Generative AI- Workplace: Legal, Ethical, Responsible AI Use

Explore how automation uses AI to handle repetitive tasks with greater speed and accuracy, including RPA, invoice processing, payroll, and chatbots, while highlighting limitations and human oversight per GDPR.

Analyze large data volumes to uncover insights, trends, and anomalies with AI analytics. Apply analytics-based AI to sales forecasts, demand planning, healthcare diagnostics, and marketing insights, with human oversight needed.

Explore generative AI, the technology that creates images, video, and code, with examples like ChatGPT and Copilot, and its use in reports, contracts, logos, and documentation, with human review.

Explore decision support by detailing data collection and integration, data analysis and modeling, and reasoning and recommendation, with examples like pattern recognition and outcome prediction.

Explore global AI regulations shaping workplace use, including GDPR data protection, intellectual property, Equality Act 2010 and other human rights laws, plus consumer and competition laws.

Explain why AI compliance matters, including data protection and GDPR relevance. Learn to stay updated on laws, handle subject access requests, and implement AI policies.

Learn how workplace ai policies protect data, reduce misuse, and define what can or cannot go into an ai system. Discuss personal data, special category data, and clear usage examples.

Outline the purpose, scope, and acceptable versus unacceptable AI use in organizations, covering data privacy, ownership, disclosure, and consequences for misuse to guide responsible workplace AI policies.

Explore the challenges of shadow it with ai-enabled devices and unmanaged personal devices, and the regulatory implications under gdpr for data location and subject access.

Explore how AI can enhance call centers through chatbots, KYC and AML checks, and letter writing templates, while emphasizing data privacy, human oversight, and transparent customer interactions.

Explore how AI can assist diagnosis from x ray images and monitor patients in hospitals. Maintain human oversight, protect health data under GDPR, and ensure ring-fenced systems with DPIA approvals.

Explore how AI enhances recruitment with screening, interview scheduling, automated communications, and ad placement, while addressing GDPR transparency and the need for human review of automated decisions.

Explore governance and accountability in artificial intelligence, including human in command, human in the loop, accountability and ownership, and algorithmic impact assessment, with regulator expectations.

Explore transparency and trust in ai by distinguishing interpretability from explainability, and applying transparency by design, ai disclosure, data provenance, and data minimization to ensure compliant, accountable ai.

Assess the risk of artificial intelligence by answering five key questions—what it does, who is affected, and severity—then apply controls like training, audits, and human oversight.

Identify stakeholders in AI risk assessment, including employees, job applicants, and customers, whose data or decisions affect AI processes. Distinguish internal tools from external ones, noting GDPR and security implications.

Explore six AI risk categories: legal and compliance, ethical and fairness, data and privacy, operational, reputational, and security/IP risk, and learn to identify issues from GDPR violations to model drift.

Assess residual risks after security controls to decide acceptability, executive approval, or whether to restrict or stop the artificial intelligence. Outcomes: approved, approved with restrictions, or not approved under dpia.

Maintain ongoing monitoring of AI risks, log issues via the IT service desk, reassess with a DPIA, and track model drift as laws and GDPR obligations evolve across borders.

Explore key ethical principles for AI in the workplace, including transparency and explainability, GDPR rights, fairness, accountability, human oversight, privacy and data governance, and safety and reliability.

Explore fairness and bias mitigation in AI, preventing discrimination by age, disability, or race. Learn data-level rebalancing, in-processing, and calibration techniques for equitable predictions.

Explore transparency and explainability in AI, detailing data collection and use, AI processes and limitations, training data, evaluation, and algorithms, and healthcare as an assistant.

Explore practical applications of artificial intelligence across customer service, healthcare support, fraud detection, content recommendations, cybersecurity, and real-time language translation, while noting ethical and legal considerations.