Publication2024
Fostering Understanding: Bridging the Gap Between Black-Box Models and Human Interpretability with Explainable Artificial Intelligence
The record
- Type
- Publication
- Date
- 1 January 2024
- Level
- National
- Journal
- Smart Innovation Systems and Technologies
- Volume
- 390
- Pages
- 215-227
- Published
- 1 January 2024
- DOI
- 10.1007/978-981-97-2716-2_20
- Scopus ID
- 85201120386
- ISSN
- 21903018
- Authors
- 6
- Open access
- No
- Citations
- 0
- International co-authorship
- No
- Co-author countries
- IN
Classification
Every label below carries the reasoning it was assigned on, the confidence band it was assigned in, and what produced it. The rubric is published.
UN Sustainable Development Goals
7.3 Double the global rate of improvement in energy efficiency
Explainable AI benchmarking for energy performance certification prediction enables transparent understanding of building energy functionality and efficiency improvements.
- High confidence
- The subject matter is a strong and direct inference from the record type, the funding or organising body, or the programme context, even where the title alone is thin. Example: an ICMR-funded project against SDG 3, on the strength of the agency.
- Judged
- Judged against the published classification rubric, under the rubric and prompt versions recorded with the run that produced it.
- Produced by
- claude-haiku-4-5-20251001, under kortex-publication-v1
9.5 Enhance scientific research, upgrade technological capabilities, and increase researchers and R&D spending
Machine learning interpretability research advances AI innovation capacity for complex real-world energy prediction and decision support system development.
- Moderate confidence
- The alignment is plausible and defensible but rests on an assumption a reasonable reviewer could decline. Published, and surfaced in the review queue for a human to confirm or override.
- Produced by
- claude-haiku-4-5-20251001, under kortex-publication-v1
Viksit Bharat @2047
vb-3.2 Digital technology and artificial intelligence
Explainable artificial intelligence for energy sector applications advances digital technology innovation for sustainable energy management and building efficiency.
- Produced by
- claude-haiku-4-5-20251001, under kortex-publication-v1
Each label above carries the instrument and the version that produced it. How each classification is reached.
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- Source rows resolved into this record
- 1
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Citing this record
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KIIT Deemed to be University. “Fostering Understanding: Bridging the Gap Between Black-Box Models and Human Interpretability with Explainable Artificial Intelligence”. Kortex.