Moral deficiency in AI decision-making: Underlying mechanisms and mitigation strategies

doi:10.3724/SP.J.1041.2026.0074

Abstract

Abstract:

As artificial intelligence (AI) assumes an increasingly prominent role in high-stakes decision-making, the ethical challenges it raises have become a pressing concern. This study systematically investigates the moral deficit effect in AI decision making by integrating mind perception theory with moral dualism. Through this framework, we identify a dual-path psychological mechanism and propose targeted intervention strategies. The findings show that individuals display significantly weaker moral reactions to unethical decisions made by AI than to those made by humans. This moral deficit effect arises because AI is perceived as having lower agency and experientiality compared to human decision-makers. To address this gap, the study proposes an integrated intervention strategy: enhancing moral responses through anthropomorphizing AI while simultaneously adjusting human expectations. Unlike disciplines that primarily focus on designing fair algorithms at the technical level, this study takes a psychological perspective. By highlighting differences in people’s moral judgments of AI versus human decisions, it provides new insights into mitigating the social consequences of algorithmic bias and contributes to the broader development of “algorithmic ethics.”

Key words: artificial intelligence, moral deficit effect, mind perception, anthropomorphism, expectation adjustment

HU Xiaoyong, LI Mufeng, LI Yue, LI Kai, YU Feng. (2026). Moral deficiency in AI decision-making: Underlying mechanisms and mitigation strategies. Acta Psychologica Sinica, 58(1), 74-95.

Figures/Tables 11

Table 1 Descriptive statistics of moral responses and scores across dimensions for different agents in discrimination scenarios

Variable	Discrimination	Agent	M	SD
Moral response	Education	AI	4.14	1.38
	Education	Human	5.05	1.25
	Age	AI	4.39	1.39
	Age	Human	5.30	1.06
	Gender	AI	4.49	1.48
	Gender	Human	5.28	1.23
Moral cognition	Education	AI	4.73	1.27
	Education	Human	5.18	0.99
	Age	AI	4.78	1.32
	Age	Human	5.22	1.10
	Gender	AI	4.53	1.28
	Gender	Human	5.14	1.21
Moral emotion	Education	AI	4.26	1.60
	Education	Human	5.44	1.27
	Age	AI	4.44	1.70
	Age	Human	5.39	1.45
	Gender	AI	4.02	1.51
	Gender	Human	5.13	1.39
Moral behavior	Education	AI	4.08	1.66
	Education	Human	5.31	1.29
	Age	AI	4.18	1.77
	Age	Human	5.28	1.45
	Gender	AI	3.75	1.70
	Gender	Human	4.86	1.51

Figure 1. Moral Deficit Effect Across Different Moral Decision- Making Conditions.

Figure 2. The Moderating Role of Perceived Agency in the Moral Deficit Effect.

Figure 3. The Moderating Role of Perceived Experience in the Moral Deficit Effect.

Table 2 Results of Confirmatory Factor Analysis

Model	χ2	df	χ²/df	CFI	TLI	RMSEA	SRMR
One-factor model	2749.91	189	14.55	0.70	0.67	0.19	0.14
Two-factor model	1030.31	188	5.49	0.90	0.89	0.11	0.04
Five-factor model	519.87	179	2.90	0.96	0.95	0.07	0.03

Table 3 Descriptive Statistics and Correlations among Variables

Variables	M	SD	1	2	3	4
Decision-maker	0.51	0.50	1
Perceived efficacy	5.16	1.76	0.75^***	1
Perceived experience	4.95	1.93	0.80^***	0.83^***	1
Moral response	5.38	1.31	0.42^***	0.48^***	0.47^***	1

Figure 4. Parallel Mediating Effects of Perceived Agency and Perceived Experience.

Figure 5. Enhancement of Moral Responses by Different Intervention Strategies.

Figure 6. Effects of Different Intervention Strategies on Perceived Agency.

Figure 7. Enhancement of Perceived Experience by Different Intervention Strategies.

Figure 8. Paths through which the Combined Anthropomorphism and Expectation Adjustment Intervention Enhances Moral Responses to Immoral AI Decisions.

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