The Future of Agile is Diversity
The “algorithm” is now an entity. It is a subject that society is talking a lot lately. In 2015, a photo app automatically tagged two Afro-American friends as gorillas. In 2016, a bot called Tay learned to be racist, Holocaust denier and that feminists “should all die and burn in hell”, in 12 hours. In less than 24 hours, it was shut down. There is unpredictability of machine learning algorithms when confronted with real people. How much bias machine learning algorithms can introduce? How much came from the data used to train the algorithms and how much came from the algorithm itself? How to create products based on machine learning avoiding gender, race, age or culture bias and others and avoiding doing harm to those groups? Yates (Communication of ACM, June 2018) said that “any remedy for bias must start with awareness that bias exists.” Page (The Difference, 2007) proposed that identity diversity (our gender, race, religion, etc.) leads to cognitive diversity (the way we think and solve problems), mainly in tasks as prediction and problem-solving. A study made by McKinsey & Company in 2014 says that diversity fosters innovation and increase financial results. So, workplace diversity can help in different ways, including to detect and reduce bias in algorithms design and execution. How much agile teams, from the beginning of software development chain, can help to minimize bias and reduce backslash to the end user? What is the role of agile when teams are built to work in a machine learning world? Agile Manifesto values individuals and interactions over processes and tools. Agile teams are built on that. Recently, Modern Agile also set two of four values based on people: make people awesome and make safety a prerequisite. Not as a causality, but, maybe, as a correlation, agile values are good evidence that we can have development environments that better support diversity. Once we have more diverse teams, we can expect better outputs (less biased) from machine learning algorithms.
Recommended
More Related Content
What's hot
What's hot (20)
Similar to The Future of Agile is Diversity
Similar to The Future of Agile is Diversity (20)
More from Karina Kohl
More from Karina Kohl (8)
Recently uploaded
Recently uploaded (20)
The Future of Agile is Diversity
- 1. FutureofAgileisDiversity TRACK: THE FUTURE OF AGILE SOFTWARE DEVELOPMENT (IEEE SOFTWARE) KARINA KOHL SILVEIRA
- 2. Head of Operations Porto Alegre’s Office Product Manager for the Identity Provider Teams (Devices and LGPD) PhD Student in Computer Science/Software Engineer KarinaKohl karina.kohl@gmail.com MSc in Computer Science - UFRGS BSc in Computer Science - UFRGS 17 years in IT Industry 10 working in Agile Teams
- 6. 2019…“Thealgorithm” stillthere… At the end of the day… what is the problem with that? WhenIentermycarinaSundaymorningandmyphoneautomaticallynotifiesme howmanytimetoarriveatmyparent’shome… Myfriends’postsinsocialmediathatIseeinmytimeline…orIdon’tsee… WhenIlogininmypreferredstreamingvideoappanditrecommendedmeanew HospitalTVShowbecauseI marathonedmypreferredonetheweekendbefore… AndIcanbealittlebitmoreneurotic:“whatif"myphone"(ormyassistant)is listeningtomyconversations?” 😱 🚑 🚘 🤔
- 8. GenderShades:IntersectionalAccuracyDisparitiesinCommercialGenderClassification∗ JoyBuolamwini and,TimnitGebru.2018 "Artificial Intelligence (AI) is rapidly infiltrating every aspect of society. From helping determine our gender and skin type balanced PPB dataset at gendershades.org who is hired, fired, granted a loan, or how long an individual spends in prison, decisions that have traditionally been performed by humans are rapidly made by algorithms. Many AI systems, e.g. face recognition tools, rely on machine learning algorithms that are trained with labeled data. It has recently been shown that algorithms trained with biased data have resulted in algorithmic discrimination."
- 9. Biasedandwrong?Facialrecognitiontechinthedock-MatthewWall BBCNewsJuly,2019(https://www.bbc.com/news/business-48842750) "Police and security forces around the world are testing out automated facial recognition systems as a way of identifying criminals and terrorists. But how accurate is the technology and how easily could it and the artificial intelligence (AI) it is powered by - become tools of oppression? With black Americans making up 37.5% of the US prison population (source: Federal Bureau of Prisons) despite the fact that they make up just 13% of the US population - badly written algorithms fed these datasets might predict that black people are more likely to commit crime."
- 10. Policefacecallstoenduseoffacialrecognitionsoftware-RobertBooth TheGuardian,July,2019(https://www.theguardian.com/technology/2019/jul/03/police-face-calls-to-end-use-of- facial-recognition-software) "Academics at the University of Essex concluded that matches in the London Metropolitan police trials were wrong 80% of the time, potentially leading to serious miscarriages of justice and infringements of citizens' right to privacy. The Essex researchers also raised concern about potential bias, citing US research in 2018 into facial recognition software provided by different companies, which found the programmes were most likely to wrongly identify dark-skinned women and most likely to correctly identify light-skinned men."
- 11. • “Anyremedyforbiasstartswithawarenessofits existence.” • “BiasontheWebreflectsbiaseswithinourselves, manifestedinsubtlerways.” • “Wemustconsiderandaccountforbiasinthedesignof Web-basedsystemsthattrulyaddresstheneedsofusers.“ BiasontheWeb-RicardoBaeza-Yates CommunicationsofACM,June2018
- 13. So…Here’sthething… If data is biased, what we should do? Better algorithms How? More diverse teams Datausedtotrainalgorithms arebiased.
- 15. https://www.nytimes.com/2008/01/08/science/08conv.html Question: "The term “diversity” has become a code word for inclusion of racial, ethnic and sexual minorities. Is that what you’re talking about?" Answer: ”I mean differences in how people think. Two people can look quite different and think similarly. Having said that, there’s certainly a lot of evidence that people’s identity groups — ethnic, racial, sexual, age — matter when it comes to diversity in thinking.” (Scott E. Page)
- 16. Cognitive Diversity The difference betweenhowweinterpret,reasonand solveproblems-howwethink. Identity Diversity Itisdeterminedbyaffiliationwithasocialgroupas gender,culture,ethnicity,religion,sexualorientation, etc. *ScottE.Page-TheDifference-2007.
- 17. "Identityattributescauseustoconstruct differentsetsofcognitivetools.” *ScottE.Page-TheDifference-2017. Identity diversity leads to Cognitive diversity mainly in Problem Solving and Predictions (“When making predictions, we have to include less accurate, diverse predictions”)
- 20. HeartofAgile* *Alistar Cockburn-https://heartofagile.com/ closely with otherstogenerateand developbetter startingideas. Communicateoftentosmooth transitions. smallprobesinitiallytolearnhow theworld really works. Expand deliveriesasyoulearnto predict and influence outcomes. periodically,alongthe way. Think about what you've learnedinyour collaborationandfromyour deliveries. thedirectionof yourideas,their technicalimplementation, and yourinternalprocesses.
- 22. But…Whyitmatters? Identity diversity is the right and ethical thing to do… But it is also the right thing to do for your business… Agileisbuiltonpeopleandhow theyinteract!
- 23. Foodforthought… Cognitive and identity speaking… Howdiversearetheteamsyou workwith?
- 24. Foodforthought… Hirethe“top10” withaspecificknowledge?No cognitive difference, everybody has the same tools… Hire“A” morediverseteam?Different identities —> different cognitive tools…
- 25. Thistalkisnotateaching…We want: • Tobringawareness • Tobringthesubjecttothelight • Toprovokethediscussion