What are trust issues in generative AI
|
There are several trust issues to consider with generative AI: Bias and unfairness: If the training data for the AI contains biases, the AI's creations may reflect those biases. For example, if an AI learning to generate images of people is trained mostly on images of lighter-skinned individuals, it may underrepresent or misrepresent people of color. Care must be taken to ensure the AI is exposed to diverse, representative data. Manipulation and deception: Generative AIs are getting so good that it can be difficult to distinguish AI-produced works from human-produced ones. They can be used to generate synthetic images, videos, speech, text, etc. that seem very realistic. This enables malicious actors to manipulate content or generate synthetic data for the purpose of deception. Users need to be aware of these risks. Lack of transparency: Many generative AI models are based on complex neural networks that are opaque and difficult for people to understand. It is hard to know exactly why they generate the outputs they do. This lack of explainability and transparency makes the AI's creations harder to trust. Explainable and transparent AI is an open area of research. Difficulty of oversight and governance: Because generative AIs can continue learning and adapting on their own, they may generate unexpected outputs that their developers never intended or considered. This can make the systems difficult to oversee, manage, and govern responsibly. Policies and oversight processes are still developing. Threats to human creativity: Some argue that as generative AI's get better at producing creative works of art, music, stories, and more, this could significantly impact human artists and threaten human creativity. However, others believe AI will primarily augment and enhance human creativity rather than replace it. This remains an open debate. Long-term risks from advanced AI: As generative models become more sophisticated, powerful and autonomous, this amplifies concerns about risks from artificial general intelligence, including potential threats to humanity. However, we are still quite a long way from developing human-level AI. Researchers are working to address risks from advanced AI. Overall, while generative AI promises many benefits, we must be aware of and address these trust issues if we want such systems to be adopted safely and responsibly. With proper safeguards and oversight in place, the risks could be mitigated. But researchers and policymakers still have a lot of work to do. |
Blog 8 |
|
|
|
|
|
|
|
|
|
|
|
|
From the blog |
How Dataknobs help in building data productsEnterprises are most successful when they treat data like a product. It enable to use data in multiple use cases. However data product should be designed differently compared to software product. Generative AI is one of approach to build data productGenerative AI has enabled many transformative scenarios. We combine generative AI, AI, automation, web scraping, ingesting dataset to build new data products. We have expertise in generative AI, but for business benefit we define our goal to build data product in data centric manner. |
|
|
Spotlight |
|
|
Generative AI slides |
Level Up Your AI Skills: |