Addressing Algorithmic Bias Muniba Talha GOTO 2022

This presentation was recorded at GOTO Copenhagen 2022. #GOTOcon #GOTOcph Muniba Talha - Lecturer at Copenhagen School of Design and Technology RESOURCES 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: RECOMMENDED BOOKS Fabio Pereira • Digital Nudge • Daniel Kahneman • Thinking, Fast and Slow • Thaler & Sunstein • Nudge • Dan Ariely • Predictably Irrational • Robert B Cialdini • Influence, New and Expanded • Cathy O'Neil • Weapons of Math Destruction • Nir Eyal • Indistractable • Eckhart Tolle • The Power of Now • Linda Rising • Design Patterns in Communications Software • #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 Sign up for updates and specials at SUBSCRIBE TO OUR CHANNEL - new videos posted almost daily.