Author Identifier (ORCID)
Mohiuddin Ahmed: https://orcid.org/0000-0002-4559-4768
Abstract
Cybersecurity demands creativity, persistence, and sharp pattern recognition—strengths frequently reported among neurodivergent people (e.g., autism, ADHD, dyslexia). Yet AI-driven hiring pipelines can systematically disadvantage neurodivergent applicants by misreading communication styles or valuing narrow proxies of “fit.” Demand for cybersecurity talent is growing, and experts note that neurodiverse individuals are both underrepresented and highly valuable to security teams. However, progress remains uneven without targeted educational interventions [1]. We present a curricular module that simultaneously (a) centers neurodiversity as a strength in the cybersecurity workforce and (b) trains students to audit and redesign AI hiring systems using open-source fairness and explainability toolkits (AIF360 and SHAP). Students run end-to-end labs, evaluate trade-offs between performance and equity, and propose inclusive pipeline redesigns aligned with emerging policy guidance on AI and disability [7].1
Keywords
Algorithmic fairness, cybersecurity education, disability, explainability, inclusive design, neurodiversity
Document Type
Conference Proceeding
Date of Publication
2-17-2026
Publication Title
SIGCSE TS 2026: Proceedings of the 57th ACM Technical Symposium on Computer Science Education V.2
Publisher
Association for Computing Machinery
School
School of Science
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
First Page
1373
Last Page
1374
Comments
Islam, S. R., Keim, Y., Ahmed, M., Gupta, M., Russell, I., & Abdelsalam, M. (2026, February). Empowering neurodiverse talent in cybersecurity through fair and inclusive AI education. In Proceedings of the 57th ACM Technical Symposium on Computer Science Education V. 2 (pp. 1373-1374). Association for Computing Machinery. https://doi.org/10.1145/3770761.3777172