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1
Introduction
2
Datadriven tools
3
Digital Poorhouse
4
Framing Metaphor
5
Moral Hazard
6
Poor Houses
7
Allegheny Family Screening Tool
8
Measuring Bias
9
Neutral Design
10
NonNeutral Design
11
Automation
12
Intention
13
Whos in the room
14
Audience Questions
15
M Relief
16
Measure Bias
17
Systems Engineering
18
Hirevue
19
Confidence Rises
20
Limitations of the Tool
21
Tech Literacy
22
Cultural Play
23
Health Data
24
Smart Toothbrush
25
Coordinated Entry System
26
Regulatory Framework
Description:
Explore the critical issue of bias in big data and artificial intelligence in this thought-provoking panel discussion from The Aspen Institute. Delve into how algorithms increasingly influence crucial decisions in our lives, from news consumption to mortgage approvals and health insurance rates. Examine the rampant bias and discrimination against marginalized communities caused by these systems, and consider whether machines are truly neutral or if human prejudices are embedded in their design. Investigate potential solutions, including ethical standards and regulation, to protect individuals from algorithmic bias. Learn from experts Jason Pontin, Surya Mattu, and Virginia Eubanks as they discuss topics such as the digital poorhouse, framing metaphors, moral hazards, and tools like the Allegheny Family Screening Tool. Gain insights into measuring bias, neutral and non-neutral design, automation, and the importance of diverse perspectives in technology development. Explore real-world examples and audience questions covering M Relief, Hirevue, health data applications, and regulatory frameworks to better understand and navigate the complex landscape of big data and AI-driven decision-making. Read more

Bias in Big Data and Artificial Intelligence - Protecting Against Algorithmic Discrimination

The Aspen Institute
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