What are AI Ethics, in one article?

Definition of Artificial Intelligence Ethics

Artificial Intelligence Ethics (AI Ethics) is a cross-disciplinary field that studies the moral principles, values and social responsibilities that should be followed in the development, deployment and use of AI systems. AI Ethics tries to answer the fundamental question of "how should we design, use and govern AI", covering not only the basic categories of traditional ethics, such as justice, rights, well-being and virtue, but also proposing new norms and governance frameworks in response to the characteristics of the new technology, such as algorithmic decision-making, data-driven and automated actions. The ethics of AI is not only concerned with the inherent risks of the technology itself, but also with the distribution of power, resources, cultural impacts and global governance issues arising from the embedding of the technology in the social system, with the goal of promoting innovation while minimizing the damage to personal dignity, social justice and the ecological environment, as well as ensuring that technological development enhances the well-being of human beings as a whole. The field brings together multidisciplinary perspectives from philosophy, law, computer science, sociology, economics, psychology and other disciplines to form a dynamic, open, and cross-cultural ethical governance system through principle formulation, standard design, institutional innovation and public participation, in order to address imminent challenges such as algorithmic bias, privacy leakage, automated unemployment, autonomous weapons, and information manipulation. In short, AI ethics is the sum of knowledge and practice about "making intelligence good".

Technological Safety in Artificial Intelligence Ethics

• Verifiable and testable: Establish a multi-level verification system such as formalized verification, simulation testing, and red-team exercises to ensure that critical safety attributes are fully tested before deployment.
• Security Vulnerability Management: Setting up a vulnerability disclosure reward mechanism, rapid response patch process, and sharing threat intelligence with the cybersecurity community to reduce the risk of malicious exploitation.
• Collaborative human-machine monitoring: retaining the right of final human decision-making in high-risk scenarios such as autonomous driving and medical diagnosis, and designing real-time interpretable interfaces to facilitate timely operator intervention.
• Catastrophic risk prevention: For systems with self-improvement or recursive optimization capabilities, set capacity thresholds, fusible switches and external audits to prevent runaway cascading effects.

Algorithmic Bias in Artificial Intelligence Ethics

• Data representativeness: the training data need to cover multiple dimensions of the target population such as gender, age, race, geography, etc., and mitigate the sample bias by resampling and synthesizing the data.
• Transparency in feature selection: direct use of sensitive attributes as input features is prohibited and causality tests are performed on proxy variables to prevent indirect discrimination transmission.
• Fairness metrics: Introduce multiple indicators of equal opportunity, equal outcome, and equal calibration, weighing them among different stakeholders to avoid single indicators masking localized injustices.
• Continuous monitoring and retraining: Regularly backtrack the decision results after deployment, update the model in time when deviations are found, and record the version changes to ensure the responsibility is traceable.
• Stakeholder Engagement: Bringing together algorithm-affected community representatives, advocacy organizations, and policymakers to participate in bias audits and improvement programming to enhance governance legitimacy.

Privacy Protection for Artificial Intelligence Ethics

• (c) Data minimization: collecting only the data necessary to accomplish a specific task and avoiding the "collect first, use later" model of over-grabbing.
• Differential privacy: injecting controlled noise into statistical releases or model training makes individual information difficult to be inferred in reverse, balancing data utility and privacy guarantees.
• Federated Learning and Homomorphic Encryption: Keeping data "local" for model training or computation reduces the leakage surface caused by centralized storage.
• Informed user consent: Informs in plain language about the purpose of the data, the duration of storage, the scope of third-party sharing, and provides a mechanism for consent that can be withdrawn at any time.
• Privacy Impact Assessment: Conduct systematic assessments early in product design to identify high-risk scenarios and develop mitigation measures to form a closed-loop improvement process.

Transparency and Interpretability in Artificial Intelligence Ethics

• Globally Interpretable: disclosure of model structure, training data sources, objective functions and constraints to regulators and the public to facilitate external audits.
• Locally interpretable: provide comparative examples, feature importance rankings, or natural language explanations for individual decisions to help affected individuals understand the reasons for the results.
• Interactive Explanation: Allows users to pursue further details through Q&A and visualization operations, strengthening human-machine trust and error correction.
• Interpretation fidelity: Ensure that the content of the interpretation is consistent with the internal logic of the model, and avoid misleading users with "superficial stories".
• Interpretation accessibility: Design multimodal interpretation interfaces for audiences of different cultures and educational backgrounds to lower the threshold of understanding.

Attribution of Responsibility for Artificial Intelligence Ethics

• Chain of Responsibility: Clarify the obligations and responsibilities of developers, deployers, operators and end-users in different links to avoid a "responsibility vacuum".
• Insurance and compensation mechanisms: Establish mandatory algorithmic liability insurance to ensure that victims receive prompt compensation and to motivate businesses to proactively reduce risk.
• Legal Personality Discussion: Exploring whether to create limited legal personality for highly autonomous systems to allow for direct accountability in infringement scenarios.
• Incident investigation standards: Develop an interdisciplinary incident investigation process that includes steps such as log sealing, third-party forensics, and algorithmic reproduction to ensure objective conclusions.
• (c) Public monitoring platforms: setting up independent organizations or open platforms to receive public complaints, publish a database of liability cases, and create social monitoring pressure.

Workforce Implications of Artificial Intelligence Ethics

• Job Replacement Assessment: Quantifying the scale and pace of automation's impact on employment in various industries and skill levels through macro-simulation and micro-enterprise research.
• Skills retraining: The Government, enterprises and trade unions have cooperated to establish lifelong learning accounts and provide digital skills courses and career transition guidance for the replaced population.
• (c) Social security bottoming out: exploring new redistributive mechanisms such as unconditional basic income and algorithmic dividend-sharing tax to mitigate short-term income shocks.
• New Career Creation: Encourage the cultivation of new employment forms around AI training, maintenance, ethical auditing, experience design and other areas to create a positive cycle.
• Labor Standards Update: Revisiting labor regulations on hours, safety, and privacy to ensure that AI-assisted workers' rights in the platform economy are not eroded.

Environmental Sustainability of Artificial Intelligence Ethics

• Energy-efficient algorithms: optimize model structure and training strategies to reduce floating-point operations and GPU energy consumption, e.g., using techniques such as sparsification, quantization, and knowledge distillation.
• Green Data Center: Reduce PUE (Power Usage Effectiveness) to less than 1.1 using renewable energy, liquid cooling systems and dynamic load scheduling.
• Life Cycle Assessment: Calculate the carbon footprint of the whole process from chip manufacturing, equipment transportation, operation and maintenance to end-of-life recycling, and report publicly.
• Policy incentives: Encourage enterprises to prioritize low-energy AI options through carbon tax exemptions and green procurement lists.
• Environmental justice: avoid shifting energy-intensive training tasks to areas with weak environmental regulation and prevent externalization of pollution and resource consumption.

International Governance of Artificial Intelligence Ethics

• Multilateral frameworks: Support international organizations such as the UN, OECD, GPAI and others to develop inclusive ethical guidelines and standards for AI.
• Cross-border data flows: Bilateral or multilateral agreements on topics such as privacy protection, mutual assistance in law enforcement, and tax allocation to prevent data silos and regulatory arbitrage.
• Technology export controls: Establish a list and licensing system for highly sensitive AI technologies to prevent the proliferation of military misuse and human rights abusive applications.
• North-South cooperation: help developing countries build indigenous AI ethical review capacity and digital infrastructure through the transfer of funds, technology and talent.
• Global public goods: Promote the construction of public goods such as open-source models, open datasets, and shared arithmetic platforms to reduce the inequality brought about by technological monopolies.

Cultural Diversity in Artificial Intelligence Ethics

• Value-sensitive design: Incorporating different cultures' ethical languages and symbol systems in the needs analysis phase to avoid the dominance of a single Western ethical perspective.
• Localized datasets: collect and respect text, image, and speech data in native contexts to reduce misidentification or offense due to cultural differences.
• Linguistic equity: Ensure that minority languages enjoy the same level of accuracy and service in systems such as speech recognition and machine translation, and prevent digital linguistic genocide.
• Religious and customary respect: avoid violating religious dress, ritual and privacy traditions in applications such as facial recognition and behavior prediction.
• Multi-participation mechanism: Establish regional ethics committees and invite Aboriginal people, minority communities, religious leaders, etc. to participate in standard-setting and impact assessment.