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AI & Labor Markets
Joaquim Jorge
AI: toolbox
and
techniques
Supervised
Learning
(classifies things)
Unsupervised
learning
Generative AI
Reinforcement
learning
Supervised Learning
Input (A) Classification (B) Application
Mail Spam (0/1) Filter Spam
Ad, User Profile. Clicks? (0/1) Online Advert
Image, Radar Pos. Cars on Road Self Driv Cars
RX Image Diagnostics Healthcare
Phone Image Defects? Quality Control
Audio Record Text Transcription Speech Recognition
Restaurant Review Sentiment (Pos/Neg) TripAdvisor / Yelp
Generative AI 2017  2020
Small-Scale AI Systems
Large-Scale AI Systems
(Transformers)
How LLMs Work
Use pretrained model (A B): predict the next word
Input (A) Output (B)
My favorite food is steak
My favorite food is steak
My favorite food is steak and
My favorite food is steak and french
My favorite food is steak and french fries
and
french
fries
When we train a Large AI System with Billions or Trillions of
words, we get a Large Language Model (e.g. GPT)
Large Language Models
There are MANY
ChatGPT (Open AI)
Bard (Google)
Bing (Microsoft)
LLAMA3
---
ChatGPT
GPT: Generative
Pretrained Transformer
User: what is on the image?
ChatGPT:
The image shows a simple,
modern wall clock with a
white face and black
markings.
The numbers 1 through 12 are displayed in a
traditional layout. The clock hands are positioned
such that the hour hand is
pointing to the number
10 and the minute hand
is pointing to the number
2, which indicates a time
of 10 o'clock.
The background is plain and light-colored, providing
a stark contrast to the black elements of the clock,
which makes the image look clean and minimalistic.
ChatGPT can’t tell time! Why?
BBC 24/Feb/24
Chatbot
offered 100%
discount for
bereavement
travel…
Which the
airline did not
feature!
Diffusion: The
Art of AI in
Generating
Images
Artificial intelligence model that
generates detailed images from
textual descriptions.
A deep learning process called
"diffusion," which learns to turn
noise into structured images.
Bias in Hiring
OpenAI’s GPT Shows Bias in Résumé
Ranking Experiment
Bloomberg March 8, 2024 at 9:08 PM GMT
“systematically produces biases that disadvantage
groups based on their names.”
In 2021, LinkedIn
discovered that its AI-
driven
recommendation
system for job
candidates was
producing biased results,
favoring male over
female candidates.
This bias arose despite
efforts to exclude
overtly
discriminatory data
such as gender, race,
and age.
Efficiency  Bias
… one employer prepared STEM job
advertisements to be gender-neutral,
but the algorithm disproportionately
displayed them to male candidates
because the cost of displaying them to
female candidates was higher, and the
algorithm had been programmed to be
cost efficient.
Lexology, April 28, 2023
Facial and voice recognition
software […] downgrade
applicants of different
races or with speech
impediments,
effectively
discriminating on the
basis of race or
disability.
Firing Decisions
AI Track Warehouse
Workers' Productivity
Amazon "Time off Task" (TOT)
system monitors how long
employees spend on activities
not directly related to their
assigned tasks.
This includes breaks and idle
time.
Continuous underperformance
as flagged by AI can lead to
automated termination
Terminating Employees automatically
“terminating
employees without a
manager’s direct
involvement, solely
based on
productivity metrics
tracked by the
system.”
The Verge, 2019
in 2023 TOT was again
reported in unfair termination
Uber uses AI for Driver Terminations
Uber has utilized AI algorithms to monitor driver performance and
behavior. The system tracks metrics such as ride acceptance
rates, customer ratings, and reports of misconduct
“Drivers from minority backgrounds reported higher rates of
deactivation, suggesting that customer bias influenced the AI's
termination decisions.”
Euronews, Aug 8, 2023
Facial Recognition
Issues
“The black driver, who worked
[on] Uber from 2016 until April
2021, filed an employment
tribunal claim alleging his
account was illegally
deactivated when facial-
verification software
used to log drivers on to the
ride-hailing app decided he
was not who he said he
was.” Guardian, 5 Oct 2021
The Risks of Artificial Intelligence: COMPAS
Correctional Offender Management Profiling for Alternative Sanctions
(
Can we Trust this?
(analysis by ProPublica.org)
Subsequent offenses:
1 grand theft
Subsequent offenses:
None
Subsequent offenses:
None
Subsequent offenses:
3 drug possessions
The need (rush?)
for AI regulation
Mitigation
Strategies
Bias Audits
Regularly auditing AI
systems to identify and
mitigate biases.
analyzing input data
for
representativeness
& checking output for
discriminatory
patterns.
Diverse Training Data
Ensuring that training data ARE diverse and
representative of all demographic groups to reduce
inherent biases.
Human Oversight
Implementing human oversight to review and validate
AI decisions, particularly in sensitive areas like hiring.
GDPR (2018) EU GDPR
People have a right to explanation!
“Companies should commit to ensuring
systems that could fall under GDPR,
including AI, will be compliant. The threat
of sizeable fines of €20 million or 4% of
global turnover provides a sharp incentive.
Article 22 of GDPR empowers individuals
with the right to demand an explanation
of how an AI system made a decision that
affects them. ”
- European Commission
Explainable AI
methods and techniques that make the outputs of AI
systems understandable to humans.
XAI: Transparency
XAI can highlight which features (like
certain keywords or career gaps) are
disproportionately influencing the AI’s
decisions
XAI: Accountability
By making AI decisions interpretable, XAI holds AI systems
accountable for their outputs.
Accountability ensures that AI developers and users can identify
and address any discriminatory practices.
Ex:
A bank using XAI for loan approvals can track and explain why
certain demographics might be unfairly rejected, allowing them
to adjust the model to ensure fairness.
XAI: Bias
Detection and
Correction:
XAI helps in detecting bias by
showing how different factors
contribute to the AI’s
decisions.
detailed breakdown  easier
to spot and rectify biased
influences.
Ex: “years of continuous
service,” might unfairly
disadvantage women
who take career
~~~breaks for
caregiving
XAI: Enhanced Trust
Employees and job candidates are more likely to trust AI-driven hiring
tools if they know the basis on which these tools make their
recommendations.
The Future of Labor
It is very difficult to Predict – Especially the
Future (Mark Twain)
The Luddites
Utopia
AI leads to happier and more
satisfied workers
by automating repetitive tasks.
realignment of work-life balance
The 40-hour week becoming
unnecessary
workers and companies
embrace flexibility and
efficiency.
Utopia
demand for new skills
create novel career
opportunities
blurring the distinction
between white-collar and
blue-collar jobs.
Utopia
reduced physical labour
demands, providing
upskilling opportunities
for everyone,
Create economic
opportunities across the
board.
Dystopia
where the future of work is
reshaped by AI in negative ways,
fundamentally altering the
employment landscape, worker
rights, and social structures.
Widening the economic divide
Dystopia
beneficiaries: technology & capital
owners
Wild
Capitalism
tech giants + those
with control over AI
infrastructures gain
disproportionately =>
stark inequalities
Google, OpenAI,
Microsoft, Amazon
Increased Job
precarity
Gig economy
Lean enterprises with freelance
workforce
Casual Labor
Are we building new
employment and labor
market models future
ready for a digital era
or going backwards to
casual labor albeit
supported by digital
technology?
Erosion of Workers’
Rights and Power
The shift towards
more automated
systems and reliance
on AI could
undermine the
bargaining power of
workers and trade
unions.
Taylorism is alive and kicking in the
digital age!
Educational and
Skills Mismatch
As demand for new skills
to work alongside or
develop AI
=> mismatch between
existing skills and
economy needs
Need novel
approaches to
education
Universities reaching
their Netflix moment?
many workers, especially older or
declining industries (coal),
struggle to find employment without
substantial retraining.
Education systems will almost
certainly lag behind in adapting to
new demands
Social and
Psychological
Impacts
Increased
surveillance and
loss of autonomy

Stress, anxiety +
alienation
Social and
Psychological
Impacts
Loss of work = Loss of
identity and
community
 Declining mental
health
My take: AI is neither good or bad
My Take
There are only good and bad people.
As a society, do we
have the leadership to
ensure that AI
redefines work in
positive ways
creating “greater
freedom and
fulfillment?”

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AI+Labor Markets Presentation to CSM-16-may-2024

  • 1. AI & Labor Markets Joaquim Jorge
  • 2.
  • 4. Supervised Learning Input (A) Classification (B) Application Mail Spam (0/1) Filter Spam Ad, User Profile. Clicks? (0/1) Online Advert Image, Radar Pos. Cars on Road Self Driv Cars RX Image Diagnostics Healthcare Phone Image Defects? Quality Control Audio Record Text Transcription Speech Recognition Restaurant Review Sentiment (Pos/Neg) TripAdvisor / Yelp
  • 5. Generative AI 2017  2020 Small-Scale AI Systems Large-Scale AI Systems (Transformers)
  • 6. How LLMs Work Use pretrained model (A B): predict the next word Input (A) Output (B) My favorite food is steak My favorite food is steak My favorite food is steak and My favorite food is steak and french My favorite food is steak and french fries and french fries When we train a Large AI System with Billions or Trillions of words, we get a Large Language Model (e.g. GPT)
  • 7. Large Language Models There are MANY ChatGPT (Open AI) Bard (Google) Bing (Microsoft) LLAMA3 ---
  • 9. User: what is on the image? ChatGPT: The image shows a simple, modern wall clock with a white face and black markings. The numbers 1 through 12 are displayed in a traditional layout. The clock hands are positioned such that the hour hand is pointing to the number 10 and the minute hand is pointing to the number 2, which indicates a time of 10 o'clock. The background is plain and light-colored, providing a stark contrast to the black elements of the clock, which makes the image look clean and minimalistic. ChatGPT can’t tell time! Why?
  • 10. BBC 24/Feb/24 Chatbot offered 100% discount for bereavement travel… Which the airline did not feature!
  • 11. Diffusion: The Art of AI in Generating Images Artificial intelligence model that generates detailed images from textual descriptions. A deep learning process called "diffusion," which learns to turn noise into structured images.
  • 13.
  • 14. OpenAI’s GPT Shows Bias in Résumé Ranking Experiment Bloomberg March 8, 2024 at 9:08 PM GMT “systematically produces biases that disadvantage groups based on their names.”
  • 15. In 2021, LinkedIn discovered that its AI- driven recommendation system for job candidates was producing biased results, favoring male over female candidates. This bias arose despite efforts to exclude overtly discriminatory data such as gender, race, and age.
  • 16. Efficiency  Bias … one employer prepared STEM job advertisements to be gender-neutral, but the algorithm disproportionately displayed them to male candidates because the cost of displaying them to female candidates was higher, and the algorithm had been programmed to be cost efficient. Lexology, April 28, 2023
  • 17. Facial and voice recognition software […] downgrade applicants of different races or with speech impediments, effectively discriminating on the basis of race or disability.
  • 19. AI Track Warehouse Workers' Productivity Amazon "Time off Task" (TOT) system monitors how long employees spend on activities not directly related to their assigned tasks. This includes breaks and idle time. Continuous underperformance as flagged by AI can lead to automated termination
  • 20. Terminating Employees automatically “terminating employees without a manager’s direct involvement, solely based on productivity metrics tracked by the system.” The Verge, 2019 in 2023 TOT was again reported in unfair termination
  • 21. Uber uses AI for Driver Terminations Uber has utilized AI algorithms to monitor driver performance and behavior. The system tracks metrics such as ride acceptance rates, customer ratings, and reports of misconduct “Drivers from minority backgrounds reported higher rates of deactivation, suggesting that customer bias influenced the AI's termination decisions.” Euronews, Aug 8, 2023
  • 22. Facial Recognition Issues “The black driver, who worked [on] Uber from 2016 until April 2021, filed an employment tribunal claim alleging his account was illegally deactivated when facial- verification software used to log drivers on to the ride-hailing app decided he was not who he said he was.” Guardian, 5 Oct 2021
  • 23. The Risks of Artificial Intelligence: COMPAS Correctional Offender Management Profiling for Alternative Sanctions (
  • 24. Can we Trust this? (analysis by ProPublica.org) Subsequent offenses: 1 grand theft Subsequent offenses: None Subsequent offenses: None Subsequent offenses: 3 drug possessions
  • 25.
  • 26. The need (rush?) for AI regulation
  • 28. Bias Audits Regularly auditing AI systems to identify and mitigate biases. analyzing input data for representativeness & checking output for discriminatory patterns.
  • 29. Diverse Training Data Ensuring that training data ARE diverse and representative of all demographic groups to reduce inherent biases.
  • 30. Human Oversight Implementing human oversight to review and validate AI decisions, particularly in sensitive areas like hiring.
  • 31. GDPR (2018) EU GDPR People have a right to explanation! “Companies should commit to ensuring systems that could fall under GDPR, including AI, will be compliant. The threat of sizeable fines of €20 million or 4% of global turnover provides a sharp incentive. Article 22 of GDPR empowers individuals with the right to demand an explanation of how an AI system made a decision that affects them. ” - European Commission
  • 32. Explainable AI methods and techniques that make the outputs of AI systems understandable to humans.
  • 33. XAI: Transparency XAI can highlight which features (like certain keywords or career gaps) are disproportionately influencing the AI’s decisions
  • 34. XAI: Accountability By making AI decisions interpretable, XAI holds AI systems accountable for their outputs. Accountability ensures that AI developers and users can identify and address any discriminatory practices. Ex: A bank using XAI for loan approvals can track and explain why certain demographics might be unfairly rejected, allowing them to adjust the model to ensure fairness.
  • 35. XAI: Bias Detection and Correction: XAI helps in detecting bias by showing how different factors contribute to the AI’s decisions. detailed breakdown  easier to spot and rectify biased influences. Ex: “years of continuous service,” might unfairly disadvantage women who take career ~~~breaks for caregiving
  • 36. XAI: Enhanced Trust Employees and job candidates are more likely to trust AI-driven hiring tools if they know the basis on which these tools make their recommendations.
  • 37. The Future of Labor
  • 38. It is very difficult to Predict – Especially the Future (Mark Twain)
  • 40. Utopia AI leads to happier and more satisfied workers by automating repetitive tasks. realignment of work-life balance The 40-hour week becoming unnecessary workers and companies embrace flexibility and efficiency.
  • 41. Utopia demand for new skills create novel career opportunities blurring the distinction between white-collar and blue-collar jobs.
  • 42. Utopia reduced physical labour demands, providing upskilling opportunities for everyone, Create economic opportunities across the board.
  • 43. Dystopia where the future of work is reshaped by AI in negative ways, fundamentally altering the employment landscape, worker rights, and social structures.
  • 46. Wild Capitalism tech giants + those with control over AI infrastructures gain disproportionately => stark inequalities Google, OpenAI, Microsoft, Amazon
  • 47. Increased Job precarity Gig economy Lean enterprises with freelance workforce
  • 48. Casual Labor Are we building new employment and labor market models future ready for a digital era or going backwards to casual labor albeit supported by digital technology?
  • 49. Erosion of Workers’ Rights and Power The shift towards more automated systems and reliance on AI could undermine the bargaining power of workers and trade unions.
  • 50.
  • 51. Taylorism is alive and kicking in the digital age!
  • 52. Educational and Skills Mismatch As demand for new skills to work alongside or develop AI => mismatch between existing skills and economy needs
  • 53. Need novel approaches to education Universities reaching their Netflix moment? many workers, especially older or declining industries (coal), struggle to find employment without substantial retraining. Education systems will almost certainly lag behind in adapting to new demands
  • 54. Social and Psychological Impacts Increased surveillance and loss of autonomy  Stress, anxiety + alienation
  • 55. Social and Psychological Impacts Loss of work = Loss of identity and community  Declining mental health
  • 56. My take: AI is neither good or bad
  • 57. My Take There are only good and bad people.
  • 58. As a society, do we have the leadership to ensure that AI redefines work in positive ways creating “greater freedom and fulfillment?”