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Responsible AI practices you need to implement today

AI has penetrated almost all industries, ranging from agriculture to healthcare and education. With the opportunities to enhance the lifestyle of people, comes challenges to ensure the fairness of its use.

It is important to focus on the privacy, security, interpretability and other ethical and moral aspects of AI systems. Today, business firms around the world are focusing on responsible AI practices. Evidently, it is also important to develop a model, which you can check for unfair biases and know exactly how you can evaluate the performance.

Oversight is important

In order to ensure a moral conduct while implementing AI systems, it is necessary to review the outputs of these systems. It is not possible for AI to operate completely without human intervention. From time to time, a human oversight is necessary to ensure a seamless functioning of the AI mechanism.

Since AI technologies are gaining ground fast around the world, the leading organisations have gone beyond the phase of experiments. Numerous industries have already achieved tangible results. The positive outcomes include increased sales and sales forecasts that are more accurate. This helps companies to acquire more customers.

However, companies need to stick to responsible AI practices, so they do not end up compromising with the privacy of customers, morality and ethics.

Recommended AI practices

Business leaders across the globe have acknowledged that AI systems should be effective, reliable and user-centric. Therefore, it is necessary to design these systems, complying with the best practices applied for software systems. Besides, some aspects related to machine learning should also be considered. Here are some of the key recommendations for AI practices.

The design-approach should be human-centred

The true essence of the AI system can be evaluated by assessing the experience of the users. Therefore, it is important to focus on the following aspects during the designing process:

  • The design features should have inbuilt disclosures, ensuring control and clarity. This enhances the overall user experience.
  • Assistance and augmentation need to be considered. In situations where probability is high that a single answer can cater to several users, it is appropriate to come up with a single answer. Otherwise, your system must provide the user with a few options. Technically thinking, it is difficult to achieve a good deal of accuracy with a single answer.
  • It is necessary to integrate adverse feedback in the early stages of the designing process. Next, you need to carry out live testing, considering a small portion of the traffic before deploying it fully.
  • You should engage with different sets of users. Besides, it is necessary to incorporate feedback throughout the process of project development, and even before it. Evidently, you can get a wide variety of perspectives of the users and more people will benefit from the AI system.

Training and monitoring

For training and monitoring, you need to identify several metrics. When you use multiple metrics rather than one, you can understand how various kinds of experiences and errors can be dealt with.

  • The metrics should include feedbacks the surveys on users, product health during short and long terms and quantities tracking the overall performance of the system. For instance, many companies evaluate the customer lifetime value and click-through rate.
  • Make sure that you are using the right metrics, contextualising them with the objectives of your system.

Examine raw data directly

Machine learning models work on the data that you feed them with. Therefore, you need to examine the raw data whenever possible. Evaluate the raw data to find out the following aspects:

  • Whether or not your data contains mistakes, like incorrect labels of missing values.
  • Whether the data you are using is sampled in a way representing all your users. They must come from all age groups to make the results unbiased. Besides, the data should come from the real world setting to ensure its accuracy.
  • A persistent challenge that you might face is working on the serving-training skew. The performances during training and serving will show some difference. You need to mark out the potential skews during training. Next, try to work on addressing them, adjusting your objective function or training data. When you analyse the data, try to work on information that represents the same set you will be dealing with during deployment.
  • Go ahead with the simplest model that suits your needs. Remember, certain features in your model may be unnecessary or redundant.
  • In case of supervised systems, you should focus on the connection between the labels of data and what you are willing to predict. A gap between the two may lead to a problematic situation.
  • Make sure that the raw data you are using is free from bias.

Test rigorously

Testing your AI system is of utmost importance. You need quality engineering to ensure that it works in the desired way, and proves trustable.

  • Carry out unit tests for each component in isolation.
  • Integration tests should be carried out, so that you can evaluate how each component of ML interacts with different parts of the system.
  • You can detect the input drift proactively, when you analyse the input statistics. They should not show unexpected changes.
  • The dataset used to check the system should be of gold standard. It should behave in the desired way. You need to make regular updates to the test set, according to the changing behaviours of the users.
  • It is important to carry out interactive testing of the users, so that you can use different sets of needs during the development process
  • The quality check mechanism should be able to prevent unintended failures.

To Conclude…

While working on an AI system, you need to identify the limitations of your model and dataset. If your model is developed to identify correlations, you should not apply it for making casual inferences. Today, ML models largely show a reflection of the data training patterns.

Therefore, it is necessary to convey the coverage and scope of the training. This will clarify the limitations and capabilities of your AI model. Besides, companies should convey these limitations to the people using the AI systems wherever possible. When you educate the user, the feedback carries a greater authenticity.

Aarsh, Co- Founder & COO, Gravitas AI



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