Current - Issue
Year 2026 · Volume 6 · Issue 5
Original Article
Introduction to Techno-Democracy
Dr. Nitnem Singh Sodhi1
1 Managing Director of Bharat Neurotech and Independent Researcher based in Lucknow, Ex-Military Psychologist at Indian Air Force, Consultant Psychologist at Apollo Clinics in Lucknow and Gorakhpur, presently learning AI from IIT Kharagpur.
Published Online: September-October 2026
Pages: 330-348
Cite this article
↗ https://www.doi.org/10.59256/ijrtmr.20260605036References
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accountability. New Media & Society, 20(3), 973–989. https://doi.org/10.1177/1461444816676645
3. Bainbridge, L. (1983). Ironies of automation. Automatica, 19(6), 775–779. https://doi.org/10.1016/0005-1098(83)90046-8
4. Banerjee, A., Duflo, E., Imbert, C., Mathew, S., & Pande, R. (2020). E-governance, accountability, and leakage in public programs:
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https://doi.org/10.1257/app.20180302
5. Biggio, B., Nelson, B., & Laskov, P. (2012). Poisoning attacks against support vector machines. Proceedings of the 29th International
Conference on Machine Learning. Author manuscript: https://arxiv.org/abs/1206.6389
6. Bloom, P., & Sancino, A. (2019). Disruptive democracy: The clash between techno-populism and techno-democracy. SAGE.
https://uk.sagepub.com/en-gb/eur/disruptive-democracy/book263534
7. Blum, C., & Zuber, C. I. (2016). Liquid democracy: Potentials, problems, and perspectives. Journal of Political Philosophy, 24(2), 162–
182. https://doi.org/10.1111/jopp.12065
8. Bovens, M. (2007). Analysing and assessing accountability: A conceptual framework. European Law Journal, 13(4), 447–468.
https://doi.org/10.1111/j.1468-0386.2007.00378.x
9. Buolamwini, J., & Gebru, T. (2018). Gender shades: Intersectional accuracy disparities in commercial gender classification. Proceedings
of Machine Learning Research, 81, 77–91. https://proceedings.mlr.press/v81/buolamwini18a.html
10. Calvano, E., Calzolari, G., Denicolò, V., & Pastorello, S. (2020). Artificial intelligence, algorithmic pricing, and collusion. American
Economic Review, 110(10), 3267–3297. https://doi.org/10.1257/aer.20190623
11. Castro, M., & Liskov, B. (1999). Practical Byzantine fault tolerance. Proceedings of the Third Symposium on Operating Systems Design
and Implementation. USENIX Association. https://www.usenix.org/conference/osdi-99/practical-byzantine-fault-tolerance
12. Citron, D. K. (2008). Technological due process. Washington University Law Review, 85(6), 1249–1313.
https://journals.library.wustl.edu/lawreview/article/id/6697/
13. Crawford, K. (2021). Atlas of AI: Power, politics, and the planetary costs of artificial intelligence. Yale University Press.
https://doi.org/10.12987/9780300252392
14. Crosby, S. A., & Wallach, D. S. (2009). Efficient data structures for tamper-evident logging. Proceedings of the 18th USENIX Security
Symposium. USENIX Association. https://www.usenix.org/conference/usenixsecurity09/technical-sessions/presentation/efficient-datastructures-
tamper-evident
15. Dahl, R. A. (1989). Democracy and its critics. Yale University Press. https://yalebooks.yale.edu/book/9780300049381/democracy-andits-
critics/
16. De Filippi, P., & Wright, A. (2018). Blockchain and the law: The rule of code. Harvard University Press.
https://doi.org/10.2307/j.ctv2867sp
17. Dunleavy, P., Margetts, H., Bastow, S., & Tinkler, J. (2006). New public management is dead—Long live digital-era governance. Journal
of Public Administration Research and Theory, 16(3), 467–494. https://doi.org/10.1093/jopart/mui057
18. Dwork, C., & Roth, A. (2014). The algorithmic foundations of differential privacy. Foundations and Trends in Theoretical Computer
Science, 9(3–4), 211–407. https://doi.org/10.1561/0400000042
19. Feldstein, S. (2021). The rise of digital repression: How technology is reshaping power, politics, and resistance. Oxford University Press.
https://academic.oup.com/book/39418
20. Gebru, T., Morgenstern, J., Vecchione, B., Vaughan, J. W., Wallach, H., Daumé III, H., & Crawford, K. (2021). Datasheets for datasets.
Communications of the ACM, 64(12), 86–92. https://doi.org/10.1145/3458723
21. Greshake, K., Abdelnabi, S., Mishra, S., Endres, C., Holz, T., & Fritz, M. (2023). Not what you've signed up for: Compromising realworld
LLM-integrated applications with indirect prompt injection [Preprint]. arXiv. https://arxiv.org/abs/2302.12173
22. Haber, S., & Stornetta, W. S. (1991). How to time-stamp a digital document. Journal of Cryptology, 3, 99–111.
https://doi.org/10.1007/BF00196791
23. Hernán, M. A., & Robins, J. M. (2020). Causal inference: What if. Chapman & Hall/CRC. Author-hosted text:
https://www.hsph.harvard.edu/miguel-hernan/wp-content/uploads/sites/1268/2024/01/hernanrobins_WhatIf_2jan24.pdf
24. Hevner, A. R., March, S. T., Park, J., & Ram, S. (2004). Design science in information systems research. MIS Quarterly, 28(1), 75–105.
https://doi.org/10.2307/25148625
25. Hildebrandt, M. (2018). Algorithmic regulation and the rule of law. Philosophical Transactions of the Royal Society A, 376(2128), Article
20170355. https://doi.org/10.1098/rsta.2017.0355
26. Kleinberg, J., Mullainathan, S., & Raghavan, M. (2017). Inherent trade-offs in the fair determination of risk scores. 8th Innovations in
Theoretical Computer Science Conference, LIPIcs 67, Article 43. https://doi.org/10.4230/LIPIcs.ITCS.2017.43
27. Kroll, J. A., Huey, J., Barocas, S., Felten, E. W., Reidenberg, J. R., Robinson, D. G., & Yu, H. (2017). Accountable algorithms. University
of Pennsylvania Law Review, 165(3), 633–705. https://pennlawreview.com/2017/02/23/accountable-algorithms/
28. Kusner, M. J., Loftus, J., Russell, C., & Silva, R. (2017). Counterfactual fairness. Advances in Neural Information Processing Systems,
30. https://papers.nips.cc/paper/2017/hash/a486cd07e4ac3d270571622f4f316ec5-Abstract.html
29. Lipsky, M. (2010). Street-level bureaucracy: Dilemmas of the individual in public services (30th anniversary expanded ed.). Russell Sage
Foundation. https://doi.org/10.7758/9781610446631
30. Medina, E. (2011). Cybernetic revolutionaries: Technology and politics in Allende's Chile. MIT Press.
https://mitpress.mit.edu/9780262016490/cybernetic-revolutionaries/
31. Mitchell, M., Wu, S., Zaldivar, A., Barnes, P., Vasserman, L., Hutchinson, B., Spitzer, E., Raji, I. D., & Gebru, T. (2019). Model cards
for model reporting. Proceedings of the Conference on Fairness, Accountability, and Transparency, 220–229.
https://doi.org/10.1145/3287560.3287596
32. Olken, B. A. (2007). Monitoring corruption: Evidence from a field experiment in Indonesia. Journal of Political Economy, 115(2), 200–
249. https://doi.org/10.1086/517935
33. Ostrom, E. (2010). Beyond markets and states: Polycentric governance of complex economic systems. American Economic Review,
100(3), 641–672. https://doi.org/10.1257/aer.100.3.64134. Rahwan, I. (2018). Society-in-the-loop: Programming the algorithmic social contract. Ethics and Information Technology, 20(1), 5–14.
https://doi.org/10.1007/s10676-017-9430-8
35. Raji, I. D., Smart, A., White, R. N., Mitchell, M., Gebru, T., Hutchinson, B., Smith-Loud, J., Theron, D., & Barnes, P. (2020). Closing the
AI accountability gap: Defining an end-to-end framework for internal algorithmic auditing. Proceedings of the 2020 Conference on
Fairness, Accountability, and Transparency, 33–44. https://doi.org/10.1145/3351095.3372873
36. Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead.
Nature Machine Intelligence, 1, 206–215. https://doi.org/10.1038/s42256-019-0048-x
37. Selbst, A. D., boyd, d., Friedler, S. A., Venkatasubramanian, S., & Vertesi, J. (2019). Fairness and abstraction in sociotechnical systems.
Proceedings of the Conference on Fairness, Accountability, and Transparency, 59–68. https://doi.org/10.1145/3287560.3287598
38. Shleifer, A., & Vishny, R. W. (1993). Corruption. The Quarterly Journal of Economics, 108(3), 599–617. https://doi.org/10.2307/2118402
39. Tan, J. Z., Langenkamp, M., Weichselbraun, A., Brody, A., & Korpas, L. (2024). The constitutions of Web3 [Preprint]. arXiv.
https://arxiv.org/abs/2403.00081
40. Wirtz, B. W., Weyerer, J. C., & Geyer, C. (2019). Artificial intelligence and the public sector—Applications and challenges. International
Journal of Public Administration, 42(7), 596–615. https://doi.org/10.1080/01900692.2018.1498103
41. Wüst, K., & Gervais, A. (2018). Do you need a blockchain? 2018 Crypto Valley Conference on Blockchain Technology, 45–54.
https://doi.org/10.1109/CVCBT.2018.00011
42. Yeung, K. (2018). Algorithmic regulation: A critical interrogation. Regulation & Governance, 12(4), 505–523.
https://doi.org/10.1111/rego.12158
https://arxiv.org/abs/1606.06565
2. Ananny, M., & Crawford, K. (2018). Seeing without knowing: Limitations of the transparency ideal and its application to algorithmic
accountability. New Media & Society, 20(3), 973–989. https://doi.org/10.1177/1461444816676645
3. Bainbridge, L. (1983). Ironies of automation. Automatica, 19(6), 775–779. https://doi.org/10.1016/0005-1098(83)90046-8
4. Banerjee, A., Duflo, E., Imbert, C., Mathew, S., & Pande, R. (2020). E-governance, accountability, and leakage in public programs:
Experimental evidence from a financial management reform in India. American Economic Journal: Applied Economics, 12(4), 39–72.
https://doi.org/10.1257/app.20180302
5. Biggio, B., Nelson, B., & Laskov, P. (2012). Poisoning attacks against support vector machines. Proceedings of the 29th International
Conference on Machine Learning. Author manuscript: https://arxiv.org/abs/1206.6389
6. Bloom, P., & Sancino, A. (2019). Disruptive democracy: The clash between techno-populism and techno-democracy. SAGE.
https://uk.sagepub.com/en-gb/eur/disruptive-democracy/book263534
7. Blum, C., & Zuber, C. I. (2016). Liquid democracy: Potentials, problems, and perspectives. Journal of Political Philosophy, 24(2), 162–
182. https://doi.org/10.1111/jopp.12065
8. Bovens, M. (2007). Analysing and assessing accountability: A conceptual framework. European Law Journal, 13(4), 447–468.
https://doi.org/10.1111/j.1468-0386.2007.00378.x
9. Buolamwini, J., & Gebru, T. (2018). Gender shades: Intersectional accuracy disparities in commercial gender classification. Proceedings
of Machine Learning Research, 81, 77–91. https://proceedings.mlr.press/v81/buolamwini18a.html
10. Calvano, E., Calzolari, G., Denicolò, V., & Pastorello, S. (2020). Artificial intelligence, algorithmic pricing, and collusion. American
Economic Review, 110(10), 3267–3297. https://doi.org/10.1257/aer.20190623
11. Castro, M., & Liskov, B. (1999). Practical Byzantine fault tolerance. Proceedings of the Third Symposium on Operating Systems Design
and Implementation. USENIX Association. https://www.usenix.org/conference/osdi-99/practical-byzantine-fault-tolerance
12. Citron, D. K. (2008). Technological due process. Washington University Law Review, 85(6), 1249–1313.
https://journals.library.wustl.edu/lawreview/article/id/6697/
13. Crawford, K. (2021). Atlas of AI: Power, politics, and the planetary costs of artificial intelligence. Yale University Press.
https://doi.org/10.12987/9780300252392
14. Crosby, S. A., & Wallach, D. S. (2009). Efficient data structures for tamper-evident logging. Proceedings of the 18th USENIX Security
Symposium. USENIX Association. https://www.usenix.org/conference/usenixsecurity09/technical-sessions/presentation/efficient-datastructures-
tamper-evident
15. Dahl, R. A. (1989). Democracy and its critics. Yale University Press. https://yalebooks.yale.edu/book/9780300049381/democracy-andits-
critics/
16. De Filippi, P., & Wright, A. (2018). Blockchain and the law: The rule of code. Harvard University Press.
https://doi.org/10.2307/j.ctv2867sp
17. Dunleavy, P., Margetts, H., Bastow, S., & Tinkler, J. (2006). New public management is dead—Long live digital-era governance. Journal
of Public Administration Research and Theory, 16(3), 467–494. https://doi.org/10.1093/jopart/mui057
18. Dwork, C., & Roth, A. (2014). The algorithmic foundations of differential privacy. Foundations and Trends in Theoretical Computer
Science, 9(3–4), 211–407. https://doi.org/10.1561/0400000042
19. Feldstein, S. (2021). The rise of digital repression: How technology is reshaping power, politics, and resistance. Oxford University Press.
https://academic.oup.com/book/39418
20. Gebru, T., Morgenstern, J., Vecchione, B., Vaughan, J. W., Wallach, H., Daumé III, H., & Crawford, K. (2021). Datasheets for datasets.
Communications of the ACM, 64(12), 86–92. https://doi.org/10.1145/3458723
21. Greshake, K., Abdelnabi, S., Mishra, S., Endres, C., Holz, T., & Fritz, M. (2023). Not what you've signed up for: Compromising realworld
LLM-integrated applications with indirect prompt injection [Preprint]. arXiv. https://arxiv.org/abs/2302.12173
22. Haber, S., & Stornetta, W. S. (1991). How to time-stamp a digital document. Journal of Cryptology, 3, 99–111.
https://doi.org/10.1007/BF00196791
23. Hernán, M. A., & Robins, J. M. (2020). Causal inference: What if. Chapman & Hall/CRC. Author-hosted text:
https://www.hsph.harvard.edu/miguel-hernan/wp-content/uploads/sites/1268/2024/01/hernanrobins_WhatIf_2jan24.pdf
24. Hevner, A. R., March, S. T., Park, J., & Ram, S. (2004). Design science in information systems research. MIS Quarterly, 28(1), 75–105.
https://doi.org/10.2307/25148625
25. Hildebrandt, M. (2018). Algorithmic regulation and the rule of law. Philosophical Transactions of the Royal Society A, 376(2128), Article
20170355. https://doi.org/10.1098/rsta.2017.0355
26. Kleinberg, J., Mullainathan, S., & Raghavan, M. (2017). Inherent trade-offs in the fair determination of risk scores. 8th Innovations in
Theoretical Computer Science Conference, LIPIcs 67, Article 43. https://doi.org/10.4230/LIPIcs.ITCS.2017.43
27. Kroll, J. A., Huey, J., Barocas, S., Felten, E. W., Reidenberg, J. R., Robinson, D. G., & Yu, H. (2017). Accountable algorithms. University
of Pennsylvania Law Review, 165(3), 633–705. https://pennlawreview.com/2017/02/23/accountable-algorithms/
28. Kusner, M. J., Loftus, J., Russell, C., & Silva, R. (2017). Counterfactual fairness. Advances in Neural Information Processing Systems,
30. https://papers.nips.cc/paper/2017/hash/a486cd07e4ac3d270571622f4f316ec5-Abstract.html
29. Lipsky, M. (2010). Street-level bureaucracy: Dilemmas of the individual in public services (30th anniversary expanded ed.). Russell Sage
Foundation. https://doi.org/10.7758/9781610446631
30. Medina, E. (2011). Cybernetic revolutionaries: Technology and politics in Allende's Chile. MIT Press.
https://mitpress.mit.edu/9780262016490/cybernetic-revolutionaries/
31. Mitchell, M., Wu, S., Zaldivar, A., Barnes, P., Vasserman, L., Hutchinson, B., Spitzer, E., Raji, I. D., & Gebru, T. (2019). Model cards
for model reporting. Proceedings of the Conference on Fairness, Accountability, and Transparency, 220–229.
https://doi.org/10.1145/3287560.3287596
32. Olken, B. A. (2007). Monitoring corruption: Evidence from a field experiment in Indonesia. Journal of Political Economy, 115(2), 200–
249. https://doi.org/10.1086/517935
33. Ostrom, E. (2010). Beyond markets and states: Polycentric governance of complex economic systems. American Economic Review,
100(3), 641–672. https://doi.org/10.1257/aer.100.3.64134. Rahwan, I. (2018). Society-in-the-loop: Programming the algorithmic social contract. Ethics and Information Technology, 20(1), 5–14.
https://doi.org/10.1007/s10676-017-9430-8
35. Raji, I. D., Smart, A., White, R. N., Mitchell, M., Gebru, T., Hutchinson, B., Smith-Loud, J., Theron, D., & Barnes, P. (2020). Closing the
AI accountability gap: Defining an end-to-end framework for internal algorithmic auditing. Proceedings of the 2020 Conference on
Fairness, Accountability, and Transparency, 33–44. https://doi.org/10.1145/3351095.3372873
36. Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead.
Nature Machine Intelligence, 1, 206–215. https://doi.org/10.1038/s42256-019-0048-x
37. Selbst, A. D., boyd, d., Friedler, S. A., Venkatasubramanian, S., & Vertesi, J. (2019). Fairness and abstraction in sociotechnical systems.
Proceedings of the Conference on Fairness, Accountability, and Transparency, 59–68. https://doi.org/10.1145/3287560.3287598
38. Shleifer, A., & Vishny, R. W. (1993). Corruption. The Quarterly Journal of Economics, 108(3), 599–617. https://doi.org/10.2307/2118402
39. Tan, J. Z., Langenkamp, M., Weichselbraun, A., Brody, A., & Korpas, L. (2024). The constitutions of Web3 [Preprint]. arXiv.
https://arxiv.org/abs/2403.00081
40. Wirtz, B. W., Weyerer, J. C., & Geyer, C. (2019). Artificial intelligence and the public sector—Applications and challenges. International
Journal of Public Administration, 42(7), 596–615. https://doi.org/10.1080/01900692.2018.1498103
41. Wüst, K., & Gervais, A. (2018). Do you need a blockchain? 2018 Crypto Valley Conference on Blockchain Technology, 45–54.
https://doi.org/10.1109/CVCBT.2018.00011
42. Yeung, K. (2018). Algorithmic regulation: A critical interrogation. Regulation & Governance, 12(4), 505–523.
https://doi.org/10.1111/rego.12158
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