Ruby Video
Talks
Speakers
Events
Topics
Leaderboard
Sign in
Talks
Speakers
Events
Topics
Use
Analytics
Sign in
Suggest modification to this talk
Title
Description
Augmenting Human Decision Making with Data Science by Kelsey Pedersen Humans and data science are flawed on their own. Humans lack the ability to process large volumes of information. Machines lack intuition, empathy and nuance. You’ll learn how to guide users of expert-use systems by applying data science to their user experience. Layering data science within our systems allows us to take advantage of the human-touch while leveraging our large data sets. In this talk, you’ll learn the process for sprinkling data science into your applications, the challenges we have faced along the way, and how to measure the business impact.
Date
Summary
Markdown supported
In her talk at RubyConf 2017, Kelsey Pedersen discusses the integration of data science to enhance human decision-making. She emphasizes that both data science and human judgment have inherent flaws but can work together to improve accuracy in decision-making processes. The presentation is structured into three main sections: human decision-making processes, constraints faced by humans, and ways to aid decision-makers through technology. **Key Points:** - **Human Decision-Making:** Pedersen references the dual process theory from Daniel Kahneman's "Thinking, Fast and Slow," which divides human cognition into two systems: System One (fast, intuitive) and System Two (slow, deliberate). About 95% of decisions are made using System One, highlighting its unpredictability and vulnerability to cognitive biases. - **Limits of Human Decision-Making:** Factors affecting human decisions include environmental influences, personal experiences, and cognitive biases, which lead to inconsistent and often biased outcomes. Stylists at Stitch Fix, for example, may choose different outfits from the same inventory based on mood and perceived preferences. - **Role of Data Science:** Data science can enhance decision-making by: - Offloading parts of the decision process through algorithms that suggest clothing items based on client preferences. - Providing real-time feedback on expected outcomes and allowing stylists to refine their choices. - Enabling continuous improvement of decision-making through data collection and analysis. - **Feedback Loop:** The partnership between stylists and data algorithms creates a feedback loop where human intuition informs the model, and the model aids human decisions. - **Future of Data Science:** Pedersen envisions a synergy between humans and algorithms to form what she calls "System Three," allowing for nuanced and informed decisions. She stresses continuous evolution in both human inputs and algorithmic adaptations to ensure effective outcomes. In conclusion, the presentation advocates for the powerful collaboration between human intuition and statistical insights from data science, which can lead to better decision-making processes in personalized services like Stitch Fix.
Suggest modifications
Cancel