This presentation was recorded at GOTO Copenhagen 2022. #GOTOcon #GOTOcph https://gotocph.com Muniba Talha - Lecturer at Copenhagen School of Design and Technology RESOURCES https://twitter.com/mtalha2 https://linkedin.com/in/m-talha 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/2217/addressing-algorithmic-bias 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 https://twitter.com/GOTOcon https://www.linkedin.com/company/goto- https://www.facebook.com/GOTOConferences #Bias #Biases #Heuristics #AlgorithmicBias #FamiliarityHeuristic #Stereotyping #AI #ML #DataScience #AlgorithmicAuditing Looking for a unique learning experience? Attend the next GOTO conference near you! Get your ticket at https://gotopia.tech Sign up for updates and specials at https://gotopia.tech/newsletter SUBSCRIBE TO OUR CHANNEL - new videos posted almost daily. https://www.youtube.com/user/GotoConferences/?sub_confirmation=1
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