Student or educator? Sign up with your school email and get 6 months of Pro free.
CoChat
Sign Up
Cited by 76Ethics and Social Impacts of AIExplainable Artificial Intelligence (XAI)Artificial Intelligence Applications

The DOI matches its Crossref record; 16 of the 16 references it cites were found in a registry or an index.

Ethical Implication of Artificial Intelligence (AI) Adoption in Financial Decision Making

Open access
Owolabi, Omoshola S., Uche, Prince C., Adeniken, Nathaniel T., Ihejirika, Christopher, Islam, Riyad Bin, Chhetri, Bishal Jung Thapa·2024·Computer and Information Science
DOI 10.5539/cis.v17n1p49
Open this paper in CoChat — free account

Abstract

The integration of artificial intelligence (AI) into the financial sector has raised ethical concerns that need to be addressed. This paper analyzes the ethical implications of using AI in financial decision-making and emphasizes the importance of an ethical framework to ensure its fair and trustworthy deployment. The study explores various ethical considerations, including the need to address algorithmic bias, promote transparency and explainability in AI systems, and adhere to regulations that protect equity, accountability, and public trust. By synthesizing research and empirical evidence, the paper highlights the complex relationship between AI innovation and ethical integrity in finance. To tackle this issue, the paper proposes a comprehensive and actionable ethical framework that advocates for clear guidelines, governance structures, regular audits, and collaboration among stakeholders. This framework aims to maximize the potential of AI while minimizing negative impacts and unintended consequences. The study serves as a valuable resource for policymakers, industry professionals, researchers, and other stakeholders, facilitating informed discussions, evidence-based decision-making, and the development of best practices for responsible AI integration in the financial sector. The ultimate goal is to ensure fairness, transparency, and accountability while reaping the benefits of AI for both the financial sector and society.

Overview

Integrity signals

Registry

Crossref · 10.5539/cis.v17n1p49

Cites

16 of 16 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Verification

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Citations

Cites

Show all 15 references

Cited by

Related

See all

Metadata

Authors
Owolabi, O. S., Uche, P. C., Adeniken, N. T., Ihejirika, C., Islam, R. B., & Chhetri, B. J. T.
ORCID
Ihejirika, Christopher
Venue
Computer and Information Science
Publisher
Canadian Center of Science and Education
Published
2024-04-30
Volume
17
Issue
1
Pages
49
ISSN
  • 1913-8989 (ISSN-L)
  • 1913-8997
DOI
10.5539/cis.v17n1p49
OpenAlex
W4396498564
Cited by
56
References
15
Citations by year
2026: 17 · 2025: 30 · 2024: 9
FWCI
23.32
Type
journal article
Language
en
Open access
Licence
Locations
  • Computer and Information Science (publishedVersion, cc-by)
  • RePEc: Research Papers in Economics (submittedVersion)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
  • Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
  • Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
Keywords
AI ethics, financial decision-making, algorithmic bias, transparency and explainability, AI governance, ethical frameworks, accountability, public trust, financial regulation, responsible AI

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.