AI进行管理

"风险管理"指的是使用人工智能（AI）工具来评估和管理企业面临的各种风险，从而使企业能够更有效地应对潜在的挑战。首先，使用AI工具进行风险评估。利用AI工具准确预测和识别潜在的风险，如财务风险、供应链风险等。根据企业的具体需求选择适合的AI风险评估工具，这些工具可能包括数据分析软件、预测模型等。收集相关的数据，如财务报表、市场数据、供应链信息等，以供AI工具分析。利用AI工具对这些数据进行分析，预测潜在的风险并识别风险的来源和可能的影响。例如，使用AI工具分析财务数据，以预测现金流短缺的风险；或通过分析供应链数据，预测可能的供应中断。其次，基于AI分析结果，制定相应的风险应对策略。根据AI提供的风险评估结果，制定有效的风险应对和管理策略。根据AI识别的风险类型和程度，制定具体的风险应对措施。这可能包括制定应急计划、调整业务策略等。执行风险管理策略，并持续监控其效果，以确保风险得到有效控制。根据市场和业务环境的变化，不断调整风险管理策略，以应对新的风险。定期复审风险评估模型和管理策略，确保它们仍然适用于当前的业务环境。随着市场和业务条件的变化，及时更新风险评估数据和模型，确保风险管理的及时性和准确性。通过实施AI驱动的风险管理，中小企业可以更有效地识别和应对潜在的风险，从而保护企业免受不必要的损失，并确保可持续发展。这种方法不仅提高了风险管理的效率，而且提升了对复杂情况的反应能力和适应性。

Scientists may also have succeeded in using generative AI to designantibodies that bind to a human protein linked tocancer.36AI is used in the fight against the most serious and harmful crimesThe Child Images AbuseDatabase37uses the powerful data processingcapabilities of AI to identify victims and perpetrators of child sexual abuse.Thequick and effective identification of victims and perpetrators in digital abuse imagesMia mammography intelligent assessment,NHS England,2021.Robotics and Autonomous Systems for Net Zero Agriculture,Pearson et al.,2022.Artificial intelligence,big data and machine learning approaches to precision medicine and drug discovery,Current DrugTargets,2021.Unlocking de novo antibody design with generative artificial intelligence,Shanehsazzadeh et al.,2023.Pioneering new tools to be rolled out in fight against child abusers,Home Office,2019.A pro-innovation approach to AI regulationallows for real world action to remove victims from harm and ensure their abusersare held to account.The use of AI increases the scale and speed of analysis whileprotecting staff welfare by reducing their exposure to distressing content.AI increases cyber security capabilitiesCompanies providing cyber security services are increasingly using AI to analyselarge amounts of data about malware and respond to vulnerabilities in networksecurity at faster-than-humanspeeds.38As the complexity of the cyber threatlandscape evolves,the pattern-recognition and recursive learning capabilities of AIare likely to play an increasingly significant role in proactive cyber defence againstmalicious actors.1.2 Managing AI risks

embedded in the broader regulatory considerations as regulators and AI life cycle actors are expected to comply with the UK’sdata protection framework.Public expectations for AI governance(transparency,fairness and accountability),Centre for DataEthics and Innovation,2023.Principles for the security of machine learning,National Cyber Security Centre,2022.A pro-innovation approach to AI regulationguidance in a way that is coordinated and coherent with the activities of otherregulators.Regulators’ implementation of this principle may require thecorresponding AI life cycle actors to regularly test or carry out due diligenceon the functioning,resilience and security of asystem.93Regulators mayalso need to consider technical standards addressing safety,robustness andsecurity to benchmark the safe and robust performance of AI systems and toprovide AI life cycle actors with guidance for implementing this principle intheir remit.Principle Appropriate transparency and explainabilityDefinitionandexplanationAI systems should be appropriately transparent and explainable.Transparency refers to the communication of appropriate information aboutan AI system to relevant people(for example,information on how,when,andfor which purposes an AI system is being used).Explainability refers to theextent to which it is possible for relevant parties to access,interpret andunderstand the decision-making processes of an AIsystem.94An appropriate level of transparency and explainability will mean thatregulators have sufficient information about AI systems and their associatedinputs and outputs to give meaningful effect to the other principles(e.g.toidentify accountability).An appropriate degree of transparency andexplainability should be proportionate to the risk(s)presented by an AIsystem.Regulators may need to look for ways to support and encourage relevant lifecycle actors to implement appropriate transparency measures,for example