Stars, Stripes, and Silicon: Unravelling the ChatGPT's All-American, Monochrome, Cis-centric Bias

• URL: http://arxiv.org/abs/2410.13868v1
• Date: Wed, 02 Oct 2024 08:55:00 GMT
• Title: Stars, Stripes, and Silicon: Unravelling the ChatGPT's All-American, Monochrome, Cis-centric Bias
• Authors: Federico Torrielli,
• Abstract summary: The paper calls for interdisciplinary efforts to address these challenges. It highlights the need for collaboration between researchers, practitioners, and stakeholders to establish governance frameworks.
• Score: 0.0
• License:
• Abstract: This paper investigates the challenges associated with bias, toxicity, unreliability, and lack of robustness in large language models (LLMs) such as ChatGPT. It emphasizes that these issues primarily stem from the quality and diversity of data on which LLMs are trained, rather than the model architectures themselves. As LLMs are increasingly integrated into various real-world applications, their potential to negatively impact society by amplifying existing biases and generating harmful content becomes a pressing concern. The paper calls for interdisciplinary efforts to address these challenges. Additionally, it highlights the need for collaboration between researchers, practitioners, and stakeholders to establish governance frameworks, oversight, and accountability mechanisms to mitigate the harmful consequences of biased LLMs. By proactively addressing these challenges, the AI community can harness the enormous potential of LLMs for the betterment of society without perpetuating harmful biases or exacerbating existing inequalities.

Related papers

• Large Language Models Reflect the Ideology of their Creators [73.25935570218375]
  Large language models (LLMs) are trained on vast amounts of data to generate natural language. We uncover notable diversity in the ideological stance exhibited across different LLMs and languages.
  arXiv  Detail & Related papers  (2024-10-24T04:02:30Z)
• Towards Trustworthy AI: A Review of Ethical and Robust Large Language Models [1.7466076090043157]
  Large Language Models (LLMs) could transform many fields, but their fast development creates significant challenges for oversight, ethical creation, and building user trust. This comprehensive review looks at key trust issues in LLMs, such as unintended harms, lack of transparency, vulnerability to attacks, alignment with human values, and environmental impact. To tackle these issues, we suggest combining ethical oversight, industry accountability, regulation, and public involvement.
  arXiv  Detail & Related papers  (2024-06-01T14:47:58Z)
• The Impossibility of Fair LLMs [59.424918263776284]
  The need for fair AI is increasingly clear in the era of large language models (LLMs) We review the technical frameworks that machine learning researchers have used to evaluate fairness. We develop guidelines for the more realistic goal of achieving fairness in particular use cases.
  arXiv  Detail & Related papers  (2024-05-28T04:36:15Z)
• Navigating LLM Ethics: Advancements, Challenges, and Future Directions [5.023563968303034]
  This study addresses ethical issues surrounding Large Language Models (LLMs) within the field of artificial intelligence. It explores the common ethical challenges posed by both LLMs and other AI systems. It highlights challenges such as hallucination, verifiable accountability, and decoding censorship complexity.
  arXiv  Detail & Related papers  (2024-05-14T15:03:05Z)
• On Catastrophic Inheritance of Large Foundation Models [51.41727422011327]
  Large foundation models (LFMs) are claiming incredible performances. Yet great concerns have been raised about their mythic and uninterpreted potentials. We propose to identify a neglected issue deeply rooted in LFMs: Catastrophic Inheritance. We discuss the challenges behind this issue and propose UIM, a framework to understand the catastrophic inheritance of LFMs from both pre-training and downstream adaptation.
  arXiv  Detail & Related papers  (2024-02-02T21:21:55Z)
• The Ethics of Interaction: Mitigating Security Threats in LLMs [1.407080246204282]
  The paper delves into the nuanced ethical repercussions of such security threats on society and individual privacy. We scrutinize five major threats--prompt injection, jailbreaking, Personal Identifiable Information (PII) exposure, sexually explicit content, and hate-based content--to assess their critical ethical consequences and the urgency they create for robust defensive strategies.
  arXiv  Detail & Related papers  (2024-01-22T17:11:37Z)
• Competition-Level Problems are Effective LLM Evaluators [121.15880285283116]
  This paper aims to evaluate the reasoning capacities of large language models (LLMs) in solving recent programming problems in Codeforces. We first provide a comprehensive evaluation of GPT-4's peiceived zero-shot performance on this task, considering various aspects such as problems' release time, difficulties, and types of errors encountered. Surprisingly, theThoughtived performance of GPT-4 has experienced a cliff like decline in problems after September 2021 consistently across all the difficulties and types of problems.
  arXiv  Detail & Related papers  (2023-12-04T18:58:57Z)
• Challenges and Contributing Factors in the Utilization of Large Language Models (LLMs) [10.039589841455136]
  This review explores the issue of domain specificity, where large language models (LLMs) may struggle to provide precise answers to specialized questions within niche fields. It's suggested to diversify training data, fine-tune models, enhance transparency and interpretability, and incorporate ethics and fairness training.
  arXiv  Detail & Related papers  (2023-10-20T08:13:36Z)
• Factuality Challenges in the Era of Large Language Models [113.3282633305118]
  Large Language Models (LLMs) generate false, erroneous, or misleading content. LLMs can be exploited for malicious applications. This poses a significant challenge to society in terms of the potential deception of users.
  arXiv  Detail & Related papers  (2023-10-08T14:55:02Z)
• Voluminous yet Vacuous? Semantic Capital in an Age of Large Language Models [0.0]
  Large Language Models (LLMs) have emerged as transformative forces in the realm of natural language processing, wielding the power to generate human-like text. This paper explores the evolution, capabilities, and limitations of these models, while highlighting ethical concerns they raise.
  arXiv  Detail & Related papers  (2023-05-29T09:26:28Z)
• On the Opportunities and Risks of Foundation Models [256.61956234436553]
  We call these models foundation models to underscore their critically central yet incomplete character. This report provides a thorough account of the opportunities and risks of foundation models. To tackle these questions, we believe much of the critical research on foundation models will require deep interdisciplinary collaboration.
  arXiv  Detail & Related papers  (2021-08-16T17:50:08Z)

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.