How to navigate the the EU and other AI Act’s requirements and strategically cut compliance costs. 

• Key assumptions and provide clear methods to align your company with EU regulations.
• The common challenges companies often encounter when attempting to comply with new AI regulations
• Practical guidance on how companies typically adopt a structured approach to AI complianceand other related regulations.
• How companies can enhance their chances of successfully complying with AI regulations and minimise non-compliance risks.

The AI Act introduces new rules on AI development and use;

• Foundations of AI: Introduction to machine learning, deep learning, neural networks, and key AI concepts.
• Data Governance for AI: Implement best practices for data quality, compliance, and governance in AI initiatives.
• Compliance with the Act will require a major effort, in our view; regulatory complexity and litigation risks are key concerns
• Idenyify the implications for the tech, auto and healthcare sectors
• Early analysis of the Act’s impact on firms will allow business to better understand potential risks and opportunities

Regulating AI in the corporate world

• The new AI rules aim to mitigate potential adverse impacts of AI and support the industry by offering regulatory sandboxes for AI testing
• Regulatory uncertainty will remain a challenge for firms until the codes of practice and changes to sectoral legislation are implemented
• Management should be aware of the Act’s rules that may affect their own operations as well as portfolio companies
• Practical AI Applications: Differentiate types of machine learning algorithms and apply them to real-world business scenarios.

A primary AI Act criticism is its complexity and lack of clarity

• Identify and clarify the definitions of ‘AI systems’ that lead to uncertainties about the corporate AI scope and application
• Identify the areas of risks and concerns that could impede effective AI implementation and compliance.
• Business Integration: Identify AI opportunities tailored to your organisation, including customer journey optimisation.

The AI rules on synthetic content labelling, watermarking, transparency and clear and distinguishable disclosures

• What are the most challenging AI areas for firms to comply with including addressing the risk of malicious content creation and production and dissemination of misinformation
• AI-generated misinformation and disinformation and the key risks that may cause a material crisis
• Responsibilities of the deployers of AI systems, which generate or manipulate audio, image or video content that has been artificially generated or manipulated.
• Interacting with AI systems and transparency in regulatory compliance for building trust in the technology and mitigating risks.

 Key risk issues that the AI Act seeks to address

• Risk and Compliance Management: Assess and mitigate AI risks, impacts, and controls across the AI lifecycle. Comply with frameworks like ISO 42001, and responsible AI principles.
• Disinformation and misinformation
• Exacerbation of bias, discrimination and fairness
• Threats to cybersecurity
• Risks to human safety and data privacy
• Facilitating industry development and innovation
• Ensure proper vetting and integration of third-party AI systems and software.

Systemic, computational, trechnical, and human cognitive biases in decision-making processes across the AI lifecycle.

• Technical documentation and record-keeping
• Data and data governance
• Human oversight and AI literacy
• Increased awareness on sustainability and AI energy consumption

 Testing standards for AI 

• Implementing and executing regulatory focus on GPAI model testing for identifying and mitigating systemic risks
• GPAI models with system risk to perform model evaluation, including conducting and documenting adversarial testing

Implement AI-based software, risk, impact and compliance assessments

• AI-Powered Risk Assessment: Leverage AI to identify and prioritize potential risks, enabling proactive risk management and mitigation strategies.
• Impact Analysis: Utilize AI to assess the potential impact of AI-driven decisions on various stakeholders, including customers, employees, and society.
• Compliance Assurance: Employ AI to monitor compliance with relevant regulations and industry standards, ensuring ethical and legal AI practices.

Tea/Coffee Break

Drive AI transformation with use cases and good governance policies

Identification of AI risks, compliance, transparency, accountability and performance management

• Risk Identification and Mitigation: Proactively identify potential risks associated with AI systems, such as bias, security breaches, and unintended consequences, and implement strategies to mitigate them.
• Compliance and Ethical AI: Ensure adherence to relevant regulations and ethical guidelines, promoting fairness, transparency, and accountability in AI development and deployment.
• Performance Monitoring and Optimization: Continuously monitor AI systems’ performance, identify areas for improvement, and optimize models to achieve desired outcomes.
• Strategic Planning: Develop business cases, allocate resources, and define actionable steps for adopting AI.