Papers for Ghosh FTML'22

	A	B	C	D	E	F	G	H	I	J
1			type	URL	Year	Venue	notes
2	Note: General References/tutorials are linked to via Canvas.
3	This is an evolving list of candidate papers for presentation and discussion
4	Note: You may also pick papers from FACCT 2022 or AIES 2022 conferences.
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6	Explainability Papers
7	1	Lundberg	feature attribution	From Local Explanations to Global Understanding with Explainable AI for Trees	2020		https://github.com/slundberg/shap	How can we fool LIME and SHAP? Adversarial Attacks on Post hoc Explanation Methods
8	2		interpretable models	Distill-and-Compare: Auditing Black-Box Models Using Transparent Model Distillation	2018	AAAI	https://github.com/shftan/auditblackbox	Does knowledge distillation really work?
9	3		interpretable models	How interpretable and trustworthy are gams?	2021	KDD	InterpretableML package: Interpretml: A unified framework for machine learning interpretability	https://github.com/interpretml/interpret
10	4	Ustin and Rudin	interpretable models	Learning Optimized Risk Scores	2019	JMLR
11	5	Rudin	interpretable models	Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead	2019	NMI
12	6		Counterfactuals	Explanations based on the Missing: Towards Contrastive Explanations with Pertinent Negatives	2018	NIPS	https://github.com/IBM/Contrastive-Explanation-Method
13	7	Stepin, et al	Counterfactuals	A survey of contrastive and counterfactual explanation generation methods for explainable artificial intelligence	2021	IEEE Access
14	8		Counterfactuals	Factual and counterfactual explanations for black box decision making	2019	IEEE Intelligent Systems
15	9	Byrne	Counterfactuals	Counterfactuals in Explainable Artificial Intelligence (XAI): Evidence from Human Reasoning	2019	ICJAI
16	9.5	JPMC	regression	Counterfactual Explanations for Arbitrary Regression Models	2021
17	10		exemplar based	Understanding Black-Box Predictions via Influence Functions	2017	ICML	https://github.com/kohpangwei/influence-release
18	11		exemplar based	Interpreting black box predictions using fisher kernels	2019	AIStats
19	12	Mukund Sundararajan, Ankur Taly, Qiqi Yan	neural network oriented	Axiomatic attribution for deep networks	2017	ICML		Using a deep learning algorithm and integrated gradients explanation to assist grading for diabetic retinopathy
20	13		neural network oriented	Explaining Deep Neural Networks and Beyond: A Review of Methods and Applications	2021	Proc. IEEE	huge LRP hub at http://www.heatmapping.org/
21	14	Ismail et al	neural network oriented	Interpretable Mixture of Experts for Structured Data	2022
22	15	Chefer et al	neural network oriented	Transformer interpretability beyond attention visualization	2021	CVPR
23	16	Karimi	causal	Algorithmic recourse under imperfect causal knowledge: a probabilistic approach	2020	NeurIPS
24	17	Galhotra et al	Causal	Explaining black-box algorithms using probabilistic contrastive counterfactuals	2021	SIGMOD
25	18	Koh et al	HAI	Concept bottleneck models	2020	ICML	https://github.com/yewsiang/ConceptBottleneck
26	19	QV Liao and K. Varshney	HAI	Human-Centered Explainable AI (XAI): From Algorithms to User Experiences	2022
27	20	Lakkaraju et al	HAI	Rethinking Explainability as a Dialogue: A Practitioner's Perspective	2022
28	21	Bansal et al.	HCI + XAI	Does the Whole Exceed its Parts? The Effect of AI Explanations on Complementary Team Performance	2021	CHI
29	22	Bucinca et al.	HCI + XAI	To Trust or to Think: Cognitive Forcing Functions Can Reduce Overreliance on AI in AI-assisted Decision-making	2021	CSCW
30	23	Tiddi	Knowledge graphs	Knowledge Graphs as Tools for Explainable Machine Learning: A Survey.	2022	AI Jl
31	24	Slack et al	quality/reliability	Reliable Post hoc Explanations: Modeling Uncertainty in Explainability.	2021	NeurIPS
32	25	Rojat et al	time series	Explainable artificial intelligence (xai) on timeseries data: A survey	2021
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34	Fairness-Papers
35	1		history	50 Years of Test (Un)fairness: Lessons for Machine Learning	2019	FAT'19
36	2		post-processing	Equality of Opportunity in Supervised Learning	2016	NIPS	https://github.com/gpleiss/equalized_odds_and_calibration
37	3		post-processing	On Fairness and Calibration	2017	NIPS	https://github.com/gpleiss/equalized_odds_and_calibration
38	4		pre-processing	Optimized Pre-Processing for Discrimination Prevention	2017	NIPS	https://github.com/fair-preprocessing/nips2017
39	5	Wang, Utsun	pre-processing	Repairing without Retraining: Avoiding Disparate Impact with Counterfactual Distributions	2019	icml
40	6		bias detection	Fast Threshold Tests for Detecting Discrimination.	2018	AISTATS	https://github.com/5harad/fasttt	slides at https://samcorbettdavies.files.wordpress.com/2017/11/making-fair-decisions-with-algorithms.pdf	goes with A Bayesian Model of Cash Bail Decisions	Facct 21
41	7	Celis et al	theory	Fair classification with noisy protected attributes: A framework with provable guarantees	2021	ICML	github.com/vijaykeswani/NoisyFair-Classification.
42	8	Quy et al	data issues	A survey on datasets for fairness‐aware machine learning	2022	WIREs	goes with Retiring Adult: New datasets for fair machine learning, Nips21
43	9		in-processing	Mitigating Unwanted Biases with Adversarial Learning	2018	AAAI	https://www.ibm.com/blogs/research/2018/09/ai-fairness-360/
44	10		in-processing	Classification with Fairness Constraints: A Meta-Algorithm with Provable Guarantees	2019	ACM
45	11	Black et al	individual fairness	Fliptest: fairness testing via optimal transport
46	12	Speichter (Gummadi's group)	individual fairness	A unified approach to quantifying algorithmic unfairness: Measuring individual &group unfairness via inequality indices	2018	KDD
47	13		fairness - HCI	Disparate Interactions: An Algorithm-in-the-Loop Analysis of Fairness in Risk Assessments	2019	FAT'19
48	14	Min Lee	HCI	Procedural justice in algorithmic fairness: Leveraging transparency and outcome control for fair algorithmic mediation	2019	CSCW
49	15	Lee & Singh	HCI	The landscape and gaps in open source fairness toolkits	2021	CHI
50	16	Wang & Joachims	ranking	User Fairness, Item Fairness, and Diversity for Rankings in Two-Sided Markets	2021	SIGIR
51	17	(Kenthapadi)	ranking	Fairness-aware ranking in search & recommendation systems with application to linkedin talent search	2019	KDD
52	18	Singh, David Kempe, Thorsten Joachims	ranking	Fairness in ranking under uncertainty	2021	NIPS
53	19	Ke Yang	ranking, causality	Causal intersectionality for fair ranking
54	20	Gopalan et al	Fairness of XAI	Bias: Measuring the Fairness of Explanations
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56	Drift/robustness: Papers
57	0	pick any paper from "required" sections of https://github.com/acmi-lab/cmu-10732-robustness-adaptivity-shift/blob/main/Schedule.md except "mixture proportion" paper.
58	1	Stephan Rabanser, Stephan Günnemann, Zachary C Lipton	drift -detection	Failing loudly: An empirical study of methods for detecting dataset shift	2018	NeurIPS
59	2	Reis et al	drift -detection	Fast Unsupervised Online Drift Detection Using Incremental Kolmogorov-Smirnov Test	2016	KDD
60	3	Lipton, Wang, Smola	drift	Detecting and correcting for label shift with black box predictors	2018	ICML
61	4	Jingkang Yang	drift	Generalized Out-of-Distribution Detection	2021
62	5	Rawal et al	drift	Algorithmic recourse in the wild: Understanding the impact of data and model shifts	2020
63	6	Koh	drift benchmarking	WILDS: A Benchmark of in-the-Wild Distribution Shifts	2021	ICML
64	7	Rezaei et al	drift + fairness	Robust Fairness Under Covariate Shift	2021	AAAI
65	8	H. Singh et al	drift + fairness	Fairness violations and mitigation under covariate shift	2021	FAccT
66	9	M. Abdar et al	uncertainty	A review of uncertainty quantification in deep learning: Techniques, applications and challenges	2021	Info Fusion
67	10	Bhatt et al.	Uncertainty-XAI	Uncertainty as a Form of Transparency: Measuring, Communicating, and Using Uncertainty	2021	AIES
68	11	Ley et al.	Uncertainty-XAI	Diverse, Global and Amortised Counterfactual Explanations for Uncertainty Estimates	2022	AAAI
69	12	Ilyas et al	adversarial attacks	Adversarial examples are not bugs, they are features	2019	NeurIPS
70	13	F Tramer, N Carlini, W Brendel, A Madry	adversarial attacks	On adaptive attacks to adversarial example defenses	2020	NeurIPS
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