Presentation Title: AI & Labor Markets Presenter: Joaquim Jorge Description: Explore the transformative impact of Artificial Intelligence (AI) on labor markets in this comprehensive presentation by Joaquim Jorge. This insightful slideset delves into the opportunities and challenges that AI integration brings to various industries, highlighting key AI techniques and their real-world applications. Bias in Hiring and Firing: The presentation critically examines biases in AI systems used for hiring and firing decisions: Hiring Bias: Instances where AI systems, like LinkedIn’s recommendation system and OpenAI's GPT, have shown biases in résumé ranking and job advertisements, including gender bias and cost-efficiency algorithms inadvertently favoring male candidates. Firing Bias: AI's role in monitoring productivity and making termination decisions, with examples from Amazon’s “Time off Task” system and Uber’s driver performance metrics, highlighting unfair terminations affecting minority groups. Mitigation Strategies: Bias Audits: Regularly auditing AI systems to identify and mitigate biases. Diverse Training Data: Ensuring training data are diverse and representative of all demographic groups. Human Oversight: Implementing human oversight to review and validate AI decisions. Explainable AI (XAI): Making AI decisions transparent and accountable to detect and correct biases. Future of Labor Markets: The presentation explores potential futures of labor markets with AI, presenting both utopian and dystopian scenarios: Utopian Scenario: AI could lead to increased worker satisfaction by automating repetitive tasks, creating new career opportunities, and reducing physical labor demands, resulting in better work-life balance and economic opportunities. Dystopian Scenario: AI could widen the economic divide, increase job precarity, and erode worker rights. Risks include increased surveillance, loss of autonomy, and the social and psychological impacts of job displacement. Key Takeaways: Understand the role and impact of different AI technologies in various sectors. Recognize and address biases in AI systems, especially in hiring and firing decisions. Explore potential futures of labor markets with AI integration. Learn strategies for ensuring ethical and fair AI applications. This presentation is essential for professionals, researchers, and policymakers interested in the intersection of AI and labor markets, providing a detailed analysis of current trends, challenges, and future possibilities.

1. AI & Labor Markets
Joaquim Jorge

3. AI: toolbox
and
techniques
Supervised
Learning
(classifies things)
Unsupervised
learning
Generative AI
Reinforcement
learning

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
---

8. ChatGPT
GPT: Generative
Pretrained Transformer

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.

12. Bias in Hiring

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.

18. Firing Decisions

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

26. The need (rush?)
for AI regulation

27. Mitigation
Strategies

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)

39. The Luddites

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.

44. Widening the economic divide

45. Dystopia
beneficiaries: technology & capital
owners

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.

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?”