Masters in Business: At The Money – Algorithmic Harm with Professor Cass Sunstein

Episode Release Date: June 4, 2025 | Host: Barry Ritholtz | Guest: Professor Cass Sunstein

In this enlightening episode of Masters in Business, Bloomberg host Barry Ritholtz engages in a profound discussion with Harvard Law School Professor Cass Sunstein about the pervasive influence of algorithms in our daily lives. Drawing from Sunstein’s expertise and his co-authored book, Algorithmic Harm: Protecting People in the Age of Artificial Intelligence, the conversation delves into the nuanced ways algorithms shape consumer behavior, market dynamics, and societal structures.

1. Defining Algorithmic Harm

Professor Sunstein begins by establishing a clear definition of algorithmic harm, illustrating it through familiar contexts:

"If people don't know much, let's say, about healthcare products, an algorithm might know that they're likely not to know much and might say, we have a fantastic baldness cure for you. So that's exploitation of absence of information. That's algorithmic harm."
— Cass Sunstein [02:15]

He emphasizes that algorithmic harm arises when algorithms exploit consumers' lack of information or behavioral biases, leading to manipulation and exploitation.

2. Everyday Impacts of Algorithms

Ritholtz and Sunstein explore both obvious and subtle instances where algorithms influence everyday decisions:

• Pricing Strategies: Algorithms adjust prices based on a consumer’s economic status, allowing wealthier individuals to be charged more—a practice Sunstein views as market-efficient.

  "If you have a lot of money, the algorithm knows that maybe the price will be twice as much as it would be if you were less wealthy. That, I think is basically okay."
  — Cass Sunstein [04:03]

• Targeted Content: Personalization can lead to cultural polarization. Sunstein warns that algorithms reinforcing specific tastes (e.g., always presenting Olivia Rodrigo to fans) can lead to cultural segmentation, hindering the development of diverse individual preferences.

  "People's tastes will calcify, and we're going to get very Balkanized culturally."
  — Cass Sunstein [05:48]

3. Cultural and Societal Effects

The discussion shifts to the broader societal implications of algorithm-driven content delivery:

"Algorithms can echo chamber you... people will be living in algorithm driven universes that are very separate from one another and they can end up not liking each other very much."
— Cass Sunstein [07:03]

Sunstein highlights the risk of algorithms creating isolated information bubbles, exacerbating societal divisions and undermining mutual understanding.

4. Algorithms and Democracy

Ritholtz probes the threat algorithms pose to democratic processes, particularly through the manipulation of news feeds:

"Algorithms and large language models... can create situations in which people in some city are seeing stuff that creates a reality that's very different from the reality that people in another city are saying. And that can be a real problem for understanding one another, and also for mutual problem solving."
— Cass Sunstein [08:52]

Sunstein underscores the significant danger algorithms present to a self-regulating democracy by fostering divergent realities that impede constructive dialogue and collective decision-making.

5. Price and Quality Discrimination

A critical segment of the conversation revolves around algorithmic discrimination in pricing and product quality:

• Price Discrimination: Adjusting prices based on perceived wealth is seen as acceptable and market-friendly.

  "If people who have a lot of resources are given an offer that's not as seductive as one that is given to people who don't have a lot of resources just because the price is higher for the rich than the poor, that's okay."
  — Cass Sunstein [09:43]

• Quality Discrimination: Providing different product qualities based on consumer behavior is permissible if consumers are informed and sophisticated.

  "If it's the case that... the algorithm is aware of the fact that certain consumers are particularly likely not to have relevant information, then everything goes haywire."
  — Cass Sunstein [11:44]

Sunstein draws a clear line between acceptable market strategies and exploitative practices that harm vulnerable consumers.

6. Artificial Intelligence and Future Impacts

The conversation advances to the role of Artificial Intelligence (AI) in enhancing algorithmic capabilities:

"Large language models that track your prompts can know a lot about you... we need to have privacy protections that are working there."
— Cass Sunstein [14:44]

Sunstein discusses how AI, exemplified by tools like ChatGPT, can amass detailed personal information, amplifying both the benefits and risks of algorithmic interactions. He stresses the need for robust privacy safeguards to mitigate the potential misuse of AI-driven data collection.

7. Surge Pricing vs. Price Gouging

Exploring the ethical boundaries of dynamic pricing, Sunstein differentiates between legitimate market adjustments and exploitative price gouging:

"If there's a spectacular need for something... the market inflation of the cost... it's okay. Now if it's the case that people under short term pressure... are especially vulnerable... then there's a behavioral bias... that's something that can damage vulnerable consumers a lot."
— Cass Sunstein [17:03]

He posits that while surge pricing in response to genuine demand spikes is acceptable, exploiting consumers' emotional states during crises crosses into harmful territory.

8. Regulatory Perspectives: US vs. Europe

Finally, the discussion addresses regulatory frameworks governing algorithmic practices:

"Think we're behind [Europe] in the sense that we're less privacy focused. But it's not clear that that's bad... what are we going to do about it?"
— Cass Sunstein [18:37]

Sunstein observes that the U.S. lags behind Europe in privacy-centric regulations but notes that neither region has fully addressed the core issues of algorithmic exploitation. He advocates for enhancing consumer protection through transparency and education rather than relying solely on heavy-handed regulation.

Conclusion

Barry Ritholtz and Cass Sunstein provide a comprehensive examination of algorithmic harm, emphasizing the delicate balance between leveraging algorithmic efficiencies and protecting consumers from manipulation. Sunstein’s insights highlight the urgent need for greater algorithmic transparency and robust consumer education to navigate the complexities introduced by AI and sophisticated algorithms in modern markets and societies.

For individuals navigating the American economy, Sunstein's perspectives offer valuable guidance on recognizing and mitigating the adverse effects of algorithmic decision-making, heralding a call to action for informed consumerism and proactive regulatory measures.