{"id": "urn:uuid:de9eea15-757a-4b58-8f37-68cd0c17479d", "type": ["VerifiableCredential", "OpenBadgeCredential"], "proof": {"type": "Ed25519Signature2020", "created": "2026-07-30T21:08:35Z", "proofValue": "z2wgzAC56zHV5BsPGyoaUAF9VHPH1ML4GEP71sBAaHhovFksG4tobxg2RrAiwryLthWFyhP1ebNTFAkiZZMnVP6Td", "proofPurpose": "assertionMethod", "verificationMethod": "did:key:z6MkfwJEckNVbaLwbCETHMYkpfb7g2Nnvqn78ASyrYJfqvow#z6MkfwJEckNVbaLwbCETHMYkpfb7g2Nnvqn78ASyrYJfqvow"}, "issuer": {"id": "did:key:z6MkfwJEckNVbaLwbCETHMYkpfb7g2Nnvqn78ASyrYJfqvow", "name": "MIT Learn", "type": ["Profile"], "image": {"id": "https://learn.mit.edu/images/mit-red.png", "type": "Image", "caption": "MIT Learn logo"}}, "@context": ["https://www.w3.org/ns/credentials/v2", "https://purl.imsglobal.org/spec/ob/v3p0/context-3.0.3.json", "https://w3id.org/security/suites/ed25519-2020/v1"], "validFrom": "2026-07-30T21:08:35Z", "credentialSubject": {"type": ["AchievementSubject"], "identifier": [{"salt": "not-used", "type": "IdentityObject", "hashed": false, "identityHash": "Mohammed Baba Mohammed ", "identityType": "name"}], "achievement": {"id": "https://learn.mit.edu/courses/course-v1:UAI_SOURCE+UAI.15", "name": "Explainability and Fairness", "type": ["Achievement"], "criteria": {"narrative": "- Explain why explainability matters\u00a0in AI systems and describe approaches such as global, local, and counterfactual explanations.\r\n- Differentiate between interpretable models and post-hoc explanation methods, recognizing their strengths and limitations.\r\n- Describe how symbolic AI structures knowledge\u00a0using knowledge graphs, ontologies, and logical inference.\r\n- Illustrate how compound AI systems combine components\u00a0(LLMs, neural networks, symbolic engines) with retrieval and prompting to solve complex tasks.\r\n- Compare statistical reasoning in LLMs with symbolic reasoning\u00a0(deduction, induction, abduction).\r\n- Identify common sources of bias in datasets and models\u00a0and explain formal measures such as \u03b1-bias and \u03b5-demographic parity.\r\n- Discuss fairness-aware optimization strategies\u00a0to mitigate bias while balancing equity and predictive performance."}, "description": "Mohammed Baba Mohammed  has successfully completed all modules and earned a Course Certificate in Explainability and Fairness.", "achievementType": "Course"}, "activityEndDate": "2026-07-30T21:08:35Z", "activityStartDate": "2026-07-29T17:06:02Z"}}