Blind Spots and Biases: Exploring the Role of Annotator Cognitive Biases in NLP

• URL: http://arxiv.org/abs/2404.19071v1
• Date: Mon, 29 Apr 2024 19:28:35 GMT
• Title: Blind Spots and Biases: Exploring the Role of Annotator Cognitive Biases in NLP
• Authors: Sanjana Gautam, Mukund Srinath,
• Abstract summary: There is growing concern over its potential to exacerbate existing biases and societal disparities. This issue has prompted widespread attention from academia, policymakers, industry, and civil society. Our research focuses on reviewing existing methodologies and ongoing investigations aimed at understanding annotation attributes that contribute to bias.
• Score: 3.9287497907611875
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: With the rapid proliferation of artificial intelligence, there is growing concern over its potential to exacerbate existing biases and societal disparities and introduce novel ones. This issue has prompted widespread attention from academia, policymakers, industry, and civil society. While evidence suggests that integrating human perspectives can mitigate bias-related issues in AI systems, it also introduces challenges associated with cognitive biases inherent in human decision-making. Our research focuses on reviewing existing methodologies and ongoing investigations aimed at understanding annotation attributes that contribute to bias.

Related papers

• Biased AI can Influence Political Decision-Making [64.9461133083473]
  This paper presents two experiments investigating the effects of partisan bias in AI language models on political decision-making. We found that participants exposed to politically biased models were significantly more likely to adopt opinions and make decisions aligning with the AI's bias.
  arXiv  Detail & Related papers  (2024-10-08T22:56:00Z)
• The Odyssey of Commonsense Causality: From Foundational Benchmarks to Cutting-Edge Reasoning [70.16523526957162]
  Understanding commonsense causality helps people understand the principles of the real world better. Despite its significance, a systematic exploration of this topic is notably lacking. Our work aims to provide a systematic overview, update scholars on recent advancements, and provide a pragmatic guide for beginners.
  arXiv  Detail & Related papers  (2024-06-27T16:30:50Z)
• Interdisciplinary Expertise to Advance Equitable Explainable AI [3.4195896673488395]
  In this paper, we focus on explainable AI (XAI) and describe a framework for interdisciplinary expert panel review. We emphasize the importance of the interdisciplinary expert panel to produce more accurate, equitable interpretations.
  arXiv  Detail & Related papers  (2024-05-29T17:45:38Z)
• Enabling High-Level Machine Reasoning with Cognitive Neuro-Symbolic Systems [67.01132165581667]
  We propose to enable high-level reasoning in AI systems by integrating cognitive architectures with external neuro-symbolic components. We illustrate a hybrid framework centered on ACT-R and we discuss the role of generative models in recent and future applications.
  arXiv  Detail & Related papers  (2023-11-13T21:20:17Z)
• A Survey on Transferability of Adversarial Examples across Deep Neural Networks [53.04734042366312]
  adversarial examples can manipulate machine learning models into making erroneous predictions. The transferability of adversarial examples enables black-box attacks which circumvent the need for detailed knowledge of the target model. This survey explores the landscape of the adversarial transferability of adversarial examples.
  arXiv  Detail & Related papers  (2023-10-26T17:45:26Z)
• Unmasking Nationality Bias: A Study of Human Perception of Nationalities in AI-Generated Articles [10.8637226966191]
  We investigate the potential for nationality biases in natural language processing (NLP) models using human evaluation methods. Our study employs a two-step mixed-methods approach to identify and understand the impact of nationality bias in a text generation model. Our findings reveal that biased NLP models tend to replicate and amplify existing societal biases, which can translate to harm if used in a sociotechnical setting.
  arXiv  Detail & Related papers  (2023-08-08T15:46:27Z)
• 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)
• Fairness And Bias in Artificial Intelligence: A Brief Survey of Sources, Impacts, And Mitigation Strategies [11.323961700172175]
  This survey paper offers a succinct, comprehensive overview of fairness and bias in AI. We review sources of bias, such as data, algorithm, and human decision biases. We assess the societal impact of biased AI systems, focusing on the perpetuation of inequalities and the reinforcement of harmful stereotypes.
  arXiv  Detail & Related papers  (2023-04-16T03:23:55Z)
• From Psychological Curiosity to Artificial Curiosity: Curiosity-Driven Learning in Artificial Intelligence Tasks [56.20123080771364]
  Psychological curiosity plays a significant role in human intelligence to enhance learning through exploration and information acquisition. In the Artificial Intelligence (AI) community, artificial curiosity provides a natural intrinsic motivation for efficient learning. CDL has become increasingly popular, where agents are self-motivated to learn novel knowledge.
  arXiv  Detail & Related papers  (2022-01-20T17:07:03Z)
• Data, Power and Bias in Artificial Intelligence [5.124256074746721]
  Artificial Intelligence has the potential to exacerbate societal bias and set back decades of advances in equal rights and civil liberty. Data used to train machine learning algorithms may capture social injustices, inequality or discriminatory attitudes that may be learned and perpetuated in society. This paper reviews ongoing work to ensure data justice, fairness and bias mitigation in AI systems from different domains.
  arXiv  Detail & Related papers  (2020-07-28T16:17:40Z)
• Bias in Data-driven AI Systems -- An Introductory Survey [37.34717604783343]
  This survey focuses on data-driven AI, as a large part of AI is powered nowadays by (big) data and powerful Machine Learning (ML) algorithms. If otherwise not specified, we use the general term bias to describe problems related to the gathering or processing of data that might result in prejudiced decisions on the bases of demographic features like race, sex, etc.
  arXiv  Detail & Related papers  (2020-01-14T09:39:09Z)

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.