Media Bias Detection Using Sentimental Analysis and Clustering Algorithms

Rawat, Sachin; Vadivu, G.

doi:10.1007/978-981-16-5652-1_43

Cited by 1 publication

(2 citation statements)

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“…The third assumption focused only on fake media, systemic bias and the possibility of abuses, given the context of profound uncertainties regarding AI (Borsci et al 2023;Feher and Veres 2022;Rawat and Vadivu 2022;Holton and Boyd 2021;Bell 2018). However, this particular survey showed more comprehensive dynamics of visions strongly determined by participants' personal beliefs and negotiation of uncertainties, which we modeled as a pair of glasses on a fulcrum -a Glasses Model of AI Trust.…”

Section: Discussionmentioning

confidence: 99%

“…In parallel, a key requirement is to control (and clean up) datasets, fight disinformation and foster truly diverse, inclusive and reliable content (Georgieva et al 2022). These transformations support benefits and trigger uncertainties, especially as the above-mentioned driver functions reveal challenges of socio-technical issues with inequalities (Holton and Boyd 2021), system biases (Rawat and Vadivu 2022), cultural-economic colonization (Bell 2018), data colonialism (Couldry, and Mejias 2019), data-driven surveillance with privacy issues (Fossa 2023) and political destabilization through fake campaigns (Borsci et al 2023). The interactivity and virality of personal assistants, virtual influencers, AI-produced content and art, or deepfakes leads to fundamental questions such as how information sources can be evaluated or how they will add value to an existing ICM process or service.…”

Section: Theoretical Frameworkmentioning

confidence: 99%

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Modeling AI Trust for 2050: perspectives from media and info-communication experts

Feher,

Vicsek,

Deuze

2024

AI & Soc

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The study explores the future of AI-driven media and info-communication as envisioned by experts from all world regions, defining relevant terminology and expectations for 2050. Participants engaged in a 4-week series of surveys, questioning their definitions and projections about AI for the field of media and communication. Their expectations predict universal access to democratically available, automated, personalized and unbiased information determined by trusted narratives, recolonization of information technology and the demystification of the media process. These experts, as technology ambassadors, advocate AI-to-AI solutions to mitigate technology-driven misuse and misinformation. The optimistic scenarios shift responsibility to future generations, relying on AI-driven solutions and finding inspiration in nature. Their present-based forecasts could be construed as being indicative of professional near-sightedness and cognitive dissonance. Visualizing our findings into a Glasses Model of AI Trust, the study contributes to key debates regarding AI policy, developmental trajectories, and academic research in media and info-communication fields.

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Section: Discussionmentioning

confidence: 99%