Building Trust with Responsible AI

Artificial Intelligence is being used in almost every aspect of life. AI symbolizes growth and productivity in the minds of some, but it is raising questions as well on the fairness, privacy, and security of these systems. Many legitimate issues exist, including biased choices, labor replacement, and a lack of security. When it comes to robots, this is very frightening. Self-driving automobiles, for example, can cause injury or death if they make mistakes. Responsible AI addresses these difficulties and makes AI systems more accountable.

Responsible AI should fulfill the following aims:

• Interpretability: We obtain an explanation for how a model makes predictions when we interpret it. An AI system makes predictions for a user. Even if these selections are correct, a user is likely to seek an explanation. Responsible AI can describe how we create interpretable models.
• Fairness: AI systems have the potential to make judgments that are biased towards particular groups of people. Bias in the training data is the source of this bias. The easier it is to assure fairness and rectify any bias in a model, the more interpretable it is. As a result, we need a Responsible AI framework to explain how we evaluate fairness and what to do if a model makes unjust predictions.
• Safety and Security: AI systems aren’t deterministic. When confronted with new situations, they are prone to making poor choices. The systems can even be tampered with to make unwise decisions. Therefore, we need to ensure safety and security in these systems.
• Data Governance: The data used must be of high quality. If the data used by AI has errors, the system may make wrong decisions.

How to make sure we build Responsible AI systems?

User-centered reliable AI systems should be created using basic best practices for software systems and methods that address machine learning-specific problems. The following points should be kept in mind while designing a reliable and responsible AI.

• Consider augmenting and assisting users with a variety of options. One should use a human-centered design approach. This includes building a model with appropriate disclosures, clarity, and control for the users. Engage a wide range of users and use-case scenarios, and incorporate comments before and during the project’s development. 
• Rather than using a single metric, you should use a combination to understand better the tradeoffs between different types of errors and experiences. Make sure your metrics are appropriate for the context and purpose of your system; for example, a fire alarm system should have a high recall, even if it implies a false alarm now and then.
• ML models will reflect the data they are trained on, so make sure you understand your raw data. If this isn’t possible, such as with sensitive raw data, try to comprehend your input data as much as possible while still maintaining privacy.
• Understand the limitations of your dataset and communicate them with the users whenever possible.
• Regular testing and quality assurance ensure that your model will work as intended and can be trusted.
• Continued monitoring and updating of the system will ensure that the AI is working correctly. Make sure that you consider user feedback while regularly updating your system.

Users will not use your services if they do not trust your AI. We won’t trust systems that employ information we don’t want to share or believe will lead to biased conclusions. Decision explanations and accountability for those decisions go long toward establishing trust. The need for this trust is the driving force behind Responsible AI.

References:

• https://towardsdatascience.com/what-is-responsible-ai-548743369729
• https://ai.google/responsibilities/responsible-ai-practices/
• https://www.nytimes.com/2021/04/16/business/artificial-intelligence-regulation.html?smtyp=cur&smid=tw-nytimes
• https://www.wsj.com/articles/google-pushes-sensible-ideas-for-how-to-regulate-ai-11579521003?mod=article_inline
• https://arxiv.org/abs/2001.09784

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