IEIE SPC - IEIE Transactions on Smart Processing & Computing

1

ISO/IEC TR 24028:2020, ``Information technology -- Artificial intelligence -- Overview of trustworthiness in artificial intelligence,'' 2020.

2

ISO/IEC TR 24027:2021, ``Information technology -- Artificial intelligence (AI) -- Bias in AI systems and AI aided decision making,'' 2021.

3

ISO/IEC 23894:2023, ``Information technology -- Artificial intelligence -- Guidance on risk management,'' 2023.

4

OECD, ``Artificial Intelligence in Society,'' June 2019.

5

UNESCO, ``Recommendation on the Ethics of Artificial Intelligence,'' November 2021.

6

National Institute of Standards and Technology, ``Artificial Intelligence Risk Management Framework (AI RMF 1.0),'' January 2023.

7

D. Leslie, ``Understanding artificial intelligence ethics and safety: A guide for the responsible design and implementation of AI systems in the public sector,'' Zenodo, June 2019.

8

European Commission, Directorate-General for Communications Networks, Content and Technology, ``Ethics guidelines for trustworthy AI,'' Publications Office, November 2019.

9

Australia, ``Australia's AI Ethics Principles,'' https://www.industry.gov.au/publications/australias-artificial-intelligence-ethics-framework/australias-ai-ethics-principles

10

Ministry of Science and ICT, ``Strategy to realize trustworthy artificial intelligence,'' May. 2021, https://www.msit.go.kr/eng/bbs/view.do?sCode=eng&mId=4&mPid=2&pageIndex=&bbsSeqNo=42&nttSeqNo=509&searchOpt=ALL&searchTxt

11

Google, ``Objectives for AI applications,'' https://ai.google/responsibility/principles

12

IBM, ``Our fundamental properties for trustworthy AI,'' https://www.ibm.com/artificial-intelligence/ai-ethics-focus-areas

13

Microsoft, ``Microsoft responsible AI principles,'' https://www.microsoft.com/en-us/ai/our-approach?activetab=pivot1%3aprimaryr5

14

J. Pesenti, ``Facebook's five pillars of Responsible AI,'' June 2021, https://ai.facebook.com/blog/facebooks-five-pillars-of-responsible-ai/

15

Naver, ``AI Ethics Principles,'' https://www.navercorp.com/value/aiCodeEthics

16

Kakao, ``AI Ethics,'' https://www.kakaocorp.com/page/responsible/detail/algorithm

17

N. Mehrabi, F. Morstatter, N. Saxena, K. Lerman, and A. Galdyan, ``A survey on bias and fairness in machine learning,'' ACM Computing Surveys (CSUR), vol. 54, no. 6, pp. 1-35, 2021.

18

M. Benjamin, P. Gagnon, N Rostamzadeh, C. Pan, Y. Bengio, and A. Shee, ``Towards standardization of data licenses: The montreal data license,'' arXiv preprint arXiv:1903.12262, 2019.

19

T. Gebru, J. Morgenstern, B. Vecchione, J. W. Vaughan, H. Wallach, H. D. Ill, and K. Crawford, ``Datasheets for datasets,'' Communications of the ACM, vol. 64, no. 12, pp. 86-92, 2021

20

S. Holland, A. Hosny, S. Newman, J. Joseph, and K. Chmielinski, ``The dataset nutrition label,'' Data Protection and Privacy, vol. 12, 2020.

21

R. A. Kievit, W. E. Frankenhuis, L. J. Waldorp, and D. Borsboom, ``Simpson's paradox in psychological science: a practical guide,'' Frontiers in Psychology, vol. 4, 513, 2013.

22

N. Alipourfard, P. G. Fennell, and K. Lerman, ``Can you trust the trend?'' Proc. of the 11th ACM International Conference on Web Search and Data Mining, pp. 19-27, February 2018.

23

N. Alipourfard, P. G. Fennell, and K. Lerman, ``Using Simpson's paradox to discover interesting patterns in behavioral data,'' Proc. of 12th International AAAI Conference on Web and Social Media, June 2018.

24

L. Zhang, Y. Wu, and X. Wu, ``Achieving non-discrimination in data release,'' Proc. of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1335-1344, August 2017.

25

L. Zhang, Y. Wu, and X. Wu, ``Causal modeling-based discrimination discovery and removal: Criteria, bounds, and algorithms,'' IEEE Transactions on Knowledge and Data Engineering, vol. 31, no. 11, pp. 2035-2050, 2018.

26

S. Hajian and J. Domingo-Ferrer, ``A methodology for direct and indirect discrimination prevention in data mining,'' IEEE transactions on Knowledge and Data Engineering, vol. 25, no. 7, pp. 1445-1459, July 2013.

27

F. Kamiran and T. Calders, ``Classifying without discriminating,'' Proc. of 2009 2nd International Conference on Computer, Control and Communication, pp. 1-6, February 2009.

28

F. Kamiran and T. Calders, ``Classification with no discrimination by preferential sampling,'' Proc. of 19th Machine Learning Conference in Belgium and The Netherlands, vol. 1, no. 6, May 2010.

29

F. Kamiran and T. Calders, ``Data preprocessing techniques for classification without discrimination,'' Knowledge and Information Systems, vol. 33, no. 1, pp. 1-33, 2012.

30

M. Feldman, S. A. Friedler, J. Moeller, C. Scheidegger, and S. Venkatasubramanian, ``Certifying and removing disparate impact,'' Proc. of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 259-268, August 2015.

31

S. Radovanovi´c, G. Savi´c, B. Deliba˘si´c, and M. Suknovi´c, ``FairDEA-removing disparate impact from efficiency scores,'' European Journal of Operational Research, vol. 301, no. 3, pp. 1088-1098, 2022.

32

R. Zemel, Y. Wu, K. Swersky, T. Pitassi, and C. Dwork, ``Learning fair representations,'' Proc. of International Conference on Machine Learning, PMLR, pp. 325-333, 2013.

33

F. P. Calmon, D. Wei, B. Vinzamuri, K. N. Ramamurthy, and K. R. Varshney, ``Optimized pre-processing for discrimination prevention,'' Proc. of Conference on Neural Information Processing Systems, 2017.

34

F. Kamiran and T. Calders, ``Data preprocessing techniques for classification without discrimination,'' Knowledge and Information Systems, 2012.

35

https://github.com/fairlearn/fairlearn/blob/main/fairlearn/preprocessing/_correlation_remover.py

36

A. K. Menon and R. C. Williamson, ``The cost of fairness in binary classification,'' Proc. of Conference on Fairness, Accountability and Transparency, PMLR, pp. 107-118, January 2018.

37

T. Kamishima, S. Akaho, H. Asoh, and J. Sakuma, ``Fairness-aware classifier with prejudice remover regularizer,'' Machine Learning and Knowledge Discovery in Databases, Springer, Berlin, Heidelberg, pp. 35-50, 2012.

38

L. Oneto, M. Doninini, A. Elders, and M. Pontil, ``Taking advantage of multitask learning for fair classification,'' Proc. of Joint European Conference on Machine Learning and Knowledge Discovery in Databases, Springer, Berlin, Heidelberg, pp. 35-50, January 2019.

39

S. Jung, T. Park, S. Chun, and T. Moon, ``Re-weighting based group fairness regularization via classwise robust optimization,'' arXiv preprint arXiv:2303.00442, 2023.

40

E. Krasanakis, E. Spyromitros-Xioufis, S. Papadopoulos, and Y. Kompatsiaris, ``Adaptive sensitive reweighting to mitigate bias in fairness-aware classification,'' Proc. of the 2018 World Wide Web Conference, pp. 853-862, April 2018.

41

T. Calders and S. Verwer, ``Three naive Bayes approaches for discrimination-free classification,'' Data Mining and Knowledge Discovery, vol. 21, no. 2, pp. 277-292, July 2010.

42

C. Dwork, N. Cynthia, A. T. Kalai, and M. Leiserson, ``Decoupled classifiers for group-fair and efficient machine learning,'' Proc. of Conference on Fairness, Accountability and Transparency, PMLR, pp. 119-133, January 2018.

43

R. Jiang, A. Pacchiano, T. Stepleton, H. Jiang, and S. Chiappa, ``Wasserstein fair classification,'' Uncertainty in Artificial Intelligence, PMLR, pp. 862-872, August 2019.

44

F. Kamiran and T. Calders, ``Classification with no discrimination by preferential sampling,'' Proc. of 19th Machine Learning Conference in Belgium and The Netherlands, vol. 1. no. 6, May 2010.

45

N. Mehrabi, U. Gupta, F. Morstatter, G. V. Steeg, and A. Galstyan, ``Attributing fair decisions with attention interventions,'' arXiv preprint arXiv:2109.03952, 2021.

46

https://github.com/Trusted-AI/AIF360/blob/master/aif360/algorithms/inprocessing/art_classifier.py

47

L. E. Celis, L. Huang, V. Keswani, and N. K. Vishnoi, ``Classification with fairness constraints: A meta-algorithm with provable guarantees,'' Proc. of the Conference on Fairness, Accountability, and Transparency, pp. 319-328, January 2019.

48

B. H. Zhang, B. Lemoine, and M. Mitchell, ``Mitigating unwanted biases with adversarial learning,'' Proc. of of the 2018 AAAI/ACM Conference on AI, Ethics, and Society, New Orleans, USA, February 2018.

49

M. Kearns, S. Neel, A. Roth, and Z. S. Wu, ``Preventing fairness gerrymandering: Auditing and learning for subgroup fairness,'' Proc. of International Conference on Machine Learning, PMLR, pp. 2564-2572, July 218.

50

A. Agarwal, A. Beygelzimer, M. Dudik, J. Langford, and H. Wallach, ``A reductions approach to fair classification,'' Proc. of International Conference on Machine Learning, 2018.

51

N. Mehrabi, F. Morstatter, N. Peng, and A. Galstyan, ``Debiasing community detection,'' Proc. of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ACM, August 2019.

52

A. J. Bose and W. L. Hamilton, ``Compositional fairness constraints for graph embeddings,'' Proc. of International Conference on Machine Learning, PMLR, pp. 715-724, May. 2019.

53

A. Backurs, P. Indyk, K. Onak, B. Schieber, A. Vakilian, and T. Wagner, ``Scalable fair clustering,'' Proc. of International Conference on Machine Learning, PMLR, pp. 405-413, May 2019.

54

X. Chen, B. Fain, L. Lyu, and K. Munagala, ``Proportionally fair clustering,'' Proc. of International Conference on Machine Learning, PMLR, pp. 1032-1041, May 2019.

55

R. Berk, H. Heidari, S. Jabbari, M. Joseph, M. Kearns, J. Morhenstern, S. Neel, and A. Roth, ``A convex framework for fair regression,'' arXiv preprint arXiv:1706.02409, 2017.

56

A. Agarwal, M. Dudík, and Z. S. Wu, ``Fair regression: Quantitative definitions and reduction-based algorithms,'' Proc. of International Conference on Machine Learning, PMLR, pp. 120-129, May 2019.

57

S. Aghaei, M. J. Azizi, and P. Vayanos, ``Learning optimal and fair decision trees for non-discriminative decision-making,'' Proc. of the AAAI Conference on Artificial Intelligence, Association for the Advancement of Artificial Intelligence (AAAI), vol. 33, pp. 1418-1426, July 2019.

58

H. Zhao, ``Costs and benefits of fair regression,'' arXiv preprint arXiv:2106.08812, 2021.

59

E. Chzhen, C. Denis, M. Hebiri, L. Oneto, and M. Pontil, ``Fair regression via plug-in estimator and recalibration with statistical guarantees,'' Advances in Neural Information Processing Systems, vol. 33, pp. 19137-19148, 2020.

60

E. Chzhen, C. Denis, M. Hebiri, L. Oneto, and M. Pontil, ``Fair regression with wasserstein barycenters,'' Advances in Neural Information Processing Systems, vol. 33, pp. 7321-7331, 2020.

61

S. Samadi, U. Tantipongpipat, J. Morgenstern, M. Singh, and S. Vempala, ``The price of fair PCA: One extra dimension,'' Advances in Neural Information Processing Systems, vol. 31, 2018.

62

J. Lee, G. Kim, M. Olfat, M. Hasegawa-Johnson, and C. D. Yoo, ``Fast and efficient MMD-based fair PCA via optimization over Stiefel manifold,'' Proc. of the AAAI Conference on Artificial Intelligence, vol. 36, no. 7, pp. 7363-7371, June 2022.

63

T. Kamishima, S. Akaho, H. Asoh, and J. Sakuma, ``Fairness-aware classifier with prejudice remover regularizer,'' Machine Learning and Knowledge Discovery in Databases, pp. 35-50, 2012.

64

D. Kenna, ``Using adversarial debiasing to remove bias from word embeddings,'' arXiv preprint arXiv:2107.10251, 2021.

65

S. Chiappa and W. S. Isaac, ``A causal Bayesian networks viewpoint on fairness,'' Privacy and Identity Management: Fairness, Accountability, and Transparency in the Age of Big Data, Springer International Publishing, pp. 3-20, 2019.

66

J. R. Loftus, C. Russell, M. J. Kusner, and R. Silva, ``Causal reasoning for algorithmic fairness,'' arXiv preprint arXiv:1805.05859, 2018.

67

L. Zhang, Y. Wu, and X. Wu, ``A causal framework for discovering and removing direct and indirect discrimination,'' arXiv preprint arXiv:1611.07509, 2016.

68

C. Louizos, K. Swersky, Y. Li, M. Welling, and R. Zemel, ``The variational fair autoencoder,'' arXiv preprint arXiv:1511.00830, 2015.

69

D. Moyer, S. Gao, R. Brekelmans, G. V. Steeg, and A. Galstyan, ``Invariant representations without adversarial training,'' Advances in Neural Information Processing Systems, vol. 31, 2018.

70

A. Amini, A. P. Soleimany, W. Schwarting, S. N. Bhatia, and D. Rus, ``Uncovering and mitigating algorithmic bias through learned latent structure,'' Proc. of the 2019 AAAI/ACM Conference on AI, Ethics, and Society, ACM, January 2019.

71

B. H. Zhang, B. Lemoine, and M. Mitchell, ``Mitigating unwanted biases with adversarial learning,'' Proc. of the 2018 AAAI/ACM Conference on AI, Ethics, and Society, ACM, December 2018.

72

D. Xu, S. Yuan, L. Zhang, and X. Wu, ``FairGAN: Fairness-aware generative adversarial networks,'' Proc. of 2018 IEEE International Conference on Big Data (Big Data), IEEE, December 2018.

73

T. Bolukbasi, K.-W. Chang, J. Zou, V. Saligrama, and A. Kalai, ``Man is to computer programmer as woman is to homemaker? Debiasing word embeddings,'' Advances in Neural Information Processing Systems, vol. 29, 2016.

74

J. Zhao, T. Wang, M. Yatskar, R. Cotterell, V. Ordonez, and K.-W. Chang, ``Gender bias in contextualized word embeddings,'' arXiv preprint arXiv:1904.03310, 2019.

75

J. Chen, Y. Wang, and T. Lan, ``Bringing fairness to actor-critic reinforcement learning for network utility optimization,'' Proc. of IEEE INFOCOM 2021 - IEEE Conference on Computer Communications, IEEE, May. 2021.

76

M. Hardt, E. Price, and N. Srebro, ``Equality of opportunity in supervised learning,'' Proc. of Conference on Neural Information Processing Systems, 2016.

77

G. Pleiss, M. Raghavan, F. Wu, J. Kleinberg, and K. Q. Weinberger, ``On fairness and calibration,'' Proc. of Conference on Neural Information Processing Systems, 2017

78

F. Kamiran, A. Karim, and X. Zhang, ``Decision theory for discrimination-aware classification,'' Proc. of IEEE International Conference on Data Mining, 2012.

79

A. Agarwal, A. Beygelzimer, M. Dudík, J. Langford, and H. M. Wallach, ``A reductions approach to fair classification,'' Proc. of Machine Learning Research, PMLR, vol. 80, pp. 60-69, 2018.

80

A. Agarwal, M. Dudík, and Z. S. Wu,, ``Fair regression: Quantitative definitions and reduction-based algorithms,'' Proc. of Machine Learning Research, PMLR, vol. 97, pp. 120-129, 2019.

81

IBM Trusted-AI, ``AI Fairness 360 (AIF360),'' https://github.com/Trusted-AI/AIF360

82

R. K. E. BellamyK. Dey, M. Hind, et al., ``AI fairness 360: An extensible toolkit for detecting, understanding, and mitigating unwanted algorithmic bias,'' arXiv preprint arXiv.1810.01943, 2018.

83

S. Bird, M. Dudík, R. Edgar, B. Horn, R. Luts, V. Milan, M. Sameki, H. Wallach, and K. Walker, ``Fairlearn: A toolkit for assessing and improving fairness in AI,'' Microsoft, Tech. Rep. MSR-TR-2020-32, May 2020.

84

Microsoft, ``Fairlearn,'' https://github.com/fairlearn/fairlearn

85

https://fairlearn.org/

86

Google Tensorflow, ``Fairness Indicators,'' https://github.com/tensorflow/fairness-indicators

87

http://www.aitimes.com/news/articleView.html?idxno=143283

88

P. Saleiro, B. Kuester, A. Stevens, A. Anisfeld, L. Hinkson, J. London, and R. Ghani, ``Aequitas: A bias and fairness audit toolkit,'' arXiv preprint arXiv:1811.05577, 2018.

89

Data Science for Social Good, ``Aequitas,'' https://github.com/dssg/aequitas

90

S. Verma and J. Rubin, ``Fairness definitions explained,'' Proc. of the International Workshop on Software Fairness, 2018.

91

S. Barocas, M. Hardt, and A. Narayanan, Fairness and Machine Learning: Limitations and Opportunities, MIT Press, 2023.