Trust vs Safety in AI: An IIT Perspective
The discussion around Artificial Intelligence (AI) safety and trust is gaining momentum, especially in a diverse country like India. An IIT professor has recently pointed out that while satisfying internal benchmarks and technical safety standards is crucial, it is not enough to build trust among users. Even if AI systems pass security tests and achieve high accuracy, they can still perpetuate biases or make decisions that are not easily understandable by users.
This distinction is especially important in sectors that directly impact human welfare, such as healthcare, finance, and criminal justice. In these areas, the consequences of AI decisions can be significant. The professor emphasizes that explainability in AI should not be merely an engineering tool for debugging but should also serve to foster trust. Users need to comprehend how decisions are made and whether these decisions are fair.
In the safety paradigm, the focus is often on internal validation. Questions like whether a model achieves 95% accuracy or withstands adversarial attacks are common. However, this approach overlooks the broader ethical implications. Safety-oriented fairness aims to ensure that AI does not explicitly use sensitive attributes such as race, gender, or age in its decision-making processes. This is particularly relevant in India, where social stratification can lead to unequal treatment by AI systems.
Moreover, safety-oriented privacy focuses on protecting personal data from unauthorized access. This includes implementing robust encryption methods and access controls, ensuring that individuals' data is secure and confidential. The emphasis here is on preventing data breaches, which is vital in a country where data privacy concerns are increasingly coming to the forefront.
When it comes to safety-oriented robustness, the goal is to ensure that AI systems can withstand adversarial attacks designed to exploit their weaknesses. For instance, a vision system must be resilient against small perturbations that could lead to misclassification. This robustness is essential in various applications, including security and surveillance.
Ultimately, the article argues that building tools specifically for trust involves investing in ethical considerations. While such investments may come at a cost, they are necessary for fostering trust in AI systems across critical domains. In conclusion, AI safety, fairness, and ethical responsibility should be intertwined to ensure that technology serves humanity positively.
