AI/Data Analytics (AIDA): Key concepts, examples & risks
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The document discusses key concepts and capabilities of AI and data analytics, highlighting issues such as the potential for bias in automated systems and the oversimplification of human tasks. It emphasizes the importance of meaningful data mapping and the risks associated with predictive modeling and profiling. Additionally, the document touches on the role of trade unions in addressing AI challenges and the need for effective reskilling and upskilling of employees.
![https://www.weforum.org/reports/the-future-of-jobs-report-2018
“Insufficient reskilling and upskilling:
Employers indicate that they are set to
prioritize and focus their re- and
upskilling efforts on employees
currently performing high-value
roles as a way of strengthening their
enterprise’s strategic capacity
[...]
In other words, those most in
need of reskilling and upskilling
are least likely to receive such
training.” (p.ix)](https://image.slidesharecdn.com/aidaethics-acossintro-sept2019-191213060117/85/AI-Data-Analytics-AIDA-Key-concepts-examples-risks-30-320.jpg)
AI/Data Analytics (AIDA): Key concepts, examples & risks
- 1. AI/Data Analytics (AIDA) Key concepts, examples & risks UTS / ACOSS meeting, 24 Sept. 2019 Simon Buckingham Shum & Kirsty Kitto Connected Intelligence Centre, University of Technology Sydney cic.uts.edu.au
- 2. Overview of key AI capabilities ai4kids.org
- 3. Overview of key AI capabilities automation of routine cognitive tasks
- 4. Overview of key AI capabilities automation of routine cognitive tasks but (1) companies may oversimplify a “routine” task ignoring important human judgements, or (2) go well beyond “routine” to advanced cognition and this is where the problems lie
- 5. Overview of key AI capabilities
- 6. “From clicks to constructs” AIDA tools must map from the low level data signals that computers log, to higher level categories that are meaningful to humans Issues: This mapping always makes assumptions, which may be accidental or intentional, and can be challenged
- 7. What can a computer game tell us about a student’s “Conscientiousness”? “From clicks to constructs” Educational example
- 8. What can a computer game tell us about a student’s “Conscientiousness”?
- 9. What can a computer game tell us about a student’s “Conscientiousness”?
- 10. Shute, V. J. and M. Ventura (2013). Stealth Assessment: Measuring and supporting learning in video games. Cambridge, MA, MIT Press. Figure 5 from report to The John D. and Catherine T. MacArthur Foundation Reports on Digital Media and Learning http://myweb.fsu.edu/vshute/pdf/Stealth_Assessment.pdf What can a computer game tell us about a student’s “Conscientiousness”?
- 11. “From clicks to constructs” Substitute “student” with “employee” “Employee is competent” “Responsive” “Expert” “Team player”
- 12. Making streams of data meaningful: Substitute “student” with “employee” “Employee is competent” “Responsive” “Expert” “Team player” These are like normal performance KPIs: a set of behaviours set as approximate indicators that you have the qualities the job requires. (We can debate whether they’re sensible of course)
- 13. Making streams of data meaningful: Substitute “student” with “employee” “Employee is competent” “Responsive” “Expert” “Team player”
- 14. Flawed metrics of teacher quality 14 http://vamboozled.com https://www.edutopia.org/blog/vams-instructor-effectiveness-unpacking-debate-david-stroupe “Value Added Models” (VAMs) are automated assessments of teacher quality, causing huge controversy in the US
- 15. Natural Language Processing (text analytics) NLP enables computers to understand human speech and writing (e.g. Siri; language translation) Issues: • risk of bias in the data on which it was trained • how effectively it feeds back to the user • whether it is clear if you’re talking to a human or AI (e.g. over the phone)
- 16. Example (UTS): instant feedback on writing 16 https://uts.edu.au/acawriter
- 17. Shortlisting for interviews: Resumé analysis 17 https://www.weforum.org/agenda/2019/05/ai-assisted-recruitment-is-biased-heres-how-to-beat-it/ Examples are now being documented of systematic gender and racial bias in automated systems, because the training data of ‘desirable employees’ described a white, male world.
- 18. Personal assistant example: booking a table 18 Impressive but controversial Google demo in which an AI system booked a table over the phone https://youtu.be/-RHG5DFAjp8
- 19. Predictive modelling “Based on the past, statistically speaking, we expect the future to look like this…” Issues: What if there are good reasons to think that the past is not, or should not be, a good guide to the future? • If data from the past has biases that we now recognise, we should not perpetuate those.
- 20. Predicting future criminality 20 https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing Statistical model powering a product used in US courts, to judge the likelihood of reoffending, has been demonstrated to be systematically biased against blacks
- 21. Image and video analysis Machine learning enables computers to classify images or videos Issues: As with any machine learning system, like a child, it learns based on what examples it’s shown. If the examples are a distorted view of the world, it will learn and perpetuate those categories.
- 22. Facial recognition is becoming a commodity service22 https://aws.amazon.com/rekognition/ https://medium.com/@Joy.Buolamwini/response-racial-and-gender-bias-in-amazon-rekognition-commercial-ai-system-for-analyzing-faces-a289222eeced
- 23. So, in the future, we’ll be able to track all racial features at equally high definition (fairness) — nobody will be left out of the surveillance (ethics) https://blogs.microsoft.com/ai/gender-skin-tone-facial-recognition-improvement/
- 24. S.A.F.E Face initiative 24 https://www.safefacepledge.org
- 25. Profiling & recommendation A user is guided towards a choice based upon a profile of similar users and/or past behaviour Issues: Profiles can be poor and the recommended service or product undesired. Sometimes the user cannot overrule the recommendation. Sometimes doing so is annoying or difficult.
- 26. Shopping... 26
- 27. 27
- 28. Trade Unions: both resisting and engaging with AI Issues: We need trust-building conversations for an informed dialogue, and reskilling pathways
- 29. Teaching union concerns about AI-powered learning platforms https://twitter.com/AGavrielatos/status/1121704316069236739 https://twitter.com/hashtag/TellPearson
- 30. https://www.weforum.org/reports/the-future-of-jobs-report-2018 “Insufficient reskilling and upskilling: Employers indicate that they are set to prioritize and focus their re- and upskilling efforts on employees currently performing high-value roles as a way of strengthening their enterprise’s strategic capacity [...] In other words, those most in need of reskilling and upskilling are least likely to receive such training.” (p.ix)
- 31. RSA Future Work Centre https://www.thersa.org/action-and-research/rsa-projects/economy-enterprise-manufacturing-folder/the-future-of-work Excellent resources at sites such as...
- 32. AI Now Institute Data & Society Institute https://ainowinstitute.org https://datasociety.net Excellent resources at sites such as...
- 33. There are ways to empower employees if they are given a genuine voice in the AIDA design process 34