The nearest future of AI holds tremendous promise, but also poses significant challenges.Some of the major ones to watch out for:
- *Ethical Issues*: Ensuring AI systems are fair, transparent, and unbiased is crucial. This includes addressing concerns around privacy, data protection, and potential job displacement ¹.
- *Bias and Discrimination*: AI systems can perpetuate existing biases if trained on biased data. Mitigating this requires diverse data sources, regular auditing, and fairness-aware machine learning approaches ².
- *Technical Challenges*: Handling complex tasks, scalability, and efficiency are significant technical hurdles. For instance, training powerful AI models requires massive computing resources and data ².
- *Transparency and Accountability*: Understanding how AI systems make decisions is essential for accountability. Techniques like explainable AI (XAI) can provide insights into complex AI systems ¹.
- *Cybersecurity*: AI systems can be vulnerable to cyber attacks, emphasizing the need for robust security measures, encryption, and access controls ¹.
- *Job Displacement*: While AI creates new opportunities, it also automates routine tasks, potentially displacing jobs. Upskilling and reskilling programs can help workers adapt ¹.
- *Data Confidentiality*: Protecting sensitive data is critical, especially in industries like healthcare and finance. Implementing strict security mechanisms and complying with data protection regulations is vital ¹.
- *Lack of Explainability*: Understanding how AI systems reach conclusions is essential. Developing explainable models and analyzing feature importance can help build trust in AI.
The current challenges of AI are due to dependency on data that is already biased, discriminated, manipulated, and diplomatic and the data-driven to support capitalism, competition for weapons, trade war, nuclear threats, and fighting for global warming issues and responsibilities. When the development of AI focuses on such matters and solutions. There are requirements to generate separate data for such problems and solutions.
In our new paper in Accountability in Research, we present GAIDeT (Generative AI Delegation Taxonomy) — the first taxonomy that systematically describes delegating tasks to generative AI across all stages of research.
https://doi.org/10.1080/08989621.2025.2544331
It works like the CRediT authorship taxonomy but for AI, covering macro- and micro-level contributions, from idea generation and data collection to writing and editing.
We’ve also developed a free tool to automatically generate an AI contribution statement:
https://panbibliotekar.github.io/gaidet-declaration/
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