AI Mismatches: Identifying Potential Algorithmic Harms Before AI Development

• URL: http://arxiv.org/abs/2502.18682v2
• Date: Tue, 15 Apr 2025 03:15:40 GMT
• Title: AI Mismatches: Identifying Potential Algorithmic Harms Before AI Development
• Authors: Devansh Saxena, Ji-Youn Jung, Jodi Forlizzi, Kenneth Holstein, John Zimmerman,
• Abstract summary: We observe significant "AI Mismatches", where the system's actual performance falls short of what is needed to ensure safety and co-create value.<n>We propose an AI Mismatch approach to anticipate and mitigate risks early on, focusing on the gap between realistic model performance and required task performance.
• Score: 19.911935490500188
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: AI systems are often introduced with high expectations, yet many fail to deliver, resulting in unintended harm and missed opportunities for benefit. We frequently observe significant "AI Mismatches", where the system's actual performance falls short of what is needed to ensure safety and co-create value. These mismatches are particularly difficult to address once development is underway, highlighting the need for early-stage intervention. Navigating complex, multi-dimensional risk factors that contribute to AI Mismatches is a persistent challenge. To address it, we propose an AI Mismatch approach to anticipate and mitigate risks early on, focusing on the gap between realistic model performance and required task performance. Through an analysis of 774 AI cases, we extracted a set of critical factors, which informed the development of seven matrices that map the relationships between these factors and highlight high-risk areas. Through case studies, we demonstrate how our approach can help reduce risks in AI development.

Related papers

• Graph of Effort: Quantifying Risk of AI Usage for Vulnerability Assessment [0.0]
  An AI used to attack non-AI assets is referred to as offensive AI. The risk of exploiting its capabilities, such as high automation and complex pattern recognition, could significantly increase. This paper introduces the Graph of Effort, an intuitive, flexible, and effective threat modeling method for analyzing the effort required to use offensive AI for vulnerability exploitation by an adversary.
  arXiv  Detail & Related papers  (2025-03-20T17:52:42Z)
• Multi-Agent Risks from Advanced AI [90.74347101431474]
  Multi-agent systems of advanced AI pose novel and under-explored risks.<n>We identify three key failure modes based on agents' incentives, as well as seven key risk factors.<n>We highlight several important instances of each risk, as well as promising directions to help mitigate them.
  arXiv  Detail & Related papers  (2025-02-19T23:03:21Z)
• Engineering Trustworthy AI: A Developer Guide for Empirical Risk Minimization [53.80919781981027]
  Key requirements for trustworthy AI can be translated into design choices for the components of empirical risk minimization. We hope to provide actionable guidance for building AI systems that meet emerging standards for trustworthiness of AI.
  arXiv  Detail & Related papers  (2024-10-25T07:53:32Z)
• EARBench: Towards Evaluating Physical Risk Awareness for Task Planning of Foundation Model-based Embodied AI Agents [53.717918131568936]
  Embodied artificial intelligence (EAI) integrates advanced AI models into physical entities for real-world interaction.<n>Foundation models as the "brain" of EAI agents for high-level task planning have shown promising results.<n>However, the deployment of these agents in physical environments presents significant safety challenges.<n>This study introduces EARBench, a novel framework for automated physical risk assessment in EAI scenarios.
  arXiv  Detail & Related papers  (2024-08-08T13:19:37Z)
• The Reasoning Under Uncertainty Trap: A Structural AI Risk [0.0]
  Report provides an exposition of what makes RUU so challenging for both humans and machines. We detail how this misuse risk connects to a wider network of underlying structural risks.
  arXiv  Detail & Related papers  (2024-01-29T17:16:57Z)
• Managing extreme AI risks amid rapid progress [171.05448842016125]
  We describe risks that include large-scale social harms, malicious uses, and irreversible loss of human control over autonomous AI systems. There is a lack of consensus about how exactly such risks arise, and how to manage them. Present governance initiatives lack the mechanisms and institutions to prevent misuse and recklessness, and barely address autonomous systems.
  arXiv  Detail & Related papers  (2023-10-26T17:59:06Z)
• AI Hazard Management: A framework for the systematic management of root causes for AI risks [0.0]
  This paper introduces the AI Hazard Management (AIHM) framework. It provides a structured process to systematically identify, assess, and treat AI hazards. It builds upon an AI hazard list from a comprehensive state-of-the-art analysis.
  arXiv  Detail & Related papers  (2023-10-25T15:55:50Z)
• Building Safe and Reliable AI systems for Safety Critical Tasks with Vision-Language Processing [1.2183405753834557]
  Current AI algorithms are unable to identify common causes for failure detection. Additional techniques are required to quantify the quality of predictions. This thesis will focus on vision-language data processing for tasks like classification, image captioning, and vision question answering.
  arXiv  Detail & Related papers  (2023-08-06T18:05:59Z)
• Capsa: A Unified Framework for Quantifying Risk in Deep Neural Networks [142.67349734180445]
  Existing algorithms that provide risk-awareness to deep neural networks are complex and ad-hoc. Here we present capsa, a framework for extending models with risk-awareness.
  arXiv  Detail & Related papers  (2023-08-01T02:07:47Z)
• Fairness in AI and Its Long-Term Implications on Society [68.8204255655161]
  We take a closer look at AI fairness and analyze how lack of AI fairness can lead to deepening of biases over time. We discuss how biased models can lead to more negative real-world outcomes for certain groups. If the issues persist, they could be reinforced by interactions with other risks and have severe implications on society in the form of social unrest.
  arXiv  Detail & Related papers  (2023-04-16T11:22:59Z)
• Modelos din\^amicos aplicados \`a aprendizagem de valores em intelig\^encia artificial [0.0]
  Several researchers in the area have developed a robust, beneficial, and safe concept of AI for the preservation of humanity and the environment. It is utmost importance that artificial intelligent agents have their values aligned with human values. Perhaps this difficulty comes from the way we are addressing the problem of expressing values using cognitive methods.
  arXiv  Detail & Related papers  (2020-07-30T00:56:11Z)

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.