Art Kreimer

Product Leader writing about Artificial Intelligence, Machine Learning, Leadership and Product Management.

This is Part II of the AI for PMs series. See AI for PMs series Part I on AI use cases and what to do before you even start working on AI product.

Introduction

In AI for PMs Part I, I discussed the history of Artificial Intelligence, the diversity of applications and AI use cases, and highlighted key considerations to keep in mind before embarking on the development of an AI product. In this part, I will delve into crucial aspects to be mindful of during the development of an AI product. I will address the following topics, provide examples, and suggest potential mitigation strategies:

  • Bias and Unfairness
  • Explainability
  • Privacy, Compliance, and Security
  • Continuous Evaluation

Now, let’s explore each of these areas in detail.

Bias and Unfairness

AI algorithms can perpetuate or even exacerbate existing biases in society. This can happen when algorithms are trained on biased datasets or if their structures inadvertently introduce bias.

Examples:

  1. Gender Bias in Resume Screening: Algorithms that favour resumes with male-dominated activities can perpetuate gender imbalances in certain industries.
  2. Racial Bias in Facial Recognition: These systems may struggle to accurately identify individuals from diverse ethnic backgrounds, leading to potential injustices.

Mitigation Strategies:

  • Employ diverse training datasets.
  • Perform algorithmic fairness audits.
  • Involve a diverse team in the development and validation process.

Explainability

The “black box” nature of complex AI algorithms often hinders transparency, making explaining model decisions and outcomes difficult. In some highly regulated industries like healthcare and banking it could be a real issue.

Examples:

  1. Healthcare Diagnostics: If a machine learning model predicts a particular diagnosis, clinicians must understand why so they can make informed decisions.
  2. Loan Approval Systems: Knowing why a loan application was rejected can help applicants improve their financial behaviour.

Mitigation Strategies:

  • Use explainable AI models or techniques like LIME or SHAP.
  • Provide clear documentation for internal algorithms and decision-making processes.

Privacy, Compliance, and Security

Ensuring user privacy and compliance with data protection regulations is essential. This includes safeguarding user data through techniques like data anonymization and encryption.

Examples:

  1. Healthcare Data: Personal health information should be anonymized before being processed by AI models.
  2. Financial Transactions: Protecting users’ banking and credit card information using encryption and secure channels is paramount.

Mitigation Strategies:

  • Adhere to GDPR or similar data protection regulations.
  • Use privacy-preserving techniques like differential privacy.

Continuous Evaluation

Even more than any regular software products, AI systems require ongoing monitoring and assessment to ensure they meet desired performance criteria and remain relevant. Without the most recent data, AI model performance might deteriorate.

Examples:

  1. Real-Time Fraud Detection: The AI model must adapt and improve as fraudsters change tactics.
  2. E-commerce Recommendation Systems: As product inventory or consumer behaviour changes, the recommendation algorithms should evolve.

Mitigation Strategies:

  • Work with your Data Scientists and ML Engineers to develop a plan to continuously monitor model performance and create a continuous data feed to ensure the model has all the latest data needed.
  • Regularly collect user feedback and update the model as needed.

You can build more ethical, efficient, and effective AI systems by being attentive to the aforementioned areas.

Conclusion

AI offers numerous opportunities for innovation in product management. Being aware of its limitations can significantly improve the outcomes of your projects and make you a better Product Manager.

Be curious, follow AI trends, learn and create products that solve customer pain points and delight customers with the help of AI/ML features and algorithms.

Additional Resources

This article is just a primer to provide some knowledge and kick off your curiosity to learn more. Here is a short list of articles, courses and books to keep you going:

Comments

You May Also Enjoy