CoChat
Sign Up
Cited by 76Explainable Artificial Intelligence (XAI)Ethics and Social Impacts of AIDigitalization, Law, and Regulation

Unveiling the Black Box: Bringing Algorithmic Transparency to AI

Open access
Chaudhary, Gyandeep·2024·Masaryk University Journal of Law and Technology
DOI 10.5817/mujlt2024-1-4
Open this paper in CoChat — free account

Abstract

Overall, algorithmic transparency is an important aspect of responsible AI development and deployment. Ensuring that AI systems are transparent and accountable will help build trust and confidence in these systems and ensure that they are used ethically and effectively. Artificial intelligence (AI) has emerged as a cutting-edge domain that is fundamentally redefining different areas of daily experiences, such as health care, transport, finance, education, and others. The systems are not created for making a judgment like human judgment of natural language, spotting patterns and problem-solving; rather AI produces machines that also have intelligence level same as that of human beings. AI having more influence over us, it is to be considered the ethical directions of these tools and see that they operate under principles of transparency and accountability. The element regarding algorithmic transparency, which means the process of understanding the functioning and explanation of how AI systems make their decisions is the one that is most crucial. The issue of algorithm transparency is of fundamental importance for many considerations. AI systems are not only supported by fairness but also by their non-discrimination. If we do not know how a system of AI arrives at the decisions made, it becomes impossible to determine if the provided results meet equal treatment for everybody. If used in delicate areas like recruitment, credit, and legal system- where the AI-machine must make choices which are life changing, then this aspect is very important. On top of fairness, algorithmic transparency is also an important factor for accountability. If we are ignorant about what an artificial intelligence algorithm does and what is the source of its decision-making process, we are unable to track and classify the mistakes or mishaps of the system. This has always mattered when central to the operation of systems with high stake, such as those used in self-driving vehicles or in health care. Algorithmic transparency may be reached using different instruments. The transparent AI systems can be made by a more transparent design, for example, the simple modelling tools, that use interpretable models. Another method is designing technologies and techniques that can help people why the artificial systems difficult to be decoded but easy to understand which they can utilize in making decisions. Therefore, algorithmic transparency is a key factor of the AI made responsibly and used by the society. It is crucial that AI machines are both transparent and accountable since this will lead to people building trust in the system and accepting its ethical and practical implications. This paper examines regulation of algorithmic transparency in the EU, specifically provisions under the General Data Protection Regulation (GDPR), it aims to situate analysis of the GDPR's provisions on explainability of AI systems within broader technology ethics and policy discourse. The paper's scope is limited to EU regulations applicable to AI data processing transparency.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.5817/mujlt2024-1-4

Cites

31 of 31 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

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?

  • Students subject to consequential decisions based on opaque algorithmic outputs have no meaningful right of reply because neither they nor their educators can examine the reasoning behind the flag.

    “Chaudhary (2024), in an analysis of algorithmic transparency, describes this as a black box problem with specific legal and ethical dimensions: students subject to consequential decisions based on opaque algorithmic outputs have no meaningful right of reply, because neither they nor their educators can examine the reasoning behind the flag.”

    From Harms and Ethical Risks of AI Detection Tools in Education: False Positives, Bias, Surveillance, and Student Rights (2026) · cites this paper as Chaudhary (2024)

    The indexed Abstract says that when an AI system's decision-making is opaque, people cannot assess whether its results are fair or track its mistakes; it identifies this as especially important for consequential decisions. It does not discuss students, educators, algorithmic flags, or a lack of a meaningful right of reply, so the education-specific claim goes beyond the source. (LLM, conf=0.86)

    Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Cited by

Sorted by citations

Related

Authors
Chaudhary, G.
Affiliations
Bennett University (IN)
Corresponding author
Chaudhary, Gyandeep
ORCID
Chaudhary, Gyandeep
Venue
Masaryk University Journal of Law and Technology
Publisher
Masaryk University Press
Published
2024-06-29
Volume
18
Issue
1
Pages
93-122
ISSN
  • 1802-5943 (ISSN-L)
  • 1802-5951
DOI
10.5817/mujlt2024-1-4
OpenAlex
W4400257155
Cited by
74
References
31
Citations by year
2026: 38 · 2025: 32 · 2024: 4
FWCI
10.92
Type
journal article
Language
en
Open access
Locations
Masaryk University Journal of Law and Technology (publishedVersion)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
  • Social Sciences › Safety Research › Ethics and Social Impacts of AI
  • Social Sciences › Law › Digitalization, Law, and Regulation
Keywords
algorithmic transparency, explainable AI, responsible AI, accountability, algorithmic fairness, non-discrimination, General Data Protection Regulation, EU AI regulation, ethics of technology, trust in AI, interpretability, high-stakes decision making, algorithmic decision-making, autonomous vehicles, health care 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