Skip to main content
SpringerLink
Log in

Algorithmic Fairness in Multi-stakeholder Platforms

  • Chapter
  • First Online:
Ethics in Artificial Intelligence: Bias, Fairness and Beyond

Part of the book series: Studies in Computational Intelligence ((SCI,volume 1123))

  • 204 Accesses

Abstract

Major online platforms today are multi-stakeholder in nature and they cater to the interests of various stakeholders. Examples include e-commerce platforms with sellers of goods and services and buyers who pay for them, hotel booking platforms with hosts and guests, media-streaming platforms artists or content creators and viewers, and many more. The focus here is on the information access services (like search and recommendation) deployed on these platforms. While traditionally these services were designed in consumer-centric ways, they are found to be unfair to providers. Since the providers depend on these platforms for their livelihood, fairness for providers is an important and necessary design element in many scenarios. This chapter summarizes the domain and discusses various prior works on provider fairness in such multi-stakeholder platforms after a brief review of major works on algorithmic fairness in machine learning. Many recent works show that provider fairness can cause loss of utility and unfairness for consumers. Following this, a number of works have proposed fairness notions for both providers and consumers in different settings, and studied multi-stakeholder fairness to balance various fairness and utility goals or constraints. This chapter reviews some major works on multi-stakeholder fairness in the following three aspects: the problem setting, fairness notions, and proposed approaches. It also discusses how most of the works on multi-stakeholder fairness have considered settings with only two stakeholders, gives some examples on other platforms with more than two stakeholders, and how they are fundamentally different in terms of the utility structure for the stakeholders.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 139.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 179.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    Abdollahpouri and Burke [55] briefly talk about one of such settings and consider the intermediary stakeholders as side stakeholders.

  2. 2.

    Note that since most of the relevant research works have focused on two-sided platforms, here we limit our discussion to only two-sided platforms while studies on various three-sided platforms (as discussed in Sect. 4.2) can be a significant future research agenda.

References

  1. Rigano C (2019) Using artificial intelligence to address criminal justice needs. Natl Inst Justice J 280:1–10

    Google Scholar 

  2. Mukerjee A, Biswas R, Deb K, Mathur AP (2002) Multi-objective evolutionary algorithms for the risk-return trade-off in bank loan management. Int Trans Oper Res 9(5):583–597

    Article  Google Scholar 

  3. Cohen L, Lipton ZC, Mansour Y (2020) Efficient candidate screening under multiple tests and implications for fairness. In: 1st symposium on foundations of responsible computing

    Google Scholar 

  4. Marcinkowski F, Kieslich K, Starke C, Lünich M (2020) Implications of AI (un-) fairness in higher education admissions: the effects of perceived AI (un-) fairness on exit, voice and organizational reputation. In: Proceedings of the 2020 conference on fairness, accountability, and transparency, pp 122–130

    Google Scholar 

  5. Ntoutsi E, Fafalios P, Gadiraju U, Iosifidis V, Nejdl W, Vidal M-E, Ruggieri S, Turini F, Papadopoulos S, Krasanakis E et al (2020) Bias in data-driven artificial intelligence systems-an introductory survey. Wiley Interdiscip Rev Data Min Knowl Discov 10(3):1356

    Article  Google Scholar 

  6. Angwin J, Larson J, Mattu S, Kirchner L (2016) Machine bias

    Google Scholar 

  7. O’Neil C (2017) Weapons of math destruction: how big data increases inequality and threatens democracy (Crown publishers, New York, 2016). Coll Res Libr 78(3):403–404

    Google Scholar 

  8. Racial discrimination in face recognition technology. https://sitn.hms.harvard.edu/flash/2020/racial-discrimination-in-face-recognition-technology/. Accessed 01 Dec 2022

  9. Are face-detection cameras racist?. https://content.time.com/time/business/article/0,8599,1954643,00.html. Accessed 01 Dec 2022

  10. Lambrecht A, Tucker C (2019) Algorithmic bias? an empirical study of apparent gender-based discrimination in the display of stem career ads. Manag Sci 65(7):2966–2981

    Article  Google Scholar 

  11. Mehrabi N, Morstatter F, Saxena N, Lerman K, Galstyan A (2021) A survey on bias and fairness in machine learning. ACM Comput Surv (CSUR) 54(6):1–35

    Article  Google Scholar 

  12. Finocchiaro J, Maio R, Monachou F, Patro GK, Raghavan M, Stoica A-A, Tsirtsis S (2021) Bridging machine learning and mechanism design towards algorithmic fairness. In: Proceedings of the 2021 ACM conference on fairness, accountability, and transparency, pp 489–503

    Google Scholar 

  13. Corbett-Davies S, Pierson E, Feller A, Goel S, Huq A (2017) Algorithmic decision making and the cost of fairness. In: Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining, pp 797–806

    Google Scholar 

  14. Hardt M, Price E, Srebro N (2016) Equality of opportunity in supervised learning. Adv Neural Inf Process Syst 29

    Google Scholar 

  15. Dwork C, Hardt M, Pitassi T, Reingold O, Zemel R (2012) Fairness through awareness. In: Proceedings of the 3rd innovations in theoretical computer science conference, pp 214–226

    Google Scholar 

  16. Robertson SE (1977) The probability ranking principle in IR. J Doc

    Google Scholar 

  17. Bobadilla J, Ortega F, Hernando A, Gutiérrez A (2013) Recommender systems survey. Knowl Based Syst 46:109–132

    Google Scholar 

  18. Schütze H, Manning CD, Raghavan P (2008) Introduction to information retrieval, vol 39

    Google Scholar 

  19. Hannák A, Wagner C, Garcia D, Mislove A, Strohmaier M, Wilson C (2017) Bias in online freelance marketplaces: Evidence from taskrabbit and fiverr. In: Proceedings of the 2017 ACM conference on computer supported cooperative work and social computing, pp 1914–1933

    Google Scholar 

  20. Bellogín A, Castells P, Cantador I (2017) Statistical biases in information retrieval metrics for recommender systems. Inf Retr J 20(6):606–634

    Article  Google Scholar 

  21. Heckman J (1990) Varieties of selection bias. Am Econ Rev 80(2):313–318

    Google Scholar 

  22. Chen J, Dong H, Wang X, Feng F, Wang M, He X (2020) Bias and debias in recommender system: a survey and future directions. arXiv:2010.03240

  23. Baeza-Yates R (2018) Bias on the web. Commun ACM 61(6):54–61

    Article  Google Scholar 

  24. Abdollahpouri H (2019) Popularity bias in ranking and recommendation. In: Proceedings of the 2019 AAAI/ACM conference on AI, ethics, and society, pp 529–530

    Google Scholar 

  25. Ahanger AB, Aalam SW, Bhat MR, Assad A (2022) Popularity bias in recommender systems-a review. In: International conference on emerging technologies in computer engineering. Springer, pp 431–444

    Google Scholar 

  26. Edelman B, Luca M, Svirsky D (2017) Racial discrimination in the sharing economy: evidence from a field experiment. Am Econ J Appl Econ 9(2):1–22

    Article  Google Scholar 

  27. Salganik MJ, Dodds PS, Watts DJ (2006) Experimental study of inequality and unpredictability in an artificial cultural market. Sci 311(5762):854–856

    Google Scholar 

  28. Graham M, Hjorth I, Lehdonvirta V (2017) Digital labour and development: impacts of global digital labour platforms and the gig economy on worker livelihoods. Transf Eur Rev Labour Res 23(2):135–162

    Google Scholar 

  29. Hesmondhalgh D, Osborne R, Sun H, Barr K (2021) Music creators’ earnings in the digital era. Intellectual property office research paper forthcoming

    Google Scholar 

  30. Biega AJ, Gummadi KP, Weikum G (2018) Equity of attention: amortizing individual fairness in rankings. In: The 41st international ACM SIGIR conference on research & development in information retrieval, pp 405–414

    Google Scholar 

  31. Banerjee A, Patro GK, Dietz LW, Chakraborty A (2020) Analyzing ‘near me’services: Potential for exposure bias in location-based retrieval. In: 2020 IEEE international conference on big data (big data). IEEE, pp 3642–3651

    Google Scholar 

  32. Agarwal A, Zaitsev I, Wang X, Li C, Najork M, Joachims T (2019) Estimating position bias without intrusive interventions. In: Proceedings of the twelfth ACM international conference on web search and data mining, pp 474–482

    Google Scholar 

  33. Patro GK, Porcaro L, Mitchell L, Zhang Q, Zehlike M, Garg N (2022) Fair ranking: a critical review, challenges, and future directions. In: 2022 ACM conference on fairness, accountability, and transparency, FAccT ’22. Association for Computing Machinery, New York, NY, USA, pp 1929–1942

    Google Scholar 

  34. Singh A, Joachims T (2018) Fairness of exposure in rankings. In: Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining, pp 2219–2228

    Google Scholar 

  35. Wang Y, Ma W, Zhang* M, Liu Y, Ma S (2022) A survey on the fairness of recommender systems. ACM Trans Inf Syst

    Google Scholar 

  36. Ekstrand MD, Das A, Burke R, Diaz F (2022) Fairness in recommender systems, pp 679–707

    Google Scholar 

  37. Zehlike M, Yang K, Stoyanovich J (2021) Fairness in ranking: a survey. arXiv:2103.14000

  38. Zehlike M, Bonchi F, Castillo C, Hajian S, Megahed M, Baeza-Yates R (2017) Fa* ir: a fair top-k ranking algorithm. In: Proceedings of the 2017 ACM on conference on information and knowledge management, pp 1569–1578

    Google Scholar 

  39. Celis LE, Straszak D, Vishnoi NK (2018) Ranking with fairness constraints. In: ICALP

    Google Scholar 

  40. Asudeh A, Jagadish H, Stoyanovich J, Das G (2019) Designing fair ranking schemes. In: Proceedings of the 2019 international conference on management of data, pp 1259–1276

    Google Scholar 

  41. Zehlike M, Castillo C (2020) Reducing disparate exposure in ranking: a learning to rank approach. In: Proceedings of the web conference 2020, pp 2849–2855

    Google Scholar 

  42. Liu T-Y et al (2009) Learning to rank for information retrieval. Foundations and Trends® in Information Retrieval 3(3):225–331

    Google Scholar 

  43. Adomavicius G, Tuzhilin A (2005) Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans Knowl Data Eng 17(6):734–749

    Article  Google Scholar 

  44. Voorhees EM et al (1999) The trec-8 question answering track report. In: Trec, vol 99, pp 77–82

    Google Scholar 

  45. Järvelin K, Kekäläinen J (2002) Cumulated gain-based evaluation of IR techniques. ACM Trans Inf Syst (TOIS) 20(4):422–446

    Article  Google Scholar 

  46. Patro GK, Biswas A, Ganguly N, Gummadi KP, Chakraborty A (2020) Fairrec: two-sided fairness for personalized recommendations in two-sided platforms. In: Proceedings of the web conference 2020, pp 1194–1204

    Google Scholar 

  47. Geyik SC, Ambler S, Kenthapadi K (2019) Fairness-aware ranking in search & recommendation systems with application to linkedin talent search. In: Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining, pp 2221–2231

    Google Scholar 

  48. Yao S, Huang B (2017) Beyond parity: fairness objectives for collaborative filtering. Adv Neural Inf Process Syst 30

    Google Scholar 

  49. Rastegarpanah B, Gummadi KP, Crovella M (2019) Fighting fire with fire: using antidote data to improve polarization and fairness of recommender systems. In: Proceedings of the twelfth ACM international conference on web search and data mining, pp 231–239

    Google Scholar 

  50. Patro GK, Chakraborty A, Banerjee A, Ganguly N (2020) Towards safety and sustainability: designing local recommendations for post-pandemic world. In: Fourteenth ACM conference on recommender systems, pp 358–367

    Google Scholar 

  51. Do V, Corbett-Davies S, Atif J, Usunier N (2021) Two-sided fairness in rankings via Lorenz dominance. Adv Neural Inf Process Syst 34:8596–8608

    Google Scholar 

  52. Wu Y, Cao J, Xu G, Tan Y (2021) Tfrom: A two-sided fairness-aware recommendation model for both customers and providers. In: Proceedings of the 44th international ACM SIGIR conference on research and development in information retrieval, pp 1013–1022

    Google Scholar 

  53. Patro GK, Chakraborty A, Ganguly N, Gummadi K (2020) Incremental fairness in two-sided market platforms: on smoothly updating recommendations. In: Proceedings of the AAAI conference on artificial intelligence, vol 34, pp 181–188

    Google Scholar 

  54. Mondal AS, Bal R, Sinha S, Patro GK (2021) Two-sided fairness in non-personalised recommendations (student abstract). In: Proceedings of the AAAI conference on artificial intelligence, vol. 35, pp 15851–15852

    Google Scholar 

  55. Abdollahpouri H, Burke R (2019) Multi-stakeholder recommendation and its connection to multi-sided fairness. arXiv:1907.13158

  56. Burke R, Sonboli N, Ordonez-Gauger A (2018) Balanced neighborhoods for multi-sided fairness in recommendation. In: Conference on fairness, accountability and transparency. PMLR, pp 202–214

    Google Scholar 

  57. Biswas A, Patro GK, Ganguly N, Gummadi KP, Chakraborty A (2021) Toward fair recommendation in two-sided platforms. ACM Trans Web (TWEB) 16(2):1–34

    Google Scholar 

  58. Basu K, DiCiccio C, Logan H, Karoui NE (2020) A framework for fairness in two-sided marketplaces. arXiv:2006.12756

  59. Sühr T, Biega AJ, Zehlike M, Gummadi KP, Chakraborty A (2019) Two-sided fairness for repeated matchings in two-sided markets: a case study of a ride-hailing platform. In: Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining, pp 3082–3092

    Google Scholar 

  60. Wang L, Joachims T (2021) User fairness, item fairness, and diversity for rankings in two-sided markets. In: Proceedings of the 2021 ACM SIGIR international conference on theory of information retrieval, pp 23–41

    Google Scholar 

  61. Naghiaei M, Rahmani HA, Deldjoo Y (2022) Cpfair: personalized consumer and producer fairness re-ranking for recommender systems. In: SIGIR ’22, New York, NY, USA

    Google Scholar 

  62. Naghiaei M, Rahmani HA, Deldjoo Y (2022) PyCPFair: a framework for consumer and producer fairness in recommender systems. Softw Impacts 13:100382

    Google Scholar 

  63. Wu H, Ma C, Mitra B, Diaz F, Liu X (2022) A multi-objective optimization framework for multi-stakeholder fairness-aware recommendation. ACM Trans Inf Syst (TOIS)

    Google Scholar 

  64. Burke R (2017) Multisided fairness for recommendation. arXiv:1707.00093

  65. Sonboli N, Burke R, Ekstrand M, Mehrotra R (2022) The multisided complexity of fairness in recommender systems. AI Mag 43(2):164–176

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. L3S Research Center, Leibniz University Hannover and Indian Institute of Technology Kharagpur, Kharagpur, India

    Gourab K. Patro

Authors
  1. Gourab K. Patro
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Gourab K. Patro .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, IIT Kharagpur, West Bengal, India

    Animesh Mukherjee

  2. Department of Computer Science, Aalto University, Espoo, Finland

    Juhi Kulshrestha

  3. Department of Computer Science and Engineering, IIT Delhi, New Delhi, Delhi, India

    Abhijnan Chakraborty

  4. School of Computational Science and Engineering, College of Computing, Georgia Institute of Technology, Atlanta, GA, USA

    Srijan Kumar

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Institution of Engineers (India)

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Patro, G.K. (2023). Algorithmic Fairness in Multi-stakeholder Platforms. In: Mukherjee, A., Kulshrestha, J., Chakraborty, A., Kumar, S. (eds) Ethics in Artificial Intelligence: Bias, Fairness and Beyond. Studies in Computational Intelligence, vol 1123. Springer, Singapore. https://doi.org/10.1007/978-981-99-7184-8_5

Download citation

Publish with us

Policies and ethics

Search

Navigation