Artificial Intelligence can facilitate selfish decisions by altering the appearance of interaction partners

• URL: http://arxiv.org/abs/2306.04484v1
• Date: Wed, 7 Jun 2023 14:53:12 GMT
• Title: Artificial Intelligence can facilitate selfish decisions by altering the appearance of interaction partners
• Authors: Nils K\"obis, Philipp Lorenz-Spreen, Tamer Ajaj, Jean-Francois Bonnefon, Ralph Hertwig, Iyad Rahwan
• Abstract summary: We investigate the potential impact of blur filters, a type of appearance-altering technology, on individuals' behavior towards others. Our findings consistently demonstrate a significant increase in selfish behavior directed towards individuals whose appearance is blurred. These results emphasize the need for broader ethical discussions surrounding AI technologies that modify our perception of others.
• Score: 2.3208437191245133
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The increasing prevalence of image-altering filters on social media and video conferencing technologies has raised concerns about the ethical and psychological implications of using Artificial Intelligence (AI) to manipulate our perception of others. In this study, we specifically investigate the potential impact of blur filters, a type of appearance-altering technology, on individuals' behavior towards others. Our findings consistently demonstrate a significant increase in selfish behavior directed towards individuals whose appearance is blurred, suggesting that blur filters can facilitate moral disengagement through depersonalization. These results emphasize the need for broader ethical discussions surrounding AI technologies that modify our perception of others, including issues of transparency, consent, and the awareness of being subject to appearance manipulation by others. We also emphasize the importance of anticipatory experiments in informing the development of responsible guidelines and policies prior to the widespread adoption of such technologies.

Related papers

• Attention is all they need: Cognitive science and the (techno)political economy of attention in humans and machines [0.0]
  This paper critically analyses the "attention economy" within the framework of cognitive science and techno-political economics. We explore how current business models, particularly in digital platform capitalism, harness user engagement by strategically shaping attentional patterns.
  arXiv  Detail & Related papers  (2024-05-10T13:53:46Z)
• Predictable Artificial Intelligence [67.79118050651908]
  We argue that achieving predictability is crucial for fostering trust, liability, control, alignment and safety of AI ecosystems. This paper aims to elucidate the questions, hypotheses and challenges relevant to Predictable AI.
  arXiv  Detail & Related papers  (2023-10-09T21:36:21Z)
• Analyzing Character and Consciousness in AI-Generated Social Content: A Case Study of Chirper, the AI Social Network [0.0]
  The study embarks on a comprehensive exploration of AI behavior, analyzing the effects of diverse settings on Chirper's responses. Through a series of cognitive tests, the study gauges the self-awareness and pattern recognition prowess of Chirpers. An intriguing aspect of the research is the exploration of the potential influence of a Chirper's handle or personality type on its performance.
  arXiv  Detail & Related papers  (2023-08-30T15:40:18Z)
• Anthropomorphization of AI: Opportunities and Risks [24.137106159123892]
  Anthropomorphization is the tendency to attribute human-like traits to non-human entities. With widespread adoption of AI systems, the tendency for users to anthropomorphize it increases significantly. We study the objective legal implications, as analyzed through the lens of the recent blueprint of AI bill of rights.
  arXiv  Detail & Related papers  (2023-05-24T06:39:45Z)
• Self-mediated exploration in artificial intelligence inspired by cognitive psychology [1.3351610617039975]
  Exploration of the physical environment is an indispensable precursor to data acquisition and enables knowledge generation via analytical or direct trialing. This work links human behavior and artificial agents to endorse self-development. A study is subsequently designed to mirror previous human trials, which artificial agents are made to undergo repeatedly towards convergence. Results demonstrate causality, learned by the vast majority of agents, between their internal states and exploration to match those reported for human counterparts.
  arXiv  Detail & Related papers  (2023-02-13T18:20:44Z)
• Investigating the Potential of Artificial Intelligence Powered Interfaces to Support Different Types of Memory for People with Dementia [22.89233407347665]
  One of the most difficult challenges to address is supporting the fluctuating accessibility needs of people with dementia. We present future directions for the design of AI-based systems to personalize an interface for dementia-related changes in different types of memory.
  arXiv  Detail & Related papers  (2022-11-19T17:31:45Z)
• I am Only Happy When There is Light: The Impact of Environmental Changes on Affective Facial Expressions Recognition [65.69256728493015]
  We study the impact of different image conditions on the recognition of arousal from human facial expressions. Our results show how the interpretation of human affective states can differ greatly in either the positive or negative direction.
  arXiv  Detail & Related papers  (2022-10-28T16:28:26Z)
• Adaptive cognitive fit: Artificial intelligence augmented management of information facets and representations [62.997667081978825]
  Explosive growth in big data technologies and artificial intelligence [AI] applications have led to increasing pervasiveness of information facets. Information facets, such as equivocality and veracity, can dominate and significantly influence human perceptions of information. We suggest that artificially intelligent technologies that can adapt information representations to overcome cognitive limitations are necessary.
  arXiv  Detail & Related papers  (2022-04-25T02:47:25Z)
• The world seems different in a social context: a neural network analysis of human experimental data [57.729312306803955]
  We show that it is possible to replicate human behavioral data in both individual and social task settings by modifying the precision of prior and sensory signals. An analysis of the neural activation traces of the trained networks provides evidence that information is coded in fundamentally different ways in the network in the individual and in the social conditions.
  arXiv  Detail & Related papers  (2022-03-03T17:19:12Z)
• 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)
• Learning Emotional-Blinded Face Representations [77.7653702071127]
  We propose two face representations that are blind to facial expressions associated to emotional responses. This work is motivated by new international regulations for personal data protection.
  arXiv  Detail & Related papers  (2020-09-18T09:24:10Z)

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.