AI safety and bias: unraveling the complex chain of AI training

AI safety and bias are urgent and complex issues for safety researchers.As AI is embedded in all aspects of society, it is of utmost importance to understand its development process, functions and potential shortcomings.

Lama Nachman, director of the Intelligent Systems Laboratory at Intel Labs, said it is essential to bring in input from experts in various fields in the AI ​​training and learning process. she said: “We assume that AI systems learn from subject matter experts, not from AI developers. Those who teach AI systems do not understand how to program AI systems. , the system can automatically learn to build these action recognition and interaction models.”

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This presents an exciting, but potentially costly prospect, with the potential to continuously improve the system while interacting with users. “Some of the general aspects of dialogue are absolutely usable, but the idiosyncrasies of how people do things in the physical world are unlike anything real,” Nachman said. There are a lot of things,” he explains. This shows that while her current AI technology offers excellent interaction systems, the transition to understanding and performing physical tasks is an entirely different challenge,” she said. Ta.

AI security could be compromisedand she attributed it to several factors, including poorly defined objectives, lack of robustness, and unpredictability of the AI’s response to certain inputs. When AI systems are trained on large datasets, they may learn and reproduce harmful behaviors found in the data.

Bias in AI systems can also lead to unfair outcomes such as discrimination and unfair decision-making. Bias can creep into AI systems In many ways. For example, the data used for training may reflect prejudices that exist in society. As AI continues to permeate many aspects of human life, the potential for harm from biased decisions has increased significantly, and the need for effective methodologies to detect and mitigate these biases. is enhanced.

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Another concern is the role of AI. spread false information. As advanced AI tools become more accessible, there is an increasing risk that these tools will be used to generate deceptive content that can mislead public opinion or promote false narratives. . The impact can be far-reaching, including threats to democracy, public health, and social cohesion. This underscores the need to build strong countermeasures to mitigate the spread of AI misinformation and the need for continued research to stay ahead of evolving threats.

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Every innovation comes with an unavoidable set of challenges. Nachman proposes that AI systems be designed to be “aligned with human values” at a high level, and considers the risks to his AI development considering trust, accountability, transparency and explainability. I suggested a base approach. Addressing AI today will help secure future systems.

https://www.zdnet.com/article/ai-safety-and-bias-untangling-the-complex-chain-of-ai-training/#ftag=RSSbaffb68 AI safety and bias: unraveling the complex chain of AI training

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