Robust-satisficing ethics in intelligence

ABSTRACT

Diverse methodologies are employed for ethical intelligence analysis and operations: Kantian deontology optimizes fidelity to moral axioms; pragmatic realism optimally achieves righteous goals (usually national interests); consequentialism optimally balances moral good and bad. Each methodology optimizes an ethically significant entity. Uncertainty adversely impacts ethical intelligence. We develop a distinct methodology for ethical analysis of intelligence under deep uncertainty: satisfice the ethical entity and maximize the robustness to uncertainty. The outcome isn’t necessarily ethically optimal, but it’s ethically adequate over the maximal range of unknown futures. This robust-satisficing methodology is developed generically, and demonstrated for consequentialism on a hypothetical realistic example.

Acknowledgements

The author is indebted to Dr Uri Eran and Prof Andy German for invaluable advice and suggestions.

Disclosure statement

No potential conflict of interest was reported by the author.

Notes

1. Erskine, “As Rays of Light”.

2. Olson, Fair Play, 15–31.

3. Jones, Is ethical Intelligence an Contradiction.

4. Shelton, “Framing the Oxymoron”.

5. Erskine, “As Rays of Light,” op. cit., 376.

6. Olson, Fair Play, op. cit.

7. Erskine, “As Rays of Light,” op. cit., 375.

8. Le Carrè, The Spy Who Came in from the Cold, 17.

9. See note 5 above.

10. Herman, Intelligence Services, 202.

11. Gendron, “Just War, Just Intelligence,” 418.

12. Pekel, The Need for Improvement, 57.

13. Diderichsen and Rønn, “Intelligence by Consent,’ 479, 483, 480.

14. Gill, “Security Intelligence and Human Rights,” 81, 82.

15. Ben-Haim, “Info-gap Forecasting”; Ben-Haim and Hemez, “Robustness, Fidelity and Prediction-looseness”; Ben-Haim and Smithson, “Data-based Prediction Under Uncertainty”.

16. Walsh and Miller, “Rethinking ‘Five Eye’,” 348, 361.

17. DNI website.

18. Erskine, “As rays of Light,” op. cit., 368–369.

19. Quinlan, “Just Intelligence,, 8, 12.

20. Verma, “Intelligence Officers as Professionals,” 376.

21. Herman, Intelligence Services, op. cit., 345, 356.

22. Erskine, “As rays of Light,” op. cit., 366.

23. Olson, Fair Play, 49–50.

24. Skerker, Interrogation Ethics, 149.

25. Wald, “Statistical Decision Functions”.

26. Ben-Haim and Demertzis, “Decision Making in Times of Knightian Uncertainty”; and Ben-Haim, Dilemmas of Wonderland.

27. Ben-Haim, Info-Gap Decision Theory; and Ben-Haim, Dilemmas of Wonderland.

Additional information

Notes on contributors

Yakov Ben-Haim

Prof. Yakov Ben-Haim initiated and developed info-gap decision theory for modeling and managing deep uncertainty. Info-gap theory is a decision-support tool, providing a methodology for assisting in the assessment and selection of policy, strategy, action, or decision in a wide range of disciplines. Info-gap theory has impacted the fundamental understanding of uncertainty in human affairs, and is applied in decision-making by scholars and practitioners around the world in engineering, biological conservation, economics, project management, climate change, natural hazard response, national security, medicine, and other areas (see info-gap.com). He has been a visiting scholar in many countries and has lectured at universities, technological and medical research institutions, public utilities and central banks. He has published more than 100 articles and 6 books. He is a professor of mechanical engineering and holds the Yitzhak Moda’i Chair in Technology and Economics at the Technion - Israel Institute of Technology.