A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms

• URL: http://arxiv.org/abs/2407.01294v2
• Date: Sat, 09 Nov 2024 12:00:09 GMT
• Title: A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms
• Abstract summary: This paper introduces a collaborative, human-centred taxonomy of AI, algorithmic and automation harms. We argue that existing, while valuable, can be narrow, unclear, and often overlook the needs of the wider public.
• Score: 2.411037855204708
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: This paper introduces a collaborative, human-centred taxonomy of AI, algorithmic and automation harms. We argue that existing taxonomies, while valuable, can be narrow, unclear, typically cater to practitioners and government, and often overlook the needs of the wider public. Drawing on existing taxonomies and a large repository of documented incidents, we propose a taxonomy that is clear and understandable to a broad set of audiences, as well as being flexible, extensible, and interoperable. Through iterative refinement with topic experts and crowdsourced annotation testing, we propose a taxonomy that can serve as a powerful tool for civil society organisations, educators, policymakers, product teams and the general public. By fostering a greater understanding of the real-world harms of AI and related technologies, we aim to increase understanding, empower NGOs and individuals to identify and report violations, inform policy discussions, and encourage responsible technology development and deployment.

Related papers

• Big AI's Regulatory Capture: Mapping Industry Interference and Government Complicity [7.894837175061716]
  It is critical that we comprehend the extent and depth of pervasive and multifaceted capture of AI regulation by corporate actors.<n>We first develop a taxonomy of mechanisms enabling capture to provide a comprehensive understanding of the problem.<n>Our analysis identifies 249 instances of capture mechanisms, often co-occurring with narratives that rationalise such capture.
  arXiv  Detail & Related papers  (2026-05-07T18:07:57Z)
• Taxonomy of User Needs and Actions [51.86289485979439]
  The growing ubiquity of conversational AI highlights the need for frameworks that capture users' instrumental goals and the situated, adaptive, and social practices through which they achieve them.<n>To address this gap, we introduce the Taxonomy of User Needs and Actions (TUNA), an empirically grounded framework developed through iterative qualitative analysis of 1193 human-AI conversations.<n> TUNA organizes user actions into a three-level hierarchy encompassing behaviors associated with information seeking, synthesis, procedural guidance, content creation, social interaction, and meta-conversation.
  arXiv  Detail & Related papers  (2025-10-07T17:04:42Z)
• Understanding and Mitigating Risks of Generative AI in Financial Services [22.673239064487667]
  We aim to highlight AI content safety considerations specific to the financial services domain and outline an associated AI content risk taxonomy. We evaluate how existing open-source technical guardrail solutions cover this taxonomy by assessing them on data collected via red-teaming activities.
  arXiv  Detail & Related papers  (2025-04-25T16:55:51Z)
• Who is Responsible When AI Fails? Mapping Causes, Entities, and Consequences of AI Privacy and Ethical Incidents [31.53910982726317]
  We analyzed 202 real-world AI privacy and ethical incidents to develop a taxonomy.<n>Our findings reveal widespread harms from poor organizational decisions and legal non-compliance.<n>Our findings provide actionable guidance for policymakers and practitioners.
  arXiv  Detail & Related papers  (2025-03-28T21:57:38Z)
• Bridging Legal Knowledge and AI: Retrieval-Augmented Generation with Vector Stores, Knowledge Graphs, and Hierarchical Non-negative Matrix Factorization [6.0045906216050815]
  Agentic Generative AI, powered by Large Language Models (LLMs) with Retrieval-Augmented Generation (RAG), Knowledge Graphs (KGs), and Vector Stores (VSs) This technology excels at inferring relationships within vast unstructured or semi-structured datasets. We introduce a generative AI system that integrates RAG, VS, and KG, constructed via Non-Negative Matrix Factorization (NMF)
  arXiv  Detail & Related papers  (2025-02-27T18:35:39Z)
• Vernacularizing Taxonomies of Harm is Essential for Operationalizing Holistic AI Safety [0.0]
  Operationalizing AI ethics and safety principles and frameworks is essential to realizing potential benefits and mitigating potential harms caused by AI systems. We argue that taxonomy must also be transferred into local categories to be readily implemented in sector-specific AI safety operationalization efforts. Drawing from emerging anthropological theories of human rights, we propose that the process of "vernacularization" can help bridge this gap.
  arXiv  Detail & Related papers  (2024-10-21T22:47:48Z)
• The Ethics of Advanced AI Assistants [53.89899371095332]
  This paper focuses on the opportunities and the ethical and societal risks posed by advanced AI assistants. We define advanced AI assistants as artificial agents with natural language interfaces, whose function is to plan and execute sequences of actions on behalf of a user. We consider the deployment of advanced assistants at a societal scale, focusing on cooperation, equity and access, misinformation, economic impact, the environment and how best to evaluate advanced AI assistants.
  arXiv  Detail & Related papers  (2024-04-24T23:18:46Z)
• Exploiting the Margin: How Capitalism Fuels AI at the Expense of Minoritized Groups [0.0]
  This paper explores the relationship between capitalism, racial injustice, and artificial intelligence (AI) It argues that AI acts as a contemporary vehicle for age-old forms of exploitation. The paper promotes an approach that integrates social justice and equity into the core of technological design and policy.
  arXiv  Detail & Related papers  (2024-03-10T22:40:07Z)
• Position Paper: Agent AI Towards a Holistic Intelligence [53.35971598180146]
  We emphasize developing Agent AI -- an embodied system that integrates large foundation models into agent actions. In this paper, we propose a novel large action model to achieve embodied intelligent behavior, the Agent Foundation Model.
  arXiv  Detail & Related papers  (2024-02-28T16:09:56Z)
• Report of the 1st Workshop on Generative AI and Law [78.62063815165968]
  This report presents the takeaways of the inaugural Workshop on Generative AI and Law (GenLaw) A cross-disciplinary group of practitioners and scholars from computer science and law convened to discuss the technical, doctrinal, and policy challenges presented by law for Generative AI.
  arXiv  Detail & Related papers  (2023-11-11T04:13:37Z)
• General Purpose Artificial Intelligence Systems (GPAIS): Properties, Definition, Taxonomy, Societal Implications and Responsible Governance [16.030931070783637]
  General-Purpose Artificial Intelligence Systems (GPAIS) has been defined to refer to these AI systems. To date, the possibility of an Artificial General Intelligence, powerful enough to perform any intellectual task as if it were human, or even improve it, has remained an aspiration, fiction, and considered a risk for our society. This work discusses existing definitions for GPAIS and proposes a new definition that allows for a gradual differentiation among types of GPAIS according to their properties and limitations.
  arXiv  Detail & Related papers  (2023-07-26T16:35:48Z)
• Fairness in Agreement With European Values: An Interdisciplinary Perspective on AI Regulation [61.77881142275982]
  This interdisciplinary position paper considers various concerns surrounding fairness and discrimination in AI, and discusses how AI regulations address them. We first look at AI and fairness through the lenses of law, (AI) industry, sociotechnology, and (moral) philosophy, and present various perspectives. We identify and propose the roles AI Regulation should take to make the endeavor of the AI Act a success in terms of AI fairness concerns.
  arXiv  Detail & Related papers  (2022-06-08T12:32:08Z)
• TaxoCom: Topic Taxonomy Completion with Hierarchical Discovery of Novel Topic Clusters [57.59286394188025]
  We propose a novel framework for topic taxonomy completion, named TaxoCom. TaxoCom discovers novel sub-topic clusters of terms and documents. Our comprehensive experiments on two real-world datasets demonstrate that TaxoCom not only generates the high-quality topic taxonomy in terms of term coherency and topic coverage.
  arXiv  Detail & Related papers  (2022-01-18T07:07:38Z)
• Building Bridges: Generative Artworks to Explore AI Ethics [56.058588908294446]
  In recent years, there has been an increased emphasis on understanding and mitigating adverse impacts of artificial intelligence (AI) technologies on society. A significant challenge in the design of ethical AI systems is that there are multiple stakeholders in the AI pipeline, each with their own set of constraints and interests. This position paper outlines some potential ways in which generative artworks can play this role by serving as accessible and powerful educational tools.
  arXiv  Detail & Related papers  (2021-06-25T22:31:55Z)
• Large-scale Taxonomy Induction Using Entity and Word Embeddings [13.30719395448771]
  We propose TIEmb, an approach for automatic subsumption extraction from knowledge using entity and text embeddings. We apply the approach on the WebIsA database, a database of classes subsumption relations extracted from the large portion of Wide Web, to extract hierarchies in the Person and Place domain.
  arXiv  Detail & Related papers  (2021-05-04T05:53:12Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.