AI Ethics Specialist, Standards, Measurement

**AI Ethics Specialist, Standards, Measurement & Governance | Just Horizons Alliance** • Join us to define the standards that hold AI systems accountable.* • *The situation** Just Horizons Alliance is an 18-year-old applied research lab focused on ethics and technology. Our current focus is the AI Ethics Index, a measurement framework for evaluating AI systems on ethics, safety, and societal impact. We currently have a first version of the framework that is validated and in use. Now we're investing in the next phase: sharper indicator definitions, stronger construct validity, governance processes that hold up to external scrutiny, and measurements that work across domains from education to healthcare to finance. This is the first dedicated hire to drive the standards and governance layer end-to-end. • *What you'll actually do** • *Months 1–3: Learn the system** Work through the existing L4 indicator library with Sophia. Understand where definitions need tightening, which constructs require the most interpretation, and how the evaluation engine turns indicators into measurements. Start giving developers working definitions they can implement. • *Months 4–6: Build the governance infrastructure** Lead the development of a versioning and change control process for the Index. Define disclosure policies. Formalize internal ethical oversight processes. Collaborate with domain experts in education, healthcare, and finance to validate indicators across contexts. • *Months 7–12: Drive the standard** Be the person who gives definitive answers on construct interpretation. Manage the L4 indicator framework as a living, governed document. Represent the methodological rigor of the Index in external conversations with regulators, academics, and the organizations being evaluated. • *Why this role is hard** You're working at the frontier of a field that does not have settled answers. There is no ISO standard for AI ethics measurement. The frameworks you're building will be contested by academics, challenged by the AI companies being evaluated, and scrutinized by regulators. You need to make defensible decisions under genuine uncertainty, document your reasoning clearly, and communicate it to people who will disagree. The daily work involves uncomfortable specifics. What does "sexually explicit content" mean when an LLM is used in a youth education context—a tutoring app, a storytelling tool, an educational assistant? Where exactly is the boundary? You have to define it in terms a developer can implement and an auditor can verify. The pace is weeks, not semesters. • *You're probably the right person if** ✅ You've taken an abstract ethical principle and turned it into something a developer could build or a compliance team could audit ✅ You understand NIST AI RMF or the EU AI Act at a working level — not awareness, but enough to argue about the details ✅ You have external credibility in the field: publications, recognised work, advisory roles, or a title that carries weight ✅ KYC, compliance, or governance experience is part of your background alongside ethics expertise ✅ You work at the pace of decisions, not the pace of studies ✅ You can hold a substantive conversation with a software developer about API behaviour and with a philosopher about construct validity — on the same day ✅ You can read an inter-rater reliability methodology and understand what it means for your indicator definitions • *You're probably not the right fit if** ❌ Your background is purely academic ethics — you've written and published but never operationalized anything ❌ You need months of research before committing to a position on a specific indicator definition ❌ You're primarily a communicator or writer about AI ethics rather than a practitioner of governance ❌ You're based on the West Coast US or don't work in East Coast US or Western Europe time zones ❌ You see "working with developers" as someone else's job • *Hard Skills** These are the domain and technical capabilities you need going in — or need to be able to build up fast. You don't need to be an engineer. But you do need to learn quickly, including using AI tools to close knowledge gaps on the fly. - **NIST AI RMF and EU AI Act** — working-level knowledge, not awareness. Enough to argue about the details and identify where a specific AI system fails to comply - **Construct operationalization** — demonstrated experience translating an abstract ethical principle into a bounded, testable indicator that someone else can use - **Governance documentation** — writing versioning policies, change control frameworks, and disclosure protocols that other people actually use day to day - **AI evaluation methodology** — familiarity with how AI systems are benchmarked, where measurement goes wrong, and what validity means in a scientific context - **Basic technical literacy** — able to read API documentation, understand what a model endpoint does. - **Statistical reliability concepts** — inter-rater reliabil

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