Concept Graph & Resume using Claude 3 Opus | Chat GPT4 | Gemini Adv | Llama 3:

Resume:

1.-Cynthia Dwork is a renowned computer scientist who uses theoretical computer science to address societal problems.

2.-Algorithms can be unfair due to biased training data, historical bias in labels, and differentially expressive features.

3.-Algorithmic unfairness has significant real-world consequences, such as in child protection services and recidivism prediction.

4.-Group fairness definitions, while popular, often fail under scrutiny compared to individual fairness.

5.-Ilvento's work approximates a similarity metric for individual fairness using human knowledge and learning theory.

6.-Multi-accuracy achieves group fairness simultaneously for intersectional groups defined by a large collection of sets.

7.-Scoring functions produce probabilities, but the meaning is unclear for non-repeatable events like tumor metastasis.

8.-Calibration in forecasting requires predicted probabilities to match observed frequencies for each predicted value.

9.-Multi-accuracy retains expectations for predefined sets; solutions vary without training data or additional constraints.

10.-Complexity theory suggests considering all efficiently computable sets to capture historically disadvantaged groups.

11.-Multi-accuracy and multi-calibration together aim to capture all task-specific, semantically significant differences.

12.-Data collected is often differentially expressive for advantaged vs. disadvantaged groups.

13.-Ranking underlies many applications like triage, admissions, and affirmative action strategies.

14.-Fair ranking should prevent obviously unfair outcomes, e.g., all of one group ranked above another.

15.-Multi-accuracy prevents certain unfair rankings; multi-calibration is even stronger.

16.-Focus should be on what data is collected and measured, as unfairness often lies there.

17.-Computationally indistinguishable positive and negative examples suggest assigning base rate probabilities.

18.-Rich multi-calibration may justify treating predictions as pseudo-random "truth" with respect to the defining sets.

19.-Fair representation learning aims to hide sensitive attributes while enabling standard training.

20.-Adversarial approaches censor representations to achieve group fairness notions like statistical parity.

21.-Learned censored representations can enable transfer learning to other prediction tasks.

22.-Censoring techniques may identify commonalities across populations for out-of-distribution generalization.

23.-Synthetic data experiments show promise for learning common predictive signal across populations.

24.-Fair algorithms alone cannot fully address societal unfairness.

25.-Breakthroughs in metric learning enable individual fairness.

26.-Multi-calibration emerged as significant for fair scoring, ranking, and understanding individual probabilities.

27.-Representation and data collection are critical factors in algorithmic fairness.

28.-Censored representations offer a path to generalizing across populations.

29.-Achieving truly "superhuman" fairness remains an open challenge.

30.-Much work remains to deeply understand fairness and develop principled, broadly applicable solutions.

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