Addressing Algorithmic Bias • Muniba Talha • GOTO 2022
This presentation was recorded at GOTO Copenhagen 2022. #GOTOcon #GOTOcph
https://gotocph.com
Muniba Talha - Lecturer at Copenhagen School of Design and Technology
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ABSTRACT
Algorithmic bias can be described as systematic and repeatable errors in a computer system that create "unfair" outcomes, such as "privileging" one category over another in ways different from the intended function of the algorithm. Machine learning or AI develops the same biases as humans when it comes to collecting, categorizing, producing, and interpreting data. The issue arises for a number of reasons, but the most prolific reason stems from the initial design and programming of the algorithm; the unintended or unanticipated use or the decisions relating to the way data is coded, collected, selected or used to train the algorithm which leads to poorly calibrated models that only produce biased results.
AI algorithmic bias is everywhere, according to the Center for Applied AI at Chicago Booth in their recently released playbook. Machine Learning, AI and Data Driven Decision Making is spreading ever deeper into all kinds of operations, influencing life-critical decisions such as who gets a job, who gets a loan and what kind of medical treatment a patient receives. This makes the potential risk of algorithmic bias even more significant.
This talk focuses on strategies for Addressing, Avoiding and Mitigating AI Algorithmic Bias. [...]
TIMECODES
00:00 Intro
01:10 Heuristics & biases
03:43 The cognitive bias codex
04:45 Algorithmic bias
07:00 Examples
14:57 Where does bias enter the algorithms?
17:04 Fair algorithms
20:26 Analysing trade-offs when choosing who to protect from algorithmic bias
23:58 Protecting groups, protecting individuals
25:12 Accuracy is fairness
28:23 Combating bias in algorithms
31:20 Screening algorithms for bias
31:51 What can we control?
35:41 Algorithmic auditing
38:06 Challenges with addressing algorithmic bias
39:33 Mitigating algorithmic bias
43:02 Outro
Download slides and read the full abstract here:
https://gotocph.com/2022/sessions/221...
RECOMMENDED BOOKS
Fabio Pereira • Digital Nudge • https://amzn.to/3yhxJu9
Daniel Kahneman • Thinking, Fast and Slow • https://amzn.to/2XmJEtf
Thaler & Sunstein • Nudge • https://amzn.to/3CglrmX
Dan Ariely • Predictably Irrational • https://amzn.to/3lyDBd7
Robert B Cialdini • Influence, New and Expanded • https://amzn.to/3tL8GxB
Cathy O'Neil • Weapons of Math Destruction • https://amzn.to/3EO8Bi7
Nir Eyal • Indistractable • https://amzn.to/3EE3Pn9
Eckhart Tolle • The Power of Now • https://amzn.to/39yjS7r
Linda Rising • Design Patterns in Communications Software • https://amzn.to/2XsxDCg
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#Bias #Biases #Heuristics #AlgorithmicBias #FamiliarityHeuristic #Stereotyping #AI #ML #DataScience #AlgorithmicAuditing
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