In today’s digital society, algorithms are deeply embedded in nearly every aspect of daily life—from social media recommendations and credit approvals to hiring filters and judicial sentencing assistance. However, when algorithms prioritize only efficiency, speed, or profit maximization, they often overlook the protection of individual rights. For instance, some hiring algorithms perpetuate gender or racial bias due to skewed training data, while opaque credit-scoring systems may deny financial opportunities to vulnerable groups. Therefore, we urgently need to reorient algorithmic design toward ethical values: efficiency matters, but not at the expense of fairness, privacy, the right to information, or the right to appeal.The principle that ‘algorithms must account for rights, not just efficiency’ calls for integrating ethical review, diverse stakeholder input, and accountability mechanisms into algorithm development and deployment. This includes ensuring data fairness, model interpretability, and users’ rights to challenge and correct algorithmic decisions. Regulatory frameworks like the EU’s Artificial Intelligence Act and China’s Interim Measures for the Management of Generative AI Services reflect this growing consensus. Only by placing fundamental human rights at the core of algorithmic logic can technology truly serve societal well-being rather than deepen inequality or undermine democratic foundations.
在当今数字化社会，算法已深度嵌入人们生活的方方面面——从社交媒体推荐、信贷审批到招聘筛选、司法量刑辅助。然而，当算法仅以效率、速度或利润最大化为目标时，往往忽视了对个体权利的尊重与保障。例如，某些招聘算法因训练数据偏见而歧视特定性别或种族；信用评分系统可能因缺乏透明度而剥夺弱势群体的金融机会。因此，我们亟需重新思考算法设计的价值导向：效率固然重要，但不能以牺牲公平、隐私、知情权和申诉权为代价。“算法不能只算效率，更要算‘权利’”，意味着在算法开发与部署过程中，必须引入伦理审查、多元参与和问责机制。这包括确保数据来源的公正性、模型决策的可解释性，以及用户对算法结果提出异议和修正的权利。欧盟《人工智能法案》、中国《生成式人工智能服务管理暂行办法》等法规的出台，正是对这一理念的制度回应。唯有将人的基本权利置于算法逻辑的核心，技术才能真正服务于社会福祉，而非加剧不平等或侵蚀民主根基。
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