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Machine Learning, Law and Ethics

Vincent Agnello & Andrew Kumiega

Business and Professional Ethics Journal2025https://doi.org/10.5840/bpej20251112176article
AJG 1ABDC C
Weight
0.50

What the paper says

In business, applying machine learning (mL) algorithms to make decisions in finance is expanding from proprietary trading to making financial decisions that affect everyone. Unfortunately, mL algorithms are statistical methods that lack ethics or fairness. Since many mL algorithms operate as a black box solution, even the users of these techniques can be unaware of ethical issues in finance. Nevertheless, an mL algorithm used as a simple tool with no regard for ethics can make unethical decisions. This paper first details how and when an mL algorithm can make unethical decisions. It then proposes a framework to minimize the risk of an mL algorithm making a prediction considered unethical or contrary to the code of conduct for Chartered Financial Analysts, Certified Financial Planners, or Certified Loan Officers.

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https://doi.org/https://doi.org/10.5840/bpej20251112176

Or copy a formatted citation

@article{vincent2025,
  title        = {{Machine Learning, Law and Ethics}},
  author       = {Vincent Agnello & Andrew Kumiega},
  journal      = {Business and Professional Ethics Journal},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.5840/bpej20251112176},
}

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Machine Learning, Law and Ethics

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.