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Many writers, actors and other creatives are their jobs.

Generative AI (GenAI) is making machine learning and creative work more accessible to everyone. But for industry professionals, the rise of generative AI can signal the destruction of creative jobs.

Yet, according to a recent report by the World Economic Forum, .

We are four scholars in different creative industries hoping to explore educational approaches to AI. We want to help prepare the next generation to innovate within human-AI collaborative frameworks. To do this, we have begun to confer with other creative professionals through an online survey.

What if AI can actually support human creativity and productivity? Can we use these technologies to our advantage? What can we expect for the future?

We believe creative professionals can harness new technologies while still upholding their foundational creative and ethical principles.

How AI is being used in creative sectors

AI is becoming deeply embedded within the operational , from a nascent concept to an integrated reality.

Media and creative workers have gone on strike to protest the use of AI, sparking important conversations. For example, and the have raised concerns and helped shape new policies around AI and creative work.

Within media production, large language models (LLMs) can facilitate the rapid prototyping of narrative concepts, scripts and audiovisual materials, while automated editing platforms and AI-driven visual effects create massive efficiency gains in post-production. This technological integration allows creators to shift their focus from laborious manual tasks to higher-level creative refinement.

In and , AI and machine learning are acknowledged drivers of change. AI can enhance processes from ideation to production logistics like sorting and personalized web-to-print platforms. In the realm of Digital Asset Management, AI is instrumental in improving and utility through automated metadata tagging and sophisticated image recognition.

Journalism is also undergoing a significant transformation. AI has been used for a while now to analyze large datasets for investigative reporting, but LLMs now routinely streamline article summarization. More advanced applications are emerging: AI systems are designed to identify news values and auto-generate articles from live events. Major news organizations like the Financial Times and The New York Times are already deploying .

Ethical challenges

The integration of AI is not without considerable challenges.

The generation of and are documented failures. These examples highlight critical issues with accuracy and reliability.

Many people have said they is . This disparity between deployment and user consciousness underscores the subtle yet pervasive nature of AI's integration. This points to an urgent need for greater transparency and digital literacy.

Bias and intellectual property

Models trained on vast, uncurated internet data often replicate and amplify existing societal biases. For example, studies demonstrate persistent issues such as in LLMs.

At the same time, urgent ethical and legal questions regarding have emerged. The without compensation has created significant friction. For example, the pending highlights unresolved issues of fair use and remuneration for creative work.

Conversely, GenAI demonstrates considerable potential to democratize creative production. These tools, by lowering technical barriers and automating complex processes, can provide access to individuals and groups historically excluded from creative fields due to resource or educational constraints.

Specific applications are already enhancing media accessibility, such as AI-powered tools that automatically generate alt text for images and subtitles for video content.

Navigating this dual-use landscape necessitates the adoption of robust governance frameworks. Fostering industry-wide equity, diversity and innovation education is essential to mitigate risks while harnessing GenAI's potential for an inclusive creative ecosystem.

Labor and skill evolution

Technological revolutions have historically catalyzed significant transformations in and GenAI represents the latest

The proliferation of GenAI has once again , demanding new professional competencies.

Human creativity and intervention are indispensable, providing cultural and contextual accuracy. Humans must also review AI-generated content for quality and inclusivity.

In response to this shift, higher education institutions need to from tool-specific training towards fostering curiosity, ethical reasoning and AI literacy.