Media Ecology - plan

Here are my notes/plan for media ecology question! 

Explain what you think is the most significant change in the media recent years. 

[Industry]

Techniques such as algorithmic content selection and user personalization can introduce risks and societal threats. The challenge of balancing these opportunities and benefits against their potential for negative impacts underscores the need for more research in responsible media technology. While AI has brought many benefits to the media industry, it has also raised several ethical concerns. One of the most significant concerns is the potential for bias in AI algorithms. AI algorithms are only as unbiased as the data they are trained on. If the training data contains biases, the algorithm will also be biased. For example, if an AI algorithm is trained on data that contains racial biases, it will also produce biased results. This could lead to discriminatory practices in the media industry, such as targeted advertisements based on race or gender. Another concern is the potential for AI to be used for deepfake content, which involves creating fake audio, video, or images that appear to be real. Deepfake content has the potential to undermine the credibility of the media and erode public trust in journalism. 

- Representation of Gender and Identity (Stuart Hall, David Gauntlett): AI can be used to analyze media content for gender representation, potentially shedding light on biases, stereotypes, or underrepresentation of certain identities. 

A key consequence of digitalization and the new business models that have become possible is that new competition has emerged for the media industry. There are, for example, new niche players who are able to target specific user demands more accurately, thus threatening to take over positions previously held by traditional media houses and their established editorial processes. For example, finn.no has become the main platform for classified ads in Norway, a sector previously covered primarily by traditional media; Twitter has become a major debate platform, making it possible to bypass the traditional media; Facebook appears to give us far more insight into peoples’ lives than the personals sections in the newspapers ever did; and Netflix, HBO, Twitch, TikTok, and YouTube challenge the positions owned by the commercial and public broadcasters in the culture and entertainment sectors. Large platforms, such as Facebook, aggregate content and services more efficiently than the media has been able to, capitalizing on both content curation by users and algorithms for predictive content personalization. Ultimately, these large platforms now act as powerful media distribution channels, while traditional media organizations have become content providers to these platforms, almost no different than just about anyone else with a smartphone. 

[Audience]

One of the major areas in which AI impacts media and entertainment is content creation. AI algorithms can now analyze vast amounts of data to create content tailored to specific audiences. For example, Netflix uses machine learning algorithms to analyze viewing data and recommend content to users, while companies such as Jukin Media and Storyful use AI to analyze user-generated content and identify potential viral hits. AI is also being used to create new forms of content, such as virtual influencers and computer-generated actors. AI is also being used to enhance the consumer experience in entertainment. For example, chatbots are increasingly being used by entertainment companies to provide customer service and answer frequently asked questions, while voice recognition technology is being used to allow consumers to control their entertainment experience hands-free. AI-powered personal assistants such as Amazon’s Alexa and Google Assistant are also being integrated into entertainment systems, allowing consumers to control their entertainment experience with voice commands. 

- Reception theory (Stuart Hall): AI-driven recommendation algorithms analyze user data to suggest content based on past behavior. This shapes the media consumption habits of audiences, potentially influencing their tastes and preferences over time.