Biased AI can Influence Political Decision-Making
- URL: http://arxiv.org/abs/2410.06415v2
- Date: Mon, 4 Nov 2024 20:12:07 GMT
- Title: Biased AI can Influence Political Decision-Making
- Authors: Jillian Fisher, Shangbin Feng, Robert Aron, Thomas Richardson, Yejin Choi, Daniel W. Fisher, Jennifer Pan, Yulia Tsvetkov, Katharina Reinecke,
- Abstract summary: 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.
- Score: 64.9461133083473
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: As modern AI models become integral to everyday tasks, concerns about their inherent biases and their potential impact on human decision-making have emerged. While bias in models are well-documented, less is known about how these biases influence human decisions. This paper presents two interactive experiments investigating the effects of partisan bias in AI language models on political decision-making. Participants interacted freely with either a biased liberal, biased conservative, or unbiased control model while completing political decision-making tasks. 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, regardless of their personal political partisanship. However, we also discovered that prior knowledge about AI could lessen the impact of the bias, highlighting the possible importance of AI education for robust bias mitigation. Our findings not only highlight the critical effects of interacting with biased AI and its ability to impact public discourse and political conduct, but also highlights potential techniques for mitigating these risks in the future.
Related papers
- From Experts to the Public: Governing Multimodal Language Models in Politically Sensitive Video Analysis [48.14390493099495]
This paper examines the governance of large language models (MM-LLMs) through individual and collective deliberation.
We conducted a two-step study: first, interviews with 10 journalists established a baseline understanding of expert video interpretation; second, 114 individuals from the general public engaged in deliberation using Inclusive.AI.
arXiv Detail & Related papers (2024-09-15T03:17:38Z) - Rolling in the deep of cognitive and AI biases [1.556153237434314]
We argue that there is urgent need to understand AI as a sociotechnical system, inseparable from the conditions in which it is designed, developed and deployed.
We address this critical issue by following a radical new methodology under which human cognitive biases become core entities in our AI fairness overview.
We introduce a new mapping, which justifies the humans to AI biases and we detect relevant fairness intensities and inter-dependencies.
arXiv Detail & Related papers (2024-07-30T21:34:04Z) - Overcoming Anchoring Bias: The Potential of AI and XAI-based Decision Support [0.0]
Information systems (IS) are frequently designed to leverage the negative effect of anchoring bias to influence individuals' decision-making.
Recent advances in Artificial Intelligence (AI) have opened new opportunities for mitigating biased decisions.
arXiv Detail & Related papers (2024-05-08T11:25:04Z) - 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) - Unveiling the Hidden Agenda: Biases in News Reporting and Consumption [59.55900146668931]
We build a six-year dataset on the Italian vaccine debate and adopt a Bayesian latent space model to identify narrative and selection biases.
We found a nonlinear relationship between biases and engagement, with higher engagement for extreme positions.
Analysis of news consumption on Twitter reveals common audiences among news outlets with similar ideological positions.
arXiv Detail & Related papers (2023-01-14T18:58:42Z) - 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) - Deciding Fast and Slow: The Role of Cognitive Biases in AI-assisted
Decision-making [46.625616262738404]
We use knowledge from the field of cognitive science to account for cognitive biases in the human-AI collaborative decision-making setting.
We focus specifically on anchoring bias, a bias commonly encountered in human-AI collaboration.
arXiv Detail & Related papers (2020-10-15T22:25:41Z) - 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.