Artificial Intelligence is now smarter than ever, showing human-like abilities at complex tasks such as images classification or natural language processing. But despite its recent advances, it's still not a silver bullet. This talk will present a few challenges in the research and development of artificial intelligence that slow down its progress and adoption. In particular, problems around fairness, the training of models and how to share them will be introduced as well as possible Free Software solutions. Presentation by Vincent Lequertier, PhD Student, Lyon UNiverversity.

1. Vincent Lequertier · FSFE Volunteer · https://vl8r.eu
18 June 2020 · OW2online’20
Upcoming Challenges in Artificial Intelligence
Research and Development
Fairness, Parameters Reuse and Model Training

2. Artificial Intelligence
fairness


3. 3/18
Vocabulary
observed
True False
predicted
True TP FP
False FN TN
● True Positive (TP)
● True Negative (TN)
● False Positive (FP)
● False Negative (FN)

4. 4/18
COMPAS recidivism scoring
All defendants Black defendants White defendants
Low High Low High Low High
Survived 2681 1282 Survived 990 805 Survived 1139 349
Recidivated 1216 2035 Recidivated 532 1369 Recidivated 461 505
FP rate 32.35 FP rate 44.85 FP rate 23.45
FN rate 37.40 FN rate 27.99 FN rate 47.72
propublica.org (2016)

5. 5/18
Racial bias in healthcare
Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019). Dissecting racial bias in an algorithm used to manage the health of populations.

6. 6/18
Why an algorithm can be unfair?
● Bias in the input data itself
● Training with the wrong evaluation metric (bias by proxy)
● Bad prediction model

7. 7/18
A fair loss function
Let be the number of values of a protected attribute
Let be a fairness function

8. Parameters reuse


9. 9/18
What are parameters?

10. 10/18
Why parameters reuse?
Time saving
xkcd-excuse.com

11. 11/18
NLP Model kWh·PUE CO2
e (lbs)
Transformer 201 192
ELMo 275 262
BERT 1507 1438
NAS 656,347 626,155
Why parameters reuse?
Strubell, Emma, Ananya Ganesh, and Andrew McCallum. “Energy and Policy Considerations for Deep Learning in NLP,” 3645–50. Association for Computational Linguistics, 2019
Energy saving

12. 12/18
Why parameters reuse?
Knowledge sharing

13. 13/18
What is needed?
● Make it easy to reuse parameters
● Find ways to reuse parameters in more domains of AI

14. Model training
optimization


15. 15/18
What is hyperparameters tuning?
Heat oven to 180°C
Bake 20 minutes
Cool 10 minutes

16. 16/18
Hyperparameters and model selection

17. 17/18
Solutions
● Early stopping
● Converge towards the best solution
● Make the model training faster

18. Vincent Lequertier · FSFE Volunteer · https://vl8r.eu
18 June 2020 · OW2online’20
Thank you! Questions?