处理大数据需注意的七个要点

良马还与世界不同角落的其他律师事务所合作，通过指导中国实体如何按照法律法规在海外司法管辖区投资，协助中国实体与世界建立联系。
随着技术本身日益复杂化，技术开发人员的商业性增强和业务需求意识提升，再加上企业对探索新技术的积极性上涨，所有行业似乎都基于使用人工智能系统下进行着机器学习。伴着人工智能/机器学习前所未有接纳速度和使用规模，人工智能/机器学习理所当然地给技术承包商及其现有的合同模式，应对技术所承载风险同样快节奏的变化，这些变化应在相关协议中得到满足。
With the increasing sophistication of the technology itself, but also as well as greater commerciality and business needs-awareness of the developers of the technology, when combined with organisations' increasing openness to exploring these new technologies, the use of artificial intelligence-based systems ("AI"), machine learning (as a sub-category of AI) ("ML")
across all industries looks here to stay. With the unprecedented speed and scale of the adoption of AI/ML, it is unsurprising that it places a strain on the ability of technology contractors, and their existing contracting models, to address the equally fast-paced changes in the risks that those technologies carry and that should be catered for in the relevant agreements.
NO1 注意熟悉技术
Diligence and familiarity with the technology
就人工智能/机器学习服务提供商而言，由于需方可能对支持其所采购解决方案的复杂软件、算法和技术了解不足，因此需方尽可能地深入了解服务的技术方面极其重要，以便知悉业务潜在风险以及能满足业务需求的具体解决方案。服务提供商的透明度应该是进入任何涉及复杂技术解决方案和不平衡技术知识关系的关键先决条件。如果人工智能/机器学习服务提供商能够清晰地解释其产品的设计、流程和功能，其将取得客户更多的信任。
In the case of an AI/ML service provider, as you are likely coming to the table with far lesser understanding of the sophisticated software, algorithms and techniques underpinning the solution being procured, it is important to get as much of an insight into the technical aspects of the service as possible to appreciate both where the risks to your business may arise and the specific solutions it can offer to your business needs. Transparency from a service provider should be a key prerequisite to entry into any relationship involving complex technology solutions and imbalanced technical knowledge. If an AI/ML service provider can clearly explain its product's design, process and functioning, it will garner greater trust from its customers.
NO2 服务和服务标准
Services and service standard
如果设计人工智能/机器学习是为了带来特定的结果或改进（如增加收入、提高客户参与度等），那么应列出可衡量指标和具体结果，以及未达标的相应后果。尽管服务本身变得复杂，但人工智能/机器学习的服务提供商应该明确关键绩效指标。为保证企业的道德原则和监管要求（包括监管人工智能即将实行的法律法规）得到实现，审计权和控制权至关重要。
If the AI/ML is designed to bring about a particular result or particular improvement (such as increased revenues, improved customer engagement, etc.), then measurable indicators and concrete outcomes should be set out, with, if desired, consequences for failure to reach agreed standards. AI/ML service providers should use KPIs notwithstanding the heightened complexity of the service itself. Audit rights and controls are vital to ensure the business's ethical principles and regulatory requirements (including any impending legal controls on AI) are being met.
NO3 人工智能服务的性质
Nature of the AI service
在看似难懂和复杂的新技术面前，人工智能/机器学习是技术承包商创建的不应被轻易放弃的软件和现有原则，例如基于云提供应该是SaaS合同的相关原则。如果访问产品和使用产品需要许可，那么将适用支持和维护的要求。
AI/ML is software and existing principles established by technology contractors should not simply be abandoned in the face of seemingly opaque and complex new technologies, such as a cloud-based provided should be relevant principles of SaaS contracts. If licences are required to access and use the product, then support and maintenance requirements will apply.
NO4 责任
Liability
由于缺乏关于当事人之间责任分配的明确立法，哪一方承担人工智能/机器学习服务的作为或不作为法律责任将由双方之间正式合同来确定。在某种程度上，这符合合同中责任分配的一般原则（简而言之，双方都希望尽可能地将潜在责任转移给对方），双方的谈判实力在此发挥着作用。然而，客户人工智能/机器学习解决方案的关注度，及其能力和局限性将再次变得重要。从客户的角度来看，服务提供商不应试图排除对其实际控制事项的全部或部分责任，因此应该承担相应责任。
Given that there is no clear legislative provision on the allocation of liability between parties, it is up to the formal contract between the parties to determine who would be legally responsible for acts or omissions of the AI/ML service. To an extent this tracks with common principles of liability allocation in contracts (in short, both parties looking to transfer as much potential liability to the other side as possible), with each party's relative negotiating strength playing a role here. However, once again the customer's diligence of the AI/ML solution, and its abilities and limitations, will be important. From the customer's perspective, the service provider should not seek to exclude liability for matters which are actually in the service provider's control, either in whole or in part, and for which it should therefore be liable.
NO5 知识产权归属
IP Ownership
围绕谁拥有知识产权存在一个更基本问题，该知识产权实际上是由人工智能/机器学习系统本身创建的，无需人工参与，例如客户在平台通过机器学习开发的算法来使用底层平台。目前的法律没有充分规定进入和离开人工智能/机器学习系统的知识产权归属。法律应该根据双方提供的内容以及创建的内容来规定“谁拥有什么”。从客户的角度来看，服务生成的数据/输出将由该客户拥有。
There is a more fundamental question around who owns IP which is effectively created by the AI/ML system itself, without human involvement, such as where an underlying platform is utilised by a customer by way of algorithms developed by the platform by way of ML. Current legal principles do not adequately provide the ownership of intellectual property into and out of AI/ML systems. Provision should be made for 'who owns what' in terms of what is provided by both parties, and what is created. From the customer's perspective, the data/outputs generated by the service will be something that should be owned by that customer.
NO6 数据保护
Data Protection
从客户自身角度来看，数据控制者将对数据保护法规定的相关义务承担全部责任。如果个人数据很可能构成数据输入和输出的一部分，那么数据保护法律（如欧盟的《通用数据保护条例》和中国的《数据安全法》）将需要构成尽职调查和合同签订过程的一部分。
From the customer's own perspective, the data controller will bear sole responsibility for its compliance with its obligations under data protection law. If it is the case that personal data is likely to form part of data inputs and outputs, then the law on Data Protection, like GDPR in EU and data security law in China will need to form part of the diligence and contracting processes.
向客户明确解释如何处理其数据、为何处理其数据以及遵守数据安全法律规定都至关重要。在处理数据过程中，有效的法律依据是明显要求，包括为日后使用的在人工智能/机器学习服务中产生的数据。
Clear explanation to customers how and why their data are being processed is vital, as is complying with the provisions of data security law. An effective legal basis for the processing is an obvious requirement, including for any future uses for data arising from the AI/ML's services.
NO7 监管要求
Regulatory Requirements
如果客户受到特定法规要求（例如CBI法规或医疗技术领域运营等）的约束，那么确保在客户使用人工智能/机器学习服务整个过程中保持遵守这些法规要求至关重要。此时客户不仅应该考虑法规遵从性制度如何受到新技术的影响，而且还应该考虑是否仍然在该客户的控制范围内以实现遵从性，或者是否依赖于服务提供商来实现/维持该遵从性。
If the customer is subject to particular regulatory requirements (such as CBI regulation, operating in the medical technology field, etc.), then ensuring ongoing compliance with those requirements throughout that customer's use of the AI/ML service is crucial. Such customers should be thinking not only about how their compliance regimes may be affected by the new technology, but also whether or not it remains within that customer's control to achieve compliance or if, alternatively, it is reliant on the service provider to achieve/maintain that compliance.
NO8 结束语
Concluding Remarks
总会其他既有一般的（如法律的选择，潜在的默示保证和其他条款，即将出台的法律等），也有针对特定情况的。从合同的角度来看，人工智能/机器学习对传统IT的签约模式有着“颠覆性/破坏性”的影响，也就是说在全面法律规定的尽职调查下进入早期和进行中的约定。
There will always be others, both general (such as choice of law, potentially implied warranties and other terms, future impending laws, etc.) and circumstance-specific. From a contract perspective, AI/ML certainly has been 'disruptive' in its effects on traditional IT contracting models, making early and ongoing engagement alongside careful diligence combined with comprehensive legal provisions.