovation. We identify three interdep…
▽ More
Emerging technologies challenge conventional governance approaches, especially when uncertainty is not a temporary obstacle but a foundational feature as in quantum computing. This paper reframes uncertainty from a governance liability to a generative force, using the paradigms of quantum mechanics to propose adaptive, probabilistic frameworks for responsible innovation. We identify three interdependent layers of uncertainty--physical, technical, and societal--central to the evolution of quantum technologies. The proposed Quantum Risk Simulator (QRS) serves as a conceptual example, an imaginative blueprint rather than a prescriptive tool, meant to illustrate how probabilistic reasoning could guide dynamic, uncertainty-based governance. By foregrounding epistemic and ontological ambiguity, and drawing analogies from cognitive neuroscience and predictive processing, we suggest a new model of governance aligned with the probabilistic essence of quantum systems. This model, we argue, is especially promising for the European Union as a third way between laissez-faire innovation and state-led control, offering a flexible yet responsible pathway for regulating quantum and other frontier technologies.
△ Less
Submitted 17 July, 2025;
originally announced July 2025.
-
AI, Climate, and Regulation: From Data Centers to the AI Act
Authors:
Kai Ebert,
Nicolas Alder,
Ralf Herbrich,
Philipp Hacker
Abstract:
We live in a world that is experiencing an unprecedented boom of AI applications that increasingly penetrate and enhance all sectors of private and public life, from education, media, medicine, and mobility to the industrial and professional workspace, and -- potentially particularly consequentially -- robotics. As this world is simultaneously grappling with climate change, the climate and environ…
▽ More
We live in a world that is experiencing an unprecedented boom of AI applications that increasingly penetrate and enhance all sectors of private and public life, from education, media, medicine, and mobility to the industrial and professional workspace, and -- potentially particularly consequentially -- robotics. As this world is simultaneously grappling with climate change, the climate and environmental implications of the development and use of AI have become an important subject of public and academic debate. In this paper, we aim to provide guidance on the climate-related regulation for data centers and AI specifically, and discuss how to operationalize these requirements. We also highlight challenges and room for improvement, and make a number of policy proposals to this end. In particular, we propose a specific interpretation of the AI Act to bring reporting on the previously unadressed energy consumption from AI inferences back into the scope. We also find that the AI Act fails to address indirect greenhouse gas emissions from AI applications. Furthermore, for the purpose of energy consumption reporting, we compare levels of measurement within data centers and recommend measurement at the cumulative server level. We also argue for an interpretation of the AI Act that includes environmental concerns in the mandatory risk assessment (sustainability risk assessment, SIA), and provide guidance on its operationalization. The EU data center regulation proves to be a good first step but requires further development by including binding renewable energy and efficiency targets for data centers. Overall, we make twelve concrete policy proposals, in four main areas: Energy and Environmental Reporting Obligations; Legal and Regulatory Clarifications; Transparency and Accountability Mechanisms; and Future Far-Reaching Measures beyond Transparency.
△ Less
Submitted 25 May, 2025; v1 submitted 9 October, 2024;
originally announced October 2024.
-
AI, Climate, and Transparency: Operationalizing and Improving the AI Act
Authors:
Nicolas Alder,
Kai Ebert,
Ralf Herbrich,
Philipp Hacker
Abstract:
This paper critically examines the AI Act's provisions on climate-related transparency, highlighting significant gaps and challenges in its implementation. We identify key shortcomings, including the exclusion of energy consumption during AI inference, the lack of coverage for indirect greenhouse gas emissions from AI applications, and the lack of standard reporting methodology. The paper proposes…
▽ More
This paper critically examines the AI Act's provisions on climate-related transparency, highlighting significant gaps and challenges in its implementation. We identify key shortcomings, including the exclusion of energy consumption during AI inference, the lack of coverage for indirect greenhouse gas emissions from AI applications, and the lack of standard reporting methodology. The paper proposes a novel interpretation to bring inference-related energy use back within the Act's scope and advocates for public access to climate-related disclosures to foster market accountability and public scrutiny. Cumulative server level energy reporting is recommended as the most suitable method. We also suggests broader policy changes, including sustainability risk assessments and renewable energy targets, to better address AI's environmental impact.
△ Less
Submitted 28 August, 2024;
originally announced September 2024.
-
A Robust Governance for the AI Act: AI Office, AI Board, Scientific Panel, and National Authorities
Authors:
Claudio Novelli,
Philipp Hacker,
Jessica Morley,
Jarle Trondal,
Luciano Floridi
Abstract:
Regulation is nothing without enforcement. This particularly holds for the dynamic field of emerging technologies. Hence, this article has two ambitions. First, it explains how the EU's new Artificial Intelligence Act (AIA) will be implemented and enforced by various institutional bodies, thus clarifying the governance framework of the AIA. Second, it proposes a normative model of governance, prov…
▽ More
Regulation is nothing without enforcement. This particularly holds for the dynamic field of emerging technologies. Hence, this article has two ambitions. First, it explains how the EU's new Artificial Intelligence Act (AIA) will be implemented and enforced by various institutional bodies, thus clarifying the governance framework of the AIA. Second, it proposes a normative model of governance, providing recommendations to ensure uniform and coordinated execution of the AIA and the fulfilment of the legislation. Taken together, the article explores how the AIA may be implemented by national and EU institutional bodies, encompassing longstanding bodies, such as the European Commission, and those newly established under the AIA, such as the AI Office. It investigates their roles across supranational and national levels, emphasizing how EU regulations influence institutional structures and operations. These regulations may not only directly dictate the structural design of institutions but also indirectly request administrative capacities needed to enforce the AIA.
△ Less
Submitted 26 October, 2024; v1 submitted 11 May, 2024;
originally announced July 2024.
-
Generative Discrimination: What Happens When Generative AI Exhibits Bias, and What Can Be Done About It
Authors:
Philipp Hacker
Abstract:
As generative Artificial Intelligence (genAI) technologies proliferate across sectors, they offer significant benefits but also risk exacerbating discrimination. This chapter explores how genAI intersects with non-discrimination laws, identifying shortcomings and suggesting improvements. It highlights two main types of discriminatory outputs: (i) demeaning and abusive content and (ii) subtler bias…
▽ More
As generative Artificial Intelligence (genAI) technologies proliferate across sectors, they offer significant benefits but also risk exacerbating discrimination. This chapter explores how genAI intersects with non-discrimination laws, identifying shortcomings and suggesting improvements. It highlights two main types of discriminatory outputs: (i) demeaning and abusive content and (ii) subtler biases due to inadequate representation of protected groups, which may not be overtly discriminatory in individual cases but have cumulative discriminatory effects. For example, genAI systems may predominantly depict white men when asked for images of people in important jobs.
This chapter examines these issues, categorizing problematic outputs into three legal categories: discriminatory content; harassment; and legally hard cases like unbalanced content, harmful stereotypes or misclassification. It argues for holding genAI providers and deployers liable for discriminatory outputs and highlights the inadequacy of traditional legal frameworks to address genAI-specific issues. The chapter suggests updating EU laws, including the AI Act, to mitigate biases in training and input data, mandating testing and auditing, and evolving legislation to enforce standards for bias mitigation and inclusivity as technology advances.
△ Less
Submitted 26 June, 2024;
originally announced July 2024.
-
Non-discrimination law in Europe: a primer for non-lawyers
Authors:
Frederik Zuiderveen Borgesius,
Nina Baranowska,
Philipp Hacker,
Alessandro Fabris
Abstract:
This brief paper provides an introduction to non-discrimination law in Europe. It answers the questions: What are the key characteristics of non-discrimination law in Europe, and how do the different statutes relate to one another? Our main target group is computer scientists and users of artificial intelligence (AI) interested in an introduction to non-discrimination law in Europe. Notably, non-d…
▽ More
This brief paper provides an introduction to non-discrimination law in Europe. It answers the questions: What are the key characteristics of non-discrimination law in Europe, and how do the different statutes relate to one another? Our main target group is computer scientists and users of artificial intelligence (AI) interested in an introduction to non-discrimination law in Europe. Notably, non-discrimination law in Europe differs significantly from non-discrimination law in other countries, such as the US. We aim to describe the law in such a way that non-lawyers and non-European lawyers can easily grasp its contents and challenges. The paper shows that the human right to non-discrimination, to some extent, protects individuals against private actors, such as companies. We introduce the EU-wide non-discrimination rules which are included in a number of EU directives, and also explain the difference between direct and indirect discrimination. Significantly, an organization can be fined for indirect discrimination even if the company, or its AI system, discriminated by accident. The last section broadens the horizon to include bias-relevant law and cases from the GDPR, the EU AI Act, and related statutes. Finally, we give reading tips for those inclined to learn more about non-discrimination law in Europe.
△ Less
Submitted 17 April, 2024; v1 submitted 12 April, 2024;
originally announced April 2024.
-
Generative AI in EU Law: Liability, Privacy, Intellectual Property, and Cybersecurity
Authors:
Claudio Novelli,
Federico Casolari,
Philipp Hacker,
Giorgio Spedicato,
Luciano Floridi
Abstract:
The advent of Generative AI, particularly through Large Language Models (LLMs) like ChatGPT and its successors, marks a paradigm shift in the AI landscape. Advanced LLMs exhibit multimodality, handling diverse data formats, thereby broadening their application scope. However, the complexity and emergent autonomy of these models introduce challenges in predictability and legal compliance. This pape…
▽ More
The advent of Generative AI, particularly through Large Language Models (LLMs) like ChatGPT and its successors, marks a paradigm shift in the AI landscape. Advanced LLMs exhibit multimodality, handling diverse data formats, thereby broadening their application scope. However, the complexity and emergent autonomy of these models introduce challenges in predictability and legal compliance. This paper delves into the legal and regulatory implications of Generative AI and LLMs in the European Union context, analyzing aspects of liability, privacy, intellectual property, and cybersecurity. It critically examines the adequacy of the existing and proposed EU legislation, including the Artificial Intelligence Act (AIA) draft, in addressing the unique challenges posed by Generative AI in general and LLMs in particular. The paper identifies potential gaps and shortcomings in the legislative framework and proposes recommendations to ensure the safe and compliant deployment of generative models, ensuring they align with the EU's evolving digital landscape and legal standards.
△ Less
Submitted 15 March, 2024; v1 submitted 14 January, 2024;
originally announced January 2024.
-
AI Regulation in Europe: From the AI Act to Future Regulatory Challenges
Authors:
Philipp Hacker
Abstract:
This chapter provides a comprehensive discussion on AI regulation in the European Union, contrasting it with the more sectoral and self-regulatory approach in the UK. It argues for a hybrid regulatory strategy that combines elements from both philosophies, emphasizing the need for agility and safe harbors to ease compliance. The paper examines the AI Act as a pioneering legislative effort to addre…
▽ More
This chapter provides a comprehensive discussion on AI regulation in the European Union, contrasting it with the more sectoral and self-regulatory approach in the UK. It argues for a hybrid regulatory strategy that combines elements from both philosophies, emphasizing the need for agility and safe harbors to ease compliance. The paper examines the AI Act as a pioneering legislative effort to address the multifaceted challenges posed by AI, asserting that, while the Act is a step in the right direction, it has shortcomings that could hinder the advancement of AI technologies. The paper also anticipates upcoming regulatory challenges, such as the management of toxic content, environmental concerns, and hybrid threats. It advocates for immediate action to create protocols for regulated access to high-performance, potentially open-source AI systems. Although the AI Act is a significant legislative milestone, it needs additional refinement and global collaboration for the effective governance of rapidly evolving AI technologies.
△ Less
Submitted 6 October, 2023;
originally announced October 2023.
-
Fairness and Bias in Algorithmic Hiring: a Multidisciplinary Survey
Authors:
Alessandro Fabris,
Nina Baranowska,
Matthew J. Dennis,
David Graus,
Philipp Hacker,
Jorge Saldivar,
Frederik Zuiderveen Borgesius,
Asia J. Biega
Abstract:
Employers are adopting algorithmic hiring technology throughout the recruitment pipeline. Algorithmic fairness is especially applicable in this domain due to its high stakes and structural inequalities. Unfortunately, most work in this space provides partial treatment, often constrained by two competing narratives, optimistically focused on replacing biased recruiter decisions or pessimistically p…
▽ More
Employers are adopting algorithmic hiring technology throughout the recruitment pipeline. Algorithmic fairness is especially applicable in this domain due to its high stakes and structural inequalities. Unfortunately, most work in this space provides partial treatment, often constrained by two competing narratives, optimistically focused on replacing biased recruiter decisions or pessimistically pointing to the automation of discrimination. Whether, and more importantly what types of, algorithmic hiring can be less biased and more beneficial to society than low-tech alternatives currently remains unanswered, to the detriment of trustworthiness. This multidisciplinary survey caters to practitioners and researchers with a balanced and integrated coverage of systems, biases, measures, mitigation strategies, datasets, and legal aspects of algorithmic hiring and fairness. Our work supports a contextualized understanding and governance of this technology by highlighting current opportunities and limitations, providing recommendations for future work to ensure shared benefits for all stakeholders.
△ Less
Submitted 27 June, 2025; v1 submitted 25 September, 2023;
originally announced September 2023.
-
Sustainable AI Regulation
Authors:
Philipp Hacker
Abstract:
Current proposals for AI regulation, in the EU and beyond, aim to spur AI that is trustworthy (e.g., AI Act) and accountable (e.g., AI Liability) What is missing, however, is a robust regulatory discourse and roadmap to make AI, and technology more broadly, environmentally sustainable. This paper aims to take first steps to fill this gap. The ICT sector contributes up to 3.9 percent of global gree…
▽ More
Current proposals for AI regulation, in the EU and beyond, aim to spur AI that is trustworthy (e.g., AI Act) and accountable (e.g., AI Liability) What is missing, however, is a robust regulatory discourse and roadmap to make AI, and technology more broadly, environmentally sustainable. This paper aims to take first steps to fill this gap. The ICT sector contributes up to 3.9 percent of global greenhouse gas (GHG) emissions-more than global air travel at 2.5 percent. The carbon footprint and water consumption of AI, especially large-scale generative models like GPT-4, raise significant sustainability concerns. The paper is the first to assess how current and proposed technology regulations, including EU environmental law, the General Data Protection Regulation (GDPR), and the AI Act, could be adjusted to better account for environmental sustainability. The GDPR, for instance, could be interpreted to limit certain individual rights like the right to erasure if these rights significantly conflict with broader sustainability goals. In a second step, the paper suggests a multi-faceted approach to achieve sustainable AI regulation. It advocates for transparency mechanisms, such as disclosing the GHG footprint of AI systems, as laid out in the proposed EU AI Act. However, sustainable AI regulation must go beyond mere transparency. The paper proposes a regulatory toolkit comprising co-regulation, sustainability-by-design principles, restrictions on training data, and consumption caps, including integration into the EU Emissions Trading Scheme. Finally, the paper argues that this regulatory toolkit could serve as a blueprint for regulating other high-emission technologies and infrastructures like blockchain, Metaverse applications, and data centers. The framework aims to cohesively address the crucial dual challenges of our era: digital transformation and climate change mitigation.
△ Less
Submitted 6 March, 2024; v1 submitted 31 May, 2023;
originally announced June 2023.
-
Regulating ChatGPT and other Large Generative AI Models
Authors:
Philipp Hacker,
Andreas Engel,
Marco Mauer
Abstract:
Large generative AI models (LGAIMs), such as ChatGPT, GPT-4 or Stable Diffusion, are rapidly transforming the way we communicate, illustrate, and create. However, AI regulation, in the EU and beyond, has primarily focused on conventional AI models, not LGAIMs. This paper will situate these new generative models in the current debate on trustworthy AI regulation, and ask how the law can be tailored…
▽ More
Large generative AI models (LGAIMs), such as ChatGPT, GPT-4 or Stable Diffusion, are rapidly transforming the way we communicate, illustrate, and create. However, AI regulation, in the EU and beyond, has primarily focused on conventional AI models, not LGAIMs. This paper will situate these new generative models in the current debate on trustworthy AI regulation, and ask how the law can be tailored to their capabilities. After laying technical foundations, the legal part of the paper proceeds in four steps, covering (1) direct regulation, (2) data protection, (3) content moderation, and (4) policy proposals. It suggests a novel terminology to capture the AI value chain in LGAIM settings by differentiating between LGAIM developers, deployers, professional and non-professional users, as well as recipients of LGAIM output. We tailor regulatory duties to these different actors along the value chain and suggest strategies to ensure that LGAIMs are trustworthy and deployed for the benefit of society at large. Rules in the AI Act and other direct regulation must match the specificities of pre-trained models. The paper argues for three layers of obligations concerning LGAIMs (minimum standards for all LGAIMs; high-risk obligations for high-risk use cases; collaborations along the AI value chain). In general, regulation should focus on concrete high-risk applications, and not the pre-trained model itself, and should include (i) obligations regarding transparency and (ii) risk management. Non-discrimination provisions (iii) may, however, apply to LGAIM developers. Lastly, (iv) the core of the DSA content moderation rules should be expanded to cover LGAIMs. This includes notice and action mechanisms, and trusted flaggers. In all areas, regulators and lawmakers need to act fast to keep track with the dynamics of ChatGPT et al.
△ Less
Submitted 12 May, 2023; v1 submitted 5 February, 2023;
originally announced February 2023.
-
Regulating Gatekeeper AI and Data: Transparency, Access, and Fairness under the DMA, the GDPR, and beyond
Authors:
Philipp Hacker,
Johann Cordes,
Janina Rochon
Abstract:
Artificial intelligence is not only increasingly used in business and administration contexts, but a race for its regulation is also underway, with the EU spearheading the efforts. Contrary to existing literature, this article suggests, however, that the most far-reaching and effective EU rules for AI applications in the digital economy will not be contained in the proposed AI Act - but have just…
▽ More
Artificial intelligence is not only increasingly used in business and administration contexts, but a race for its regulation is also underway, with the EU spearheading the efforts. Contrary to existing literature, this article suggests, however, that the most far-reaching and effective EU rules for AI applications in the digital economy will not be contained in the proposed AI Act - but have just been enacted in the Digital Markets Act. We analyze the impact of the DMA and related EU acts on AI models and their underlying data across four key areas: disclosure requirements; the regulation of AI training data; access rules; and the regime for fair rankings. The paper demonstrates that fairness, in the sense of the DMA, goes beyond traditionally protected categories of non-discrimination law on which scholarship at the intersection of AI and law has so far largely focused on. Rather, we draw on competition law and the FRAND criteria known from intellectual property law to interpret and refine the DMA provisions on fair rankings. Moreover, we show how, based on CJEU jurisprudence, a coherent interpretation of the concept of non-discrimination in both traditional non-discrimination and competition law may be found. The final part sketches specific proposals for a comprehensive framework of transparency, access, and fairness under the DMA and beyond.
△ Less
Submitted 24 August, 2023; v1 submitted 9 December, 2022;
originally announced December 2022.
-
Beyond Incompatibility: Trade-offs between Mutually Exclusive Fairness Criteria in Machine Learning and Law
Authors:
Meike Zehlike,
Alex Loosley,
Håkan Jonsson,
Emil Wiedemann,
Philipp Hacker
Abstract:
Fair and trustworthy AI is becoming ever more important in both machine learning and legal domains. One important consequence is that decision makers must seek to guarantee a 'fair', i.e., non-discriminatory, algorithmic decision procedure. However, there are several competing notions of algorithmic fairness that have been shown to be mutually incompatible under realistic factual assumptions. This…
▽ More
Fair and trustworthy AI is becoming ever more important in both machine learning and legal domains. One important consequence is that decision makers must seek to guarantee a 'fair', i.e., non-discriminatory, algorithmic decision procedure. However, there are several competing notions of algorithmic fairness that have been shown to be mutually incompatible under realistic factual assumptions. This concerns, for example, the widely used fairness measures of 'calibration within groups' and 'balance for the positive/negative class'. In this paper, we present a novel algorithm (FAir Interpolation Method: FAIM) for continuously interpolating between these three fairness criteria. Thus, an initially unfair prediction can be remedied to, at least partially, meet a desired, weighted combination of the respective fairness conditions. We demonstrate the effectiveness of our algorithm when applied to synthetic data, the COMPAS data set, and a new, real-world data set from the e-commerce sector. Finally, we discuss to what extent FAIM can be harnessed to comply with conflicting legal obligations. The analysis suggests that it may operationalize duties in traditional legal fields, such as credit scoring and criminal justice proceedings, but also for the latest AI regulations put forth in the EU, like the Digital Markets Act and the recently enacted AI Act.
△ Less
Submitted 20 December, 2024; v1 submitted 1 December, 2022;
originally announced December 2022.
-
The European AI Liability Directives -- Critique of a Half-Hearted Approach and Lessons for the Future
Authors:
Philipp Hacker
Abstract:
As ChatGPT et al. conquer the world, the optimal liability framework for AI systems remains an unsolved problem across the globe. In a much-anticipated move, the European Commission advanced two proposals outlining the European approach to AI liability in September 2022: a novel AI Liability Directive and a revision of the Product Liability Directive. They constitute the final cornerstone of EU AI…
▽ More
As ChatGPT et al. conquer the world, the optimal liability framework for AI systems remains an unsolved problem across the globe. In a much-anticipated move, the European Commission advanced two proposals outlining the European approach to AI liability in September 2022: a novel AI Liability Directive and a revision of the Product Liability Directive. They constitute the final cornerstone of EU AI regulation. Crucially, the liability proposals and the EU AI Act are inherently intertwined: the latter does not contain any individual rights of affected persons, and the former lack specific, substantive rules on AI development and deployment. Taken together, these acts may well trigger a Brussels Effect in AI regulation, with significant consequences for the US and beyond.
This paper makes three novel contributions. First, it examines in detail the Commission proposals and shows that, while making steps in the right direction, they ultimately represent a half-hearted approach: if enacted as foreseen, AI liability in the EU will primarily rest on disclosure of evidence mechanisms and a set of narrowly defined presumptions concerning fault, defectiveness and causality. Hence, second, the article suggests amendments, which are collected in an Annex at the end of the paper. Third, based on an analysis of the key risks AI poses, the final part of the paper maps out a road for the future of AI liability and regulation, in the EU and beyond. This includes: a comprehensive framework for AI liability; provisions to support innovation; an extension to non-discrimination/algorithmic fairness, as well as explainable AI; and sustainability. I propose to jump-start sustainable AI regulation via sustainability impact assessments in the AI Act and sustainable design defects in the liability regime. In this way, the law may help spur not only fair AI and XAI, but potentially also sustainable AI (SAI).
△ Less
Submitted 28 July, 2023; v1 submitted 25 November, 2022;
originally announced November 2022.
-
Bounds on quantum evolution complexity via lattice cryptography
Authors:
Ben Craps,
Marine De Clerck,
Oleg Evnin,
Philip Hacker,
Maxim Pavlov
Abstract:
We address the difference between integrable and chaotic motion in quantum theory as manifested by the complexity of the corresponding evolution operators. Complexity is understood here as the shortest geodesic distance between the time-dependent evolution operator and the origin within the group of unitaries. (An appropriate `complexity metric' must be used that takes into account the relative di…
▽ More
We address the difference between integrable and chaotic motion in quantum theory as manifested by the complexity of the corresponding evolution operators. Complexity is understood here as the shortest geodesic distance between the time-dependent evolution operator and the origin within the group of unitaries. (An appropriate `complexity metric' must be used that takes into account the relative difficulty of performing `nonlocal' operations that act on many degrees of freedom at once.) While simply formulated and geometrically attractive, this notion of complexity is numerically intractable save for toy models with Hilbert spaces of very low dimensions. To bypass this difficulty, we trade the exact definition in terms of geodesics for an upper bound on complexity, obtained by minimizing the distance over an explicitly prescribed infinite set of curves, rather than over all possible curves. Identifying this upper bound turns out equivalent to the closest vector problem (CVP) previously studied in integer optimization theory, in particular, in relation to lattice-based cryptography. Effective approximate algorithms are hence provided by the existing mathematical considerations, and they can be utilized in our analysis of the upper bounds on quantum evolution complexity. The resulting algorithmically implemented complexity bound systematically assigns lower values to integrable than to chaotic systems, as we demonstrate by explicit numerical work for Hilbert spaces of dimensions up to ~10^4.
△ Less
Submitted 11 October, 2022; v1 submitted 28 February, 2022;
originally announced February 2022.
-
Towards a Flexible Framework for Algorithmic Fairness
Authors:
Philip Hacker,
Emil Wiedemann,
Meike Zehlike
Abstract:
Increasingly, scholars seek to integrate legal and technological insights to combat bias in AI systems. In recent years, many different definitions for ensuring non-discrimination in algorithmic decision systems have been put forward. In this paper, we first briefly describe the EU law framework covering cases of algorithmic discrimination. Second, we present an algorithm that harnesses optimal tr…
▽ More
Increasingly, scholars seek to integrate legal and technological insights to combat bias in AI systems. In recent years, many different definitions for ensuring non-discrimination in algorithmic decision systems have been put forward. In this paper, we first briefly describe the EU law framework covering cases of algorithmic discrimination. Second, we present an algorithm that harnesses optimal transport to provide a flexible framework to interpolate between different fairness definitions. Third, we show that important normative and legal challenges remain for the implementation of algorithmic fairness interventions in real-world scenarios. Overall, the paper seeks to contribute to the quest for flexible technical frameworks that can be adapted to varying legal and normative fairness constraints.
△ Less
Submitted 15 October, 2020;
originally announced October 2020.
-
Matching Code and Law: Achieving Algorithmic Fairness with Optimal Transport
Authors:
Meike Zehlike,
Philipp Hacker,
Emil Wiedemann
Abstract:
Increasingly, discrimination by algorithms is perceived as a societal and legal problem. As a response, a number of criteria for implementing algorithmic fairness in machine learning have been developed in the literature. This paper proposes the Continuous Fairness Algorithm (CFA$θ$) which enables a continuous interpolation between different fairness definitions. More specifically, we make three m…
▽ More
Increasingly, discrimination by algorithms is perceived as a societal and legal problem. As a response, a number of criteria for implementing algorithmic fairness in machine learning have been developed in the literature. This paper proposes the Continuous Fairness Algorithm (CFA$θ$) which enables a continuous interpolation between different fairness definitions. More specifically, we make three main contributions to the existing literature. First, our approach allows the decision maker to continuously vary between specific concepts of individual and group fairness. As a consequence, the algorithm enables the decision maker to adopt intermediate ``worldviews'' on the degree of discrimination encoded in algorithmic processes, adding nuance to the extreme cases of ``we're all equal'' (WAE) and ``what you see is what you get'' (WYSIWYG) proposed so far in the literature. Second, we use optimal transport theory, and specifically the concept of the barycenter, to maximize decision maker utility under the chosen fairness constraints. Third, the algorithm is able to handle cases of intersectionality, i.e., of multi-dimensional discrimination of certain groups on grounds of several criteria. We discuss three main examples (credit applications; college admissions; insurance contracts) and map out the legal and policy implications of our approach. The explicit formalization of the trade-off between individual and group fairness allows this post-processing approach to be tailored to different situational contexts in which one or the other fairness criterion may take precedence. Finally, we evaluate our model experimentally.
△ Less
Submitted 24 September, 2019; v1 submitted 21 December, 2017;
originally announced December 2017.