FINAL WORD
There is increasing attention given to the concept of trustworthiness for artificial intelligence and robotics . However , trust is highly context-dependent , varies among cultures , and requires reflection on others ’ trustworthiness , appraising whether there is enough evidence to conclude that these agents deserve to be trusted .
Moreover , little research exists on what happens when too much trust is placed in robots and autonomous systems . Conceptual clarity and a shared framework for approaching over trust are missing .
Here are some of the most concerning risks associated with AI :
AI hallucinations
Deepfakes
The implications of fake images extend to various areas . With the rise of fake identities , revenge porn , and fabricated employees , the range of potential misuse for AI-generated photographs is expanding . One particular technology called Generative Adversarial Network , GAN , is a type of deep neural network capable of producing new data and generating highly realistic images by using random input .
This technology opens up the realm of deepfakes , where sophisticated generative techniques manipulate facial features and can be applied to images , audio , and video . This form of digital puppetry carries significant consequences in political persuasion , misinformation or polarisation campaigns .
Earlier this year , a New York attorney used a conversational chatbot for legal research . The AI deceitfully incorporated six fabricated precedents into his filing , falsely attributing them to prominent legal databases . This is a perfect example of an AI hallucination , where the output is either fake or nonsense . These incidents happen when prompts are outside of the AI ’ s training data and so the model hallucinates or contradicts itself in order to respond .
Since the very beginning of AI back in 1956 , we have made this terrible error , a sort of original sin of the field , to believe that minds are like computers and vice versa .
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