Knowledge Vault 2/83 - ICLR 2014-2023
John Amuasi ICLR 2022 - Invited Talk - Representation Learning in the Global South: Societal Considerations-Fairness, Safety and Privacy
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Concept Graph & Resume using Claude 3 Opus | Chat GPT4 | Gemini Adv | Llama 3:

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1.-Dr. John Amuasi, a senior lecturer at KNUST, Ghana, gave a keynote speech on representation learning in the Global South.

2.-Representation learning allows systems to discover representations required for feature detection or classification from raw, multidimensional data.

3.-It reduces high-dimensional data into the most important elements, finds patterns and anomalies, and arrives at conclusions.

4.-Representation learning has applications in agriculture, nature conservation, healthcare, transportation, and other areas in low and middle-income countries.

5.-The AI revolution in Africa is fueled by expanding internet access, with some areas having better 4G/5G than parts of Europe.

6.-Africa is being left behind in internet access and AI/representation learning, which is a big mistake given its data richness.

7.-Africa has the world's most genetically diverse population, diverse cultures, and a youthful population, providing valuable data for representation learning.

8.-Equal chances, fair representation, safety, privacy, and equitable sharing of advantages from representation learning in Africa need to be ensured.

9.-The private sector mostly handles representation learning capacity in Africa due to limited government funding, raising data ownership and privacy concerns.

10.-Darker-skinned females were the most misclassified group by commercial gender classification systems, highlighting gender and race inequalities in representation learning.

11.-Algorithmic bias exists as major commercial algorithms are developed outside low-resource settings, missing critical heterogeneous data from these regions.

12.-Researchers from low and middle-income countries face obstacles in participating in international conferences to share their skills and knowledge.

13.-Examples of representation learning bias include higher false positives for black offenders and hiring algorithms biased against women.

14.-Privacy concerns arise from the widespread use of security cameras and access to data from individuals' devices in low and middle-income countries.

15.-Speech imitation technology could have significant implications for security and court evidence in low and middle-income countries.

16.-To fix representation learning bias, diverse datasets, diverse teams, gender sensitivity, and support for AI training in LMICs are needed.

17.-Representation learning should be used positively to increase acceptability of global goods in thinking and doing.

18.-Stakeholder engagement is crucial to understand and address underrepresentation of certain groups in representation learning.

19.-Awareness and accountability within the AI community and among private owners of software and data are essential.

20.-Potential applications of representation learning in LMICs include education, health, disease identification, risk detection, and treatment choice.

21.-National ID cards linked to social security, financial services, and transactions could provide data for improving representation learning, with necessary safety mechanisms.

22.-The link between antimicrobial use in animals and humans is poorly understood, and representation learning could help clarify this in LMICs.

23.-Broad stakeholder engagement across LMICs and focusing on developing representation learning for good, especially in health and education, are important.

24.-Drawing on young, diverse talent across Africa and ensuring gender balance and inclusivity in representation learning are crucial.

25.-Dr. Amuasi appreciated his colleagues, Dr. Paulina Buidi-Guamensa and Dr. Joseph Bonney, for their help in preparing the presentation.

26.-The keynote provided valuable perspectives on the challenges of AI in LMICs, including private-public sector interaction, data, and bias.

27.-Dr. Amuasi was unable to attend the Q&A due to travel but was asked to respond to questions in the chat later.

28.-The AI revolution in Africa is fueled by expanding internet access, with some areas having better 4G/5G than parts of Europe.

29.-Representation learning has the potential to be a game-changer in disease identification, risk detection, and treatment choice in LMICs.

30.-Ensuring fair representation, safety, privacy, and equitable sharing of advantages from representation learning in Africa is crucial for its success.

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