Understanding role of Ethics in managing AI in modern business#400724

• Analyse the potential ethical challenges associated with AI development and implementation in various business contexts.
• Identify potential biases within AI algorithms and data, and explore strategies for mitigating those biases to ensure fairness and inclusivity.
• Discuss the ethical implications of AI on privacy, security, and human rights in the business environment.
• Develop a framework for responsible AI governance within their organizations, considering factors like transparency, accountability, and human oversight.
• Learn how future studies methodologies can be applied to anticipate potential ethical risks and opportunities related to AI in the future.
• Communicate effectively about ethical considerations in AI to technical and non-technical stakeholders within the organization.
• Advocate for and implement ethical AI practices within their teams and projects.
• Develop a personalized action plan outlining steps to integrate responsible AI principles into their business practices.

• Business leaders, managers, and project managers responsible for overseeing AI initiatives.
• AI developers, data scientists, and analysts involved in building and deploying AI solutions.
• Compliance officers and risk management professionals concerned with ethical implications of AI.
• Marketing, sales, and customer service personnel working with AI-powered tools.
• Anyone interested in understanding the ethical landscape of AI in modern business.

• Pre-assessment
• Live group instruction
• Use of real-world examples, case studies and exercises
• Interactive participation and discussion
• Power point presentation, LCD and flip chart
• Group activities and tests
• Each participant receives a binder containing a copy of the presentation
• slides and handouts
• Post-assessment

Day 1: The Ethics of AI in Business
• Welcome and program overview.
• AI & Business: A Powerful Combination: Exploring the transformative potential of AI across various industries, highlighting its benefits and potential risks.
• The Ethics Landscape of AI: Introducing key ethical considerations related to AI, such as bias, fairness, transparency, and accountability.
• Real-World Examples: analysing case studies of AI implementations that have raised ethical concerns, examined potential harms and missed opportunities.
• Guest Speaker: An ethicist, legal expert, or AI researcher specializing in ethical dilemmas can be invited to share their insights and answer participant questions.
Day 2: Bias in AI & Algorithmic Fairness
• Understanding Algorithmic Bias: Delving deeper into the concept of bias in AI, exploring how biases can creep into algorithms through data selection, training processes, and design choices.
• The Impact of Bias: Discussing the potential consequences of biased AI systems, such as discrimination in hiring practices, loan approvals, or customer service interactions.
• Strategies for Mitigating Bias: Learning techniques for identifying and mitigating bias in AI development, including data cleaning, fairness metrics, and algorithmic auditing.
• Case Study: analysing a real-world example of an organization that successfully addressed bias within their AI system, discussing the strategies implemented.
• Group Discussion: Participants engage in a facilitated discussion to explore potential biases within AI applications relevant to their specific industries.

Day 3: Privacy, Security, and AI
• Privacy Concerns in the Age of AI: Discussing the potential threats to privacy posed by AI systems, such as data collection, surveillance, and profiling.
• Data Security & AI Development: Exploring the importance of data security practices in AI development to protect sensitive information and prevent misuse.
• The Right to Explanation: Learning about the concept of "explainable AI" and its importance in ensuring transparency and accountability in AI decision-making processes.
• Future Studies for Ethical AI: Learning how to incorporate future studies methodologies like scenario planning to anticipate potential privacy and security risks related to AI advancements.
• Guest Speaker: A privacy expert or data security professional can be invited to share their insights and best practices for ethical data management in AI projects.
Day 4: Building a Responsible AI Framework
• Developing an AI Governance Framework: Creating a framework for ethical AI governance within your organization, considering factors like risk assessment, human oversight, and employee training.
• Embedding Ethics into the AI Development Lifecycle: Learning how to integrate ethical considerations into every stage of the AI development process, from initial concept to deployment and monitoring.
• The Role of Leadership in Ethical AI: Discussing the responsibilities of leadership in promoting ethical AI practices and fostering a culture of transparency within the organization.
• Case Study: analysing a real-world example of an organization with a strong track record of ethical AI development, exploring their governance framework and best practices.
• Interactive Simulation: Participants engage in a facilitated simulation where they take on different roles within an organization and navigate an ethical dilemma related to AI implementation.

Day 5: The Future of Work & Responsible AI
• The Impact of AI on Jobs and the Future of Work: Exploring the potential impact of AI on employment, focusing on strategies for human-AI collaboration and responsible workforce development.
• The Future of AI Regulation: Discussing the evolving regulatory landscape surrounding AI and potential future regulations that may impact businesses.
• Advocating for Ethical AI: Learning strategies for effectively communicating the importance of ethical AI to stakeholders within the organization and beyond.
• Building Your Action Plan for Ethical AI: Developing a personalized action plan outlining steps to integrate responsible AI principles into their business practices. This may include:
o Identifying areas within their work where ethical considerations related to AI are most relevant.
o Researching and proposing best practices for ethical AI development and deployment within their teams.
o Advocating for leadership support and resources for building a culture of responsible AI within the organization.
• Course Wrap-Up & Ongoing Learning: Reviewing key takeaways from the program, addressing any remaining questions, and discussing ongoing learning resources for staying informed about ethical considerations in AI and best practices for responsible development and implementation.
• Networking & Knowledge Sharing: Participants engage in a facilitated discussion to share their action plans and best practices for promoting ethical AI within their businesses.