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IBM

Oversight, Accountability, Trust, and Responsible AI

IBM via Coursera

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Overview

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Governance documents that gather dust do not protect teams. Most oversight schemes fail not at design time but at adoption, when review queues go unread and accountability matrices are never opened. This course, Oversight, Accountability, Trust, and Responsible AI, builds the governance backbone that keeps agentic teamwork safe, accountable, trusted, and welcomed by the broader organization. You will build accountability structures that work when both humans and agents act, embed oversight into recurring team operating rhythms so it becomes routine practice, and apply trust, transparency, and explainability practices anchored on the IBM AI Ethics and Trust Framework. You will also address ethical risks specific to agentic teams, including bias, manipulation, and dignity-of-work concerns, and survey legal, regulatory, and workforce considerations. By course completion, you will have produced a Governance and Responsible Agentic AI Package with an accountability matrix, operating-rhythm design, trust plan, responsible-AI operating practice, and an external communication statement.

Syllabus

  • Accountability Structures and Auditability
    • In this opening module of the governance phase, you will tackle the question every leader eventually asks: when something goes wrong on an agentic team, who is accountable? You will apply an accountability framework adapted for hybrid work, make AI agent actions auditable through configuration and logging choices in IBM watsonx Orchestrate, document responsibility so it holds up under stakeholder scrutiny, and recognize the patterns that diffuse accountability. The labs have you author an accountability matrix and auditability plan for a multi-agent workflow.
  • Embedding Oversight into Team Operating Rhythms
    • In this module, you will turn oversight and accountability from documents into daily practice. Most oversight schemes do not fail at design time; they fail at adoption, when review queues go unread and the accountability matrix is never opened. You will map oversight and accountability touchpoints onto a realistic team operating rhythm, design decision-log practices that capture what oversight produced without becoming bureaucratic, build escalation rituals that surface failures early, and adjust the rhythm as the team matures. The labs have you design a complete operating rhythm for an agentic team
  • Trust, Transparency, Explainability, and Ethical, Legal, and Organizational Considerations
    • In this module, you will bring the trust and ethics dimensions of agentic teams together into one substantial body of practice. You will treat trust, transparency, and explainability as practical disciplines rather than slogans, choose transparency defaults and plan how the team responds to explainability requests, and survey the wider considerations a leader cannot ignore: bias and fairness, legal and regulatory exposure, labor and workforce implications, and the organizational signals that a deployment is straying off course. The module closes by surfacing concerns constructively rather than letting them fester. The culminating lab produces a combined trust-and-considerations package.
  • Operationalizing Responsible Agentic AI
    • In this module, you will bring the IBM AI Ethics and Trust Framework into daily operation. Most teams produce a thoughtful responsible-AI document and then watch it gather dust. You will learn the practices that keep responsible-AI commitments alive: team rituals that surface ethical questions early, refreshed framework reviews as the team and technology evolve, edge-case handling when something genuinely goes wrong, and how the team communicates its responsible-AI posture externally without overpromising. The labs have you turn each framework principle into a specific ritual, decision rule, or recurring conversation.
  • Cumulative Project and Course Wrap-Up
    • In this final module, you will assemble everything from Modules 1 through 4 into a cumulative project: a Governance and Responsible Agentic AI Package. The deliverable includes an accountability matrix and auditability plan, an operating-rhythm design with oversight touchpoints, a trust, transparency, and explainability plan with an open-concerns brief, and a responsible-AI operating practice with an external statement and edge-case response template. The module closes with a wrap-up featuring an SME interview, a day-in-the-life view of a Head of AI Governance, and a bridge into the performance and capstone work of Course 5.

Taught by

Starweaver

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