Should ChatGPT be Biased? Challenges and Risks of Bias in Large Language Models

• URL: http://arxiv.org/abs/2304.03738v3
• Date: Mon, 13 Nov 2023 17:50:22 GMT
• Title: Should ChatGPT be Biased? Challenges and Risks of Bias in Large Language Models
• Authors: Emilio Ferrara
• Abstract summary: This article investigates the challenges and risks associated with biases in large-scale language models like ChatGPT. We discuss the origins of biases, stemming from, among others, the nature of training data, model specifications, algorithmic constraints, product design, and policy decisions. We review the current approaches to identify, quantify, and mitigate biases in language models, emphasizing the need for a multi-disciplinary, collaborative effort to develop more equitable, transparent, and responsible AI systems.
• Score: 11.323961700172175
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: As the capabilities of generative language models continue to advance, the implications of biases ingrained within these models have garnered increasing attention from researchers, practitioners, and the broader public. This article investigates the challenges and risks associated with biases in large-scale language models like ChatGPT. We discuss the origins of biases, stemming from, among others, the nature of training data, model specifications, algorithmic constraints, product design, and policy decisions. We explore the ethical concerns arising from the unintended consequences of biased model outputs. We further analyze the potential opportunities to mitigate biases, the inevitability of some biases, and the implications of deploying these models in various applications, such as virtual assistants, content generation, and chatbots. Finally, we review the current approaches to identify, quantify, and mitigate biases in language models, emphasizing the need for a multi-disciplinary, collaborative effort to develop more equitable, transparent, and responsible AI systems. This article aims to stimulate a thoughtful dialogue within the artificial intelligence community, encouraging researchers and developers to reflect on the role of biases in generative language models and the ongoing pursuit of ethical AI.

Related papers

• On the Fairness, Diversity and Reliability of Text-to-Image Generative Models [49.60774626839712]
  multimodal generative models have sparked critical discussions on their fairness, reliability, and potential for misuse. We propose an evaluation framework designed to assess model reliability through their responses to perturbations in the embedding space. Our method lays the groundwork for detecting unreliable, bias-injected models and retrieval of bias provenance.
  arXiv  Detail & Related papers  (2024-11-21T09:46:55Z)
• Bias in Large Language Models: Origin, Evaluation, and Mitigation [4.606140332500086]
  Large Language Models (LLMs) have revolutionized natural language processing, but their susceptibility to biases poses significant challenges. This comprehensive review examines the landscape of bias in LLMs, from its origins to current mitigation strategies. Ethical and legal implications of biased LLMs are discussed, emphasizing potential harms in real-world applications such as healthcare and criminal justice.
  arXiv  Detail & Related papers  (2024-11-16T23:54:53Z)
• Recourse for reclamation: Chatting with generative language models [2.877217169371665]
  We extend the concept of algorithmic recourse to generative language models. We provide users a novel mechanism to achieve their desired prediction by dynamically setting thresholds for toxicity filtering. A pilot study supports the potential of our proposed recourse mechanism.
  arXiv  Detail & Related papers  (2024-03-21T15:14:25Z)
• DPP-Based Adversarial Prompt Searching for Lanugage Models [56.73828162194457]
  Auto-regressive Selective Replacement Ascent (ASRA) is a discrete optimization algorithm that selects prompts based on both quality and similarity with determinantal point process (DPP) Experimental results on six different pre-trained language models demonstrate the efficacy of ASRA for eliciting toxic content.
  arXiv  Detail & Related papers  (2024-03-01T05:28:06Z)
• Cognitive bias in large language models: Cautious optimism meets anti-Panglossian meliorism [0.0]
  Traditional discussions of bias in large language models focus on a conception of bias closely tied to unfairness. Recent work raises the novel possibility of assessing the outputs of large language models for a range of cognitive biases. I draw out philosophical implications of this discussion for the rationality of human cognitive biases as well as the role of unrepresentative data in driving model biases.
  arXiv  Detail & Related papers  (2023-11-18T01:58:23Z)
• Survey of Social Bias in Vision-Language Models [65.44579542312489]
  Survey aims to provide researchers with a high-level insight into the similarities and differences of social bias studies in pre-trained models across NLP, CV, and VL. The findings and recommendations presented here can benefit the ML community, fostering the development of fairer and non-biased AI models.
  arXiv  Detail & Related papers  (2023-09-24T15:34:56Z)
• CBBQ: A Chinese Bias Benchmark Dataset Curated with Human-AI Collaboration for Large Language Models [52.25049362267279]
  We present a Chinese Bias Benchmark dataset that consists of over 100K questions jointly constructed by human experts and generative language models. The testing instances in the dataset are automatically derived from 3K+ high-quality templates manually authored with stringent quality control. Extensive experiments demonstrate the effectiveness of the dataset in detecting model bias, with all 10 publicly available Chinese large language models exhibiting strong bias in certain categories.
  arXiv  Detail & Related papers  (2023-06-28T14:14:44Z)
• Democratizing Ethical Assessment of Natural Language Generation Models [0.0]
  Natural language generation models are computer systems that generate coherent language when prompted with a sequence of words as context. Despite their ubiquity and many beneficial applications, language generation models also have the potential to inflict social harms. Ethical assessment of these models is therefore critical. This article introduces a new tool to democratize and standardize ethical assessment of natural language generation models.
  arXiv  Detail & Related papers  (2022-06-30T12:20:31Z)
• Estimating the Personality of White-Box Language Models [0.589889361990138]
  Large-scale language models, which are trained on large corpora of text, are being used in a wide range of applications everywhere. Existing research shows that these models can and do capture human biases. Many of these biases, especially those that could potentially cause harm, are being well-investigated. However, studies that infer and change human personality traits inherited by these models have been scarce or non-existent.
  arXiv  Detail & Related papers  (2022-04-25T23:53:53Z)
• DIME: Fine-grained Interpretations of Multimodal Models via Disentangled Local Explanations [119.1953397679783]
  We focus on advancing the state-of-the-art in interpreting multimodal models. Our proposed approach, DIME, enables accurate and fine-grained analysis of multimodal models.
  arXiv  Detail & Related papers  (2022-03-03T20:52:47Z)
• Plausible Counterfactuals: Auditing Deep Learning Classifiers with Realistic Adversarial Examples [84.8370546614042]
  Black-box nature of Deep Learning models has posed unanswered questions about what they learn from data. Generative Adversarial Network (GAN) and multi-objectives are used to furnish a plausible attack to the audited model. Its utility is showcased within a human face classification task, unveiling the enormous potential of the proposed framework.
  arXiv  Detail & Related papers  (2020-03-25T11:08:56Z)

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.