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Summary
The prevalence and technical relevance of machine learning algorithms have increased over the years, making predictive decisionmaking tools part of the everyday lives of online users. Most users are unaware of the widespread and normalized use of automated decisionmaking, making them completely oblivious to when machines start, and humans take over, or vice versa.The compiled edition of the handbook offers various perspectives on the current state, and future of governance of AI and related technologies. Responding to the current debates around the trustworthiness and fairness of AI systems, Dr. Turner Lee’s chapter, “Mitigating Algorithmic Biases through Incentive-Based Rating Systems,” explores how to improve upon informed consumer choice in the use of machine learning algorithms. Given that AI systems can sometimes mimic and often amplify existing systems of inequalities, there is a need to bring consumers more agency over their trust in and engagement with these models.
Show Notes
The prevalence and technical relevance of machine learning algorithms have increased over the years, making predictive decisionmaking tools part of the everyday lives of online users.
The compiled edition of the handbook offers various perspectives on the current state, and future of governance of AI and related technologies.
Responding to the current debates around the trustworthiness and fairness of AI systems, Dr. Turner Lee’s chapter, “Mitigating Algorithmic Biases through Incentive-Based Rating Systems,” explores how to improve upon informed consumer choice in the use of machine learning algorithms.
Given that AI systems can sometimes mimic and often amplify existing systems of inequalities, there is a need to bring consumers more agency over their trust in and engagement with these models.
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