Michael Kearns: Algorithmic Fairness, Privacy & Ethics | Lex Fridman Podcast #50 | Summary and Q&A

November 19, 2019
Lex Fridman Podcast
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Michael Kearns: Algorithmic Fairness, Privacy & Ethics | Lex Fridman Podcast #50


The book explores the challenges of algorithmic fairness, privacy, and ethics, and suggests a new approach for incorporating ethical considerations into AI systems.

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Key Insights

  • 🤝 Michael Kern's is a professor at the University of Pennsylvania and co-author of the book "Ethical Algorithm".
  • 🧠 Kern's has expertise in various fields, including machine learning, game theory, quantitative finance, and computational social science.
  • 📚 Kern's favorite novel is "Infinite Jest" by David Foster Wallace, which had a significant influence on him.
  • 💡 Kern's and his colleague, Aaron Roth, approached ethical questions from a technical angle but also sought input from moral philosophers.
  • 🤖 Algorithmic solutions can address issues of fairness and privacy in various contexts, but there are still challenges, including defining fairness and determining trade-offs.
  • 🌐 Algorithms should consider individual fairness as well as group fairness, but finding a balance between the two is complex and requires further research.
  • 🔍 Machine learning algorithms can inadvertently perpetuate biases and discrimination, and efforts should be made to address and mitigate these issues.
  • 💻 Computer scientists should be more involved in interdisciplinary research to address ethical concerns and work towards fair and ethical algorithms.


the following is a conversation with Michael Kern's he's a professor at the University of Pennsylvania and a co-author of the new book ethical algorithm that is the focus of much of this conversation it includes algorithmic fairness bias privacy and ethics in general but that is just one of many fields that Michael's a world-class researcher in som... Read More

Questions & Answers

Q: How are algorithmic fairness and privacy different from other ethical considerations in AI systems?

Algorithmic fairness and privacy are unique ethical considerations in AI systems because they involve making decisions about protecting specific groups and defining harm. These concepts require careful discussion and input from stakeholders to determine the right definitions and trade-offs involved.

Q: How do algorithmic solutions address issues of fairness and privacy in AI systems?

Algorithmic solutions can address obvious cases of unfairness and privacy violations, but they face challenges in handling more complex situations. These solutions can mitigate biases and violations, but there is an ongoing need to refine definitions and consider individual fairness within various subgroups.

Q: Why is human input essential in determining ethical algorithms?

Human input is crucial because ethical considerations in AI systems involve societal values and subjective notions of fairness. Computer scientists should not make decisions about social norms or values but should instead provide algorithms that can incorporate the preferences of stakeholders and allow them to make choices aligned with their desired outcomes.

Q: How can algorithmic auditing help address issues of unfairness in AI systems?

Algorithmic auditing can identify and address unfairness by analyzing the impact and outcomes of algorithms on different groups. This process helps identify discriminatory patterns and allows for adjustments to ensure fairness, although it may not fully guarantee individual fairness and may require ongoing revisions.


In this conversation with Michael Kerns, the professor at the University of Pennsylvania and co-author of the book "Ethical Algorithm," they discuss algorithmic fairness, bias, privacy, and ethics. They also touch on topics such as learning theory, game theory, quantitative finance, and computational social science. They explore the influence of literature on Kerns' life and how their work intersects with philosophical questions. They delve into the challenges of defining and measuring ethical algorithms, the trade-offs between individual and group fairness, and the need for human input in determining the importance of different groups. They discuss the role of trade-offs and how to navigate the complex intersection between fairness, accuracy, and societal values.

Questions & Answers

Q: What books had the biggest impact on Kerns' life?

Kerns mentions that "Infinite Jest" by David Foster Wallace was his favorite novel and it influenced him greatly. He also mentions being influenced by journalistic writing when he was an English major in college.

Q: How often does Kerns return to his literary mindset when thinking about fairness and privacy?

Kerns admits that his focus is primarily on technical angles, particularly as a machine learning researcher. However, he does draw on his uncle's perspectives as a moral philosopher to ensure an ethical approach in algorithmic work.

Q: What are some challenges in defining algorithmic fairness?

Kerns discusses the tensions between how philosophers and computer scientists think about fairness. While philosophers have approached it from a humanitarian perspective, computer scientists need to think about fairness in terms of algorithmic implementation. There is currently a lack of consensus on the definitions and multiple notions of fairness exist, making it a complex problem to solve.

Q: Can algorithms be measured based on their level of ethics?

Kerns explains that measuring the ethics of an algorithm is not as straightforward as measuring other metrics like running time. Ethical considerations involve subjective judgments and can vary depending on the definition of fairness and the context in which it is applied. Kerns suggests that trade-off curves, also known as Pareto curves, could serve as an interface between researchers and policymakers to decide on the best balance between fairness and accuracy.

Q: What is the trade-off between fairness and accuracy in algorithm design?

Kerns highlights that achieving perfect fairness without any errors is unlikely in real-world scenarios. There is often a trade-off between reducing errors and minimizing fairness violations, and finding the right balance depends on the specific application and societal values. Trade-off curves can help visualize these trade-offs and facilitate decision-making.

Q: Should fairness be considered at the individual level or the group level?

Kerns explains that fairness definitions can be applied at both the group and individual levels. Group fairness aims to protect groups from discrimination, while individual fairness ensures specific individuals are not unfairly treated. Balancing these two perspectives is challenging, and researchers are working on developing more refined sub-group approaches to fairness.

Q: Is there a need for human input in determining the importance of different groups in fairness considerations?

Kerns emphasizes the importance of involving stakeholders such as policymakers and regulators in decision-making about fairness trade-offs. Determining the importance of different groups and how fairness should be prioritized requires societal consensus and should not be decided by computer scientists alone.

Q: How does bias or unfairness enter algorithms in the era of machine learning?

Kerns provides an example of face recognition systems being biased towards certain demographics if the training data primarily consists of individuals from those demographics. Biases can enter algorithms through the data collected and the algorithms' training process, leading to unfair outcomes for certain groups.

Q: What challenges are there in designing fair algorithms?

Kerns highlights the challenge of reconciling fairness definitions that protect groups with the need for fairness towards individuals within those groups. Achieving fairness for individuals becomes more complex when considering intersecting attributes, and the trade-offs between accuracy and fairness must also be addressed. There is ongoing research to address these challenges and develop algorithmic approaches that are fairer at both the group and individual levels.

Q: Can algorithmic fairness be used as a corrective mechanism for historical injustices?

Kerns suggests that algorithmic fairness can be seen as a way to account for historical injustices that may have affected certain groups' outcomes. By constraining algorithms to be fair towards specific groups, societal imbalances can be partially rectified. However, fairness definitions should be open to debate and consensus-building, as different stakeholders may prioritize fairness differently.

Q: How can algorithmic fairness be explained to non-technical stakeholders?

Kerns discusses the concept of Pareto curves, which can visually represent trade-offs between accuracy and unfairness. These curves can be used as a communication tool to help policymakers and stakeholders understand the implications of different fairness and accuracy levels and make informed decisions on the balance between the two. Trade-offs should be part of the discourse between technical researchers and non-technical decision-makers.


Algorithmic fairness is a complex and evolving field that intersects with ethics, privacy, and societal values. Defining and measuring ethical algorithms is an ongoing challenge, as fairness notions vary and trade-offs between fairness and accuracy exist. Balancing individual and group fairness, as well as addressing historical injustices, requires collective decision-making involving stakeholders. Pareto curves can provide a visual representation of trade-offs and help navigate discussions on fairness and accuracy. As algorithms increasingly impact society, it is crucial to consider the broader implications and engage in interdisciplinary dialogue to ensure ethical decision-making in algorithm design.

Summary & Key Takeaways

  • The conversation focuses on Michael Kern's research in algorithmic fairness and his co-authorship of the book "Ethical Algorithm."

  • Michael highlights the importance of fairness, privacy, and ethics in AI systems, and their impact in various fields such as machine learning, game theory, and quantitative finance.

  • The discussion delves into the challenges of defining fairness, the trade-offs involved in algorithmic decision-making, and the need for human input in ethical algorithms.

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