How is safety seen in the artificial intelligence (AI) community? It uses the term 'AI safety', but research is needed to ensure AI is beneficial. Trustworthiness and confidence too are needed.... Following are the 7 main challenges in the context of beneficial AI:
Fairness: Machine learning (ML) uses decision-making, which might be biased. For instance, the dataset is biased due to human prejudices.
Transparency: In many cases, it is difficult to understand how ML systems take decisions. Especially when the ML system includes a neural network, there is a lack of explainability of the decisions made by the system.
Misuse: The algorithms can be maliciously used by people.
Security: AI, like every software, is vulnerable to malicious attacks. This might result in unintended actions of the initial design purpose.
Policy: AI has an increasing impact on products and society.
Ethics: AI needs to act under certain ethical standards. Human values are one of the broader goals to limit functionalities.
Control/alignment: AI must be aligned with the values of the designer so that no misinterpretation can happen.
Verily, ML-based systems cannot fully satisfy the current safety standards.
From 'A Review on AI Safety in Highly Automated Driving', Frontiers
Fairness: Machine learning (ML) uses decision-making, which might be biased. For instance, the dataset is biased due to human prejudices.
Transparency: In many cases, it is difficult to understand how ML systems take decisions. Especially when the ML system includes a neural network, there is a lack of explainability of the decisions made by the system.
Misuse: The algorithms can be maliciously used by people.
Security: AI, like every software, is vulnerable to malicious attacks. This might result in unintended actions of the initial design purpose.
Policy: AI has an increasing impact on products and society.
Ethics: AI needs to act under certain ethical standards. Human values are one of the broader goals to limit functionalities.
Control/alignment: AI must be aligned with the values of the designer so that no misinterpretation can happen.
Verily, ML-based systems cannot fully satisfy the current safety standards.
From 'A Review on AI Safety in Highly Automated Driving', Frontiers
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