How do you ensure transparency and explainability of recommender systems to users and stakeholders?

Recommender systems are algorithms that suggest products, services, or content to users based on their preferences, behavior, or context. They are widely used by online platforms such as e-commerce, streaming, social media, and news sites to enhance user experience, engagement, and loyalty. However, recommender systems also pose ethical and social challenges, such as privacy, fairness, diversity, accountability, and trust. How do you ensure transparency and explainability of recommender systems to users and stakeholders? Here are some best practices and tips to consider.

1 Define your objectives and criteria

Before designing and implementing a recommender system, you should clearly define your objectives and criteria for success. What are you trying to achieve with your recommendations? How will you measure the performance and impact of your system? How will you balance the trade-offs between different objectives, such as accuracy, diversity, novelty, or revenue? How will you align your system with your values and principles, such as fairness, privacy, or social responsibility? By defining your objectives and criteria, you can set the expectations and boundaries for your system and communicate them to your users and stakeholders.

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Ravi T.

AI Transformation Executive | 4X CIO 100 Award | MIT | Board Member
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Recommender Systems can be valuable tool to cross sell products based of past transactions and product data. It is important to understand how many products are in scope to be recommended. A simplified Recommender Systems based of frequency tables and conditional probability should be created a a basis of more complex recommender systems. It allows to compare and explain how simple system can work better then complex system.

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2 Explain how your system works and why

One of the key aspects of transparency and explainability is to provide information and insights about how your system works and why it makes certain recommendations. This can help your users and stakeholders understand the logic and rationale behind your system, as well as the benefits and limitations of your system. You can use different methods and tools to explain your system, such as visualizations, summaries, examples, feedback mechanisms, or interactive interfaces. You should also consider the level and format of explanation that is appropriate for your target audience, depending on their background, needs, and goals.

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3 Disclose your data sources and quality

Another important aspect of transparency and explainability is to disclose your data sources and quality. Your data is the foundation of your recommender system, and it can affect the quality and reliability of your recommendations. You should inform your users and stakeholders about where you collect your data from, how you process and store your data, and what are the potential biases and errors in your data. You should also provide ways for your users and stakeholders to access, verify, correct, or delete their data, as well as to opt-out or consent to your data collection and usage.

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Sachin Sharma

Digital Transformation Advisor | Enterprise Systems | Published Author | Passionate AI Advocate
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An example from my dissertation, "Unraveling Bias and Customer Heterogeneity in E-Commerce Recommendations: Assessing Impact on Distrust," I personally did the following- 1. Obtained informed consent from participants before they engaged in the survey to ensure ethical compliance and voluntary participation. 2. Examined customer bias and variability within e-commerce recommendation systems and its subsequent effect on consumer distrust towards recommendations. Prior to the data collection, I implemented a series of manipulation checks and attention assessments within the questionnaire distributed to online shoppers 3. Lastly, I ensured the dataset was free from duplicated entries and responses generated by automated bots.

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4 Evaluate and monitor your system's impact and behavior

Finally, you should evaluate and monitor your system's impact and behavior on a regular basis. You should not only assess your system's technical performance, such as accuracy, speed, or scalability, but also its ethical and social impact, such as fairness, diversity, or trust. You should also monitor your system's behavior for any anomalies, errors, or malicious activities, such as manipulation, fraud, or hacking. You should have mechanisms and procedures to detect, report, and resolve any issues or risks that may arise from your system's operation. You should also update and improve your system based on the feedback and outcomes of your evaluation and monitoring.

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Ravi T.

AI Transformation Executive | 4X CIO 100 Award | MIT | Board Member
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I have built multiple recommender systems and found that it is very important to understand the business objectives. If the goal is to help customer pick the right product, then a simple recommender systems based of transaction count should be built using market based analysis. If the goal is to promote new product then it marketing campaigns should be used instead of recommender systems. To ensure recommender systems are fair & bias free, test plan and fairness metrics should be defined. Statistical Parity Difference (SPD) can help identify difference between majority and protected classes likely to receive a favorable outcome. Another KPI is Equal Opportunity Difference (EOD), which can show deviation from equality of opportunity.

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5 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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How do you ensure transparency and explainability of recommender systems to users and stakeholders?

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1 Define your objectives and criteria

2 Explain how your system works and why

3 Disclose your data sources and quality

4 Evaluate and monitor your system's impact and behavior

5 Here’s what else to consider

Recommender Systems

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