mlsanityData to 14 Oct 2026

On the Current and Emerging Challenges of Developing Fair and Ethical AI Solutions in Financial Services

arXiv 2111.01306 ↗First posted 2 Nov 2021cs.CY cs.AI cs.CEcited by 4

Not on the map. Why?

Abstract (arXiv)

Artificial intelligence (AI) continues to find more numerous and more critical applications in the financial services industry, giving rise to fair and ethical AI as an industry-wide objective. While many ethical principles and guidelines have been published in recent years, they fall short of addressing the serious challenges that model developers face when building ethical AI solutions. We survey the practical and overarching issues surrounding model development, from design and implementation complexities, to the shortage of tools, and the lack of organizational constructs. We show how practical considerations reveal the gaps between high-level principles and concrete, deployed AI applications, with the aim of starting industry-wide conversations toward solution approaches.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 24 of its 159 references that are in the map. First posted on arXiv 2 Nov 2021.

Builds on

24 of 159 references.

References · 24

  1. 2013Intriguing properties of neural networkscited by 5,155
  2. 2019A Survey on Bias and Fairness in Machine Learningcited by 1,917
  3. 2016Practical Black-Box Attacks against Machine Learningcited by 903
  4. 2017Counterfactual Fairnesscited by 804
  5. 2016Fair prediction with disparate impact: A study of bias in recidivism prediction instrumentscited by 672
  6. 2016Inherent Trade-Offs in the Fair Determination of Risk Scorescited by 586
  7. 2018Learning Adversarially Fair and Transferable Representationscited by 353
  8. 2017Fairness in Criminal Justice Risk Assessments: The State of the Artcited by 315
  9. 2019Mitigating Gender Bias in Natural Language Processing: Literature Reviewcited by 287
  10. 2017Multi-agent Reinforcement Learning in Sequential Social Dilemmascited by 209
  11. 2020Characterising Bias in Compressed Modelscited by 167
  12. 2019Principles alone cannot guarantee ethical AIcited by 135
12 more
  1. 2019The Ethics of AI Ethics -- An Evaluation of Guidelinescited by 129
  2. 2018The Social Cost of Strategic Classificationcited by 105
  3. 2018The Measure and Mismeasure of Fairnesscited by 95
  4. 2019Problem Formulation and Fairnesscited by 85
  5. 2021Demystifying the Draft EU Artificial Intelligence Actcited by 84
  6. 2018The Disparate Effects of Strategic Manipulationcited by 75
  7. 2018Practical Deep Reinforcement Learning Approach for Stock Tradingcited by 49
  8. 2020Decisions, Counterfactual Explanations and Strategic Behaviorcited by 44
  9. 2020Towards Self-Regulating AI: Challenges and Opportunities of AI Model Governance in Financial Servicescited by 8
  10. 2016The Dark Side of Ethical Robotscited by 5
  11. 2015Taxonomy of Pathways to Dangerous AIcited by 4
  12. 2019Attesting Biases and Discrimination using Language Semanticscited by 1

Built on by

Cited by year

Citing papers per year: 2024: 2; 2025: 2.

4 papers.

  1. 2024What is the Role of Small Models in the LLM Era: A Surveycited by 41
  2. 2024EARN Fairness: Explaining, Asking, Reviewing, and Negotiating Artificial Intelligence Fairness Metrics Among Stakeholderscited by 5
  3. 2025Are Bias Evaluation Methods Biased ?cited by 4
  4. 2025"I think this is fair": Uncovering the Complexities of Stakeholder Decision-Making in AI Fairness Assessmentcited by 0