Second Chair
1,501 curated sources on AI and the legal profession — scholarship, industry evidence, primary legal materials and practitioner interviews, 1949–2026 — assembled by David B. Wilkins and Anthea Roberts for the AI and the Legal Profession research program of the Center on the Legal Profession at Harvard Law School.
Open Galaxy MapHow the record is organised
The pipeline read the 1,501 sources into 369 topics, grouped the topics into 46 themes, and the themes into 5 families. Every name below is the pipeline's; click a family or a theme to open it.
- 1,501 sources · 369 topics
Read the topics → — every family and theme with its claim, its figures and its topics; every topic and source opens the record.
Source-level stances written for exemplar and high-value memberships, by the voice of the source. Counts describe the collection, never prevalence in the field.
83.7% of the 9,642 stances support or build on the framing they address; 370 redirect or oppose it. Dissent surfaces in the Supports-versus-Builds-on split, in voice divergence inside a cluster, and in who is absent from it.
Five ordinal eras derived from publication year. Era sizes are very uneven, so every comparison across them reads shares, not counts.
1296 sources engage it, 443 with a written stance across 10 voices. 84.2% supportive of its framing.
Describe a question in your own words; the clusters closest to it come from a semantic search over 43,432 passages.
Every source, filterable by voice, era, type, class, evidence type and jurisdiction, with a live drawer of what the pipeline found in it.
The pipeline, this run's profile, how to read a position, and the run's scale in live figures.
The same data over a Model Context Protocol server: add it as a connector and an agent can walk the tree, split any cluster by voice or era, and audit claims against evidence.