How do you create personalized AI systems for different users?

AI-driven Personalization boils down to being able to acquire users' preferences in large numbers. For instance, when deciding the products to suggest to a potential customer, or the right video to a user, it is key to have previously acquired users' feedback on enough products to infer the new ones. This means that is extremely important to prompt the user for feedback, even in the form of a "thumbs up/thumbs down", to then be able to drive recommendations that improve over time.

In my opinion, surveys, user testing and interviews are the most essential here. You need to talk to your actual users at multiple stages in the project for input, feedback and refinement. That said, if you’re not sure where to start, describe your scenario to ChatGPT and ask it to generate 10 personas for you.

Challenge even the most basic assumptions. Even if surveys / ethnographic interviews look positive for a particular function, do validate your starting points (e.g., Will an elderly person enter data via an app whenever prompted to do so? Do they share the passion of the project team regarding mobile phones? etc.).

The outcome of this analysis should be a clear understanding of what are the "good" user behaviors that are common within and among your various users segments so that you can build a solution that encourages more of these users behaviors. In order to build models that enable and encourage these positive behaviors for each user, think of what signals/data needs to be collected to so that the models can learn what good user behaviors are and how to identify them. Once the model is able to identify good user behaviors accurately, you want to think of ways to act on that model's output i.e. some kind of user experience that is triggered based on the model's output.

Personalized AI systems have revolutionized industries, from education to e-commerce, and have become an essential aspect of enhancing user experiences. Understanding users lies at the heart of building effective personalized AI. By employing methods like surveys, interviews, and user testing, we can gain valuable insights into user characteristics, motivations, and interactions with the system. This invaluable data empowers us to define user segments, profiles, and features that ensure a personalized touch for each user. Throughout the development journey, incorporating user feedback is key to refining and optimizing the personalized AI system.

In my experience, one common mistake was focusing too much on the impact of your feature and not looking at its impact on the whole product/ecosystem. Make sure the goals of your feature contribute to the overall goals of the product. Track not only your local product/feature metrics but also the global product metrics. Besides defining goals and measuring impact (quantitative feedback), you should also consider getting direct feedback (qualitative feedback) from your users. Gathering feedback from users should be a periodical exercise after product launch to ensure that your solution provides the value it is designed for.

Ethics should be considered in any AI but especially a personalized one. Personalization can lead to filter bubbles, privacy concerns, and potential bias in the system. RLHF (real live human feedback) is also incredibly important in a personalized model, to ensure that real world performance matches expectation and intention.

Clearly articulate the specific objectives and outcomes you aim to achieve through personalization---and ensure they're measurable. Whether it's enhancing user engagement, improving conversion rates, or increasing customer satisfaction, having well-defined goals will guide the development process and ensure the AI system's efforts align with the desired results.

After defining the personalization goals, it's crucial to understand the potential pitfalls of personalization as well. An overly personalized system may risk creating an 'echo chamber' effect, where users are only exposed to familiar content, potentially stunting their growth. In the context of a learning system, while it may be effective in boosting user satisfaction in the short term, it may not necessarily facilitate comprehensive learning. Furthermore, ethical considerations come into play, with respect to the extent and nature of user data required for enabling personalization. Balancing customization with diverse exposure and respecting privacy becomes an integral part of system design.

This step is one of the hardest step to get right. In this step as we are determining the the right objectives. We need to clearly determine, What are we trying to Achieve Long Term ? What can Unintentionally go wrong ? This requires a lot of thoughtfulness and a lot of experimentation. An oversimplified view of what the objective should be, can easily lead to a wrong path. We shouldn't overengineer too as small nuances in how we define the objectives can also dramatically change the outcome of personalization. We have read about, Pew Research highlighting consumers feeling Personalized ads are becoming too creepy. Without having proper guard rails, the objective can lead to unexpected significant side effects too.

Rule based personalisation makes it easy to tailor experiences according to given criteria. But it has to be set up manually and it is a huge task to accommodate all preferences for all scenarios and customers. AI based personalisation is driven by data and you can automatically accommodate for all customers preferences each time they visit a website. However, to get the right recommendations outcomes, you need to have high quality of data and a high AI model performance. In practice, most companies will go for a combination of expert knowledge rule based and AI recommendations to get most of the benefits.

A rule-based system is more suitable for use cases with limited users and items. For instance, a simple website with a few pages and a conversion goal (e.g., form filling) could benefit from 2-3 website variants categorized for Users, Lead nurturers, and Clients. For scenarios with numerous users and items (e.g., Songs, News articles), Machine Learning models like Collaborative filtering are worth exploring. These models identify similar users based on consumed items and similar items based on user preferences to make personalized recommendations. However these algorithms tend to serve popular items than the thematic ones. If that's the case, you could explore more advanced algorithms like Two-Tower models.

Expanding on the choice of personalization methods, it's crucial to realize that both rule-based and data-driven methods have merits and limitations. Rule-based personalization offers greater transparency and control, its flexibility can be compromised in scenarios where user behaviours deviate from defined rules. Data-driven methods leverage the power of machine learning to adapt and evolve with the user's preferences. The success of data-driven methods hinges on the quality and diversity of user data. The choice between the two should not be a binary one. rather it might be beneficial to harness a combination of both to optimize the personalization strategy.

It is crucial to understand and leverage the Virtuous cycle of AI - The more data the algorithm can learn from, the better the algorithm will be, which in turn, helps build better products, which in turn, drives more users to those products, which in turn, generates more data. Rule Based Methods can be leveraged where we don't have enough data for the algorithm to learn and for Rule Based Events. For example promoting a strategic event to users that cannot be easily learned by algorithm. Amazon's massive homepage promotion of Prime Day, that's a strategic decision done by the Amazon team and not by their homepage algorithm.

It is crucial to understand and leverage the Virtuous cycle of AI - The more data the algorithm can learn from, the better the algorithm will be, which in turn, helps build better products, which in turn, drives more users to those products, which in turn, generates more data. We should trust the algorithms. However we cant discount on Rule Based Methods, they can be leveraged where we don't have enough data for the algorithm to learn and for Rule Based Events. For example promoting a strategic event to users that cannot be easily learned by algorithm. eg. Amazon's massive homepage promotion of Prime Day, that's a strategic decision done by the Amazon team and not by their homepage algorithm.

Develop a robust and scalable infrastructure to implement the chosen personalization methods. Consider factors like data security, real-time processing, and system performance while integrating the logic into the AI system. An efficient implementation will lay the foundation for a seamless personalized experience for users.

It’s worth noting here that what follows personalization is the actual ML/AI system that provides an output (decision, recommendation, response etc) to the specific user. This calls for an AI system being adapted for the particular user and consequently being trained on the user’s peer group (profile) data or user’s own data. The former (training on particular segment of users) is more feasible and practical whereas the latter requires greater infrastructure to collect and train on user’s data.

This is the crucial step, as the Personalization Logic is the Brain Of the Operation. Few Key Aspects of Consideration. 1. Scalability - We need to look at the AI System growing, rather than becoming obsolete or struggling to handle the increased volume. 2. Transparency. It is critical to understand how the model is making its decisions? for establishing trust, and can help fine tune the model over time.

It is critical to understand that these metrics only present a part of the entire narrative. For instance, while accuracy and precision may imply an efficient model, it's equally vital to scrutinize its interpretation and application in diverse real-world contexts. The 'fairness' of a model should also be considered as a metric to prevent algorithmic biases from seeping into the decision-making process. It's thus imperative to integrate robust fairness evaluations into these measures to ensure that personalization is unbiased and equitable for all user groups.

Continuously monitor and assess the AI system's personalization outcomes against your predefined goals. Utilizing metrics like user engagement, conversion rates, and customer feedback are key to measuring success. Regular evaluation will help identify areas for improvement and allow for necessary adjustments to enhance the system's effectiveness.

No matter what test you choose, always keep your testing empathy first and human centric. The goal should always be a smooth and easy user adoption with ideal outcomes that consistently benefit end users. That is the key for not only adoption but long term success.

Embracing an iterative approach to continuously improving the AI system's personalization capabilities will go along way to strengthen its adaptability. Encouraging a culture of experimentation and innovation, can help leverage user feedback and analytics to make data-driven enhancements. Continuous improvement ensures the AI system remains adaptable and relevant to users' evolving needs.

Personalisation means offering different services/products to different people at the same price level. Or a company may charge different prices for the same service provided to different people, i.e. price discrimination. AI gives a big advantage to companies that have the resources to train AI models, when the bargaining power of customers may be weakened. Companies that have great AI power should let their customers know that they will not be discriminated, if they value the principles of transparency and AI for good.

Improving the personalization process is continuous as the users interests, preferences change over time. For combating bias we can consider leveraging diverse AI team to an extent possible and also conduct periodic data audits.

One aspect of AI which tends to concern organizations (and it should) is whether employee or organizational data is secure. There are only a small handful of truly secure AI solutions out there - start with one of those rather than having to go back to redo everything later.

Remain mindful of ethical considerations surrounding AI systems and user data privacy. Transparently communicating the personalization process to users will go a long way to establish trust and transparency. It offers users control over their data and the ability to opt-out if desired. Additionally, invest in educating your team on the latest AI advancements and industry best practices to stay at the forefront of personalized AI technology.

Even most advanced AI systems can't replicate the creativity and ingenuity of a human mind. Hence it is critical to adopt GoldiLocks Rule for AI, Not be Too Optimistic and Not be too Optimistic and be Just Right. Having a Clear understanding of the Pitfalls and Limitations of AI is crucial. Key factors to consider : a. Avoiding Oversimplification of AI System Objective. b. Utmost Focus on High-Scale and High Quality Data for AI System c. Being mindful about ethics. - Striving to be transparent on Data, Decision making process. - Most Importantly adhering to Data Privacy Regulations and implementing Best practice for secure and Confidential data.