Manish Raghavan is the Drew Houston (2005) Career Development Professor at the MIT Sloan School of Management and Department of Electrical Engineering and Computer Science. His research focuses on how algorithms and algorithmic decision-making impact society in a variety of contexts including employment and online media. He is the recipient of the 2021 ACM Doctoral Dissertation Award.

• IntroductionPart I: Theoretical Foundations for Fairness in Algorithmic Decision-Making1. Inherent Tradeoffs in the Fair Determination of Risk Scores2. On Fairness and Calibration3. The Externalities of Exploration and How Data Diversity Helps ExploitationPart II: Models of Behavior4. Selection Problems in the Presence of Implicit Bias5. How Do Classifiers Induce Agents to Behave Strategically?6. Algorithmic Monoculture and Social WelfarePart III: Application Domains7. Mitigating Bias in Algorithmic Hiring: Evaluating Claims and Practices8. The Hidden Assumptions Behind Counterfactual Explanations and Principal ReasonsPart IV: Conclusion and Future Work9. Future Directions

Manish Raghavan is the Drew Houston (2005) Career Development Professor at the MIT Sloan School of Management and Department of Electrical Engineering and Computer Science. His research focuses on how algorithms and algorithmic decision-making impact society in a variety of contexts including employment and online media. He is the recipient of the 2021 ACM Doctoral Dissertation Award.

• IntroductionPart I: Theoretical Foundations for Fairness in Algorithmic Decision-Making1. Inherent Tradeoffs in the Fair Determination of Risk Scores2. On Fairness and Calibration3. The Externalities of Exploration and How Data Diversity Helps ExploitationPart II: Models of Behavior4. Selection Problems in the Presence of Implicit Bias5. How Do Classifiers Induce Agents to Behave Strategically?6. Algorithmic Monoculture and Social WelfarePart III: Application Domains7. Mitigating Bias in Algorithmic Hiring: Evaluating Claims and Practices8. The Hidden Assumptions Behind Counterfactual Explanations and Principal ReasonsPart IV: Conclusion and Future Work9. Future Directions

Manish Raghavan is the Drew Houston (2005) Career Development Professor at the MIT Sloan School of Management and Department of Electrical Engineering and Computer Science. His research focuses on how algorithms and algorithmic decision-making impact society in a variety of contexts including employment and online media. He is the recipient of the 2021 ACM Doctoral Dissertation Award.

• IntroductionPart I: Theoretical Foundations for Fairness in Algorithmic Decision-Making1. Inherent Tradeoffs in the Fair Determination of Risk Scores2. On Fairness and Calibration3. The Externalities of Exploration and How Data Diversity Helps ExploitationPart II: Models of Behavior4. Selection Problems in the Presence of Implicit Bias5. How Do Classifiers Induce Agents to Behave Strategically?6. Algorithmic Monoculture and Social WelfarePart III: Application Domains7. Mitigating Bias in Algorithmic Hiring: Evaluating Claims and Practices8. The Hidden Assumptions Behind Counterfactual Explanations and Principal ReasonsPart IV: Conclusion and Future Work9. Future Directions