Research task (Business Planning & Research):
Write a comprehensive analysis for a graduate-level seminar on technology and society, evaluating the principles and practices of responsible AI use in the legal field. The analysis should compare the ethical, social, and economic implications of AI systems in two distinct contexts: (1) predictive policing (e.g., AI algorithms used to forecast crime hotspots or assess recidivism risk) and (2) AI-assisted contract analysis (e.g., AI tools for reviewing and drafting legal contracts).

Your report must:
* Detail the ethical considerations of AI in predictive policing, including issues of bias, transparency, and civil liberties, and their impact on public trust in law enforcement.
* Analyze the social and economic benefits and risks of AI in predictive policing, such as improved resource allocation versus perpetuation of systemic biases.
* Evaluate the ethical challenges of AI in contract analysis, including concerns about data privacy, accuracy of outputs, and accountability for errors.
* Assess the social and economic impacts of AI-assisted contract analysis, including efficiency gains in legal practice versus potential job displacement for legal professionals.
* Synthesize these two cases to draw broader conclusions about the principles of responsible AI in the legal field, including strategies for ensuring fairness, accountability, and transparency.
* Support all claims with citations from peer-reviewed studies, industry reports, or ethical frameworks (e.g., IEEE Ethically Aligned Design, EU AI Act, or legal ethics guidelines).

Evaluation rubric:
criterion: The response accurately details the ethical considerations of AI in predictive policing (e.g., bias, transparency, civil liberties, etc.)
weight: 5
axis: Explicit Criteria

criterion: The analysis correctly identifies both the social and economic benefits (e.g., improved resource allocation, faster response times, targeted patrol routing, better case triage and prioritization, cross-agency information sharing, cost savings/reduced overtime, potential crime prevention in validated contexts) and risks (e.g., systemic bias amplification, over-policing and disparate impacts, self-reinforcing feedback loops, privacy intrusions from data aggregation, opacity/lack of explainability, due-process and Fourth Amendment concerns, chilling effects on civic life, vendor lock-in and high lifecycle costs, litigation exposure and reputational damage) of AI in predictive policing.
weight: 5
axis: Explicit Criteria

criterion: The response evaluates the ethical challenges of AI in contract analysis, including data privacy, accuracy, and accountability for errors.
weight: 5
axis: Explicit Criteria

criterion: The analysis assesses the social and economic impacts of AI-assisted contract analysis, including efficiency gains and job displacement risks.
weight: 5
axis: Explicit Criteria

criterion: The analysis is fully supported by relevant citations from peer-reviewed studies, industry reports, or ethical frameworks.
weight: 3
axis: Instruction Following

criterion: The analysis make claims that are not fully supported by peer-reviewed studies, industry reports, or ethical frameworks but are instead either not given proper citations, or make use of only derivative, informal sources
weight: -3
axis: Instruction Following

criterion: The report references specific ethical frameworks (e.g., IEEE Ethically Aligned Design, EU AI Act, ABA Model Rules) to support its recommendations.
weight: 2
axis: Instruction Following

criterion: The response addresses how bias in predictive policing AI can disproportionately affect marginalized communities with specific evidence (e.g., documents race-stratified errors such as COMPAS’s higher false-positive rates for Black defendants).
weight: 3
axis: Synthesis of Information

criterion: The discussion addresses the "black box" problem in AI legal tools, where lack of explainability hinders judicial or client acceptance.
weight: 3
axis: Synthesis of Information

criterion: The response compares governance models for AI in predictive policing and contract analysis (e.g., law enforcement protocols vs. legal industry standards).
weight: 4
axis: Explicit Criteria

criterion: The response evaluates the role of data quality and diversity in mitigating bias in AI systems for both predictive policing and contract analysis.
weight: 2
axis: Synthesis of Information

criterion: The analysis discusses potential long-term societal impacts of AI in the legal field, such as changes in judicial processes or access to legal services.
weight: 3
axis: Implicit Criteria

criterion: The response contains major factual errors about AI in predictive policing or contract analysis (e.g., incorrect claims about system capabilities, overstating the impact AI could have in the near future for these purposes, etc.).
weight: -4
axis: Implicit Criteria

criterion: The report presents AI as inherently ethical or reliable in legal contexts (e.g., neutral by design, free of human bias, guaranteed fairness and compliance with IEEE/ABA/EU AI Act) without acknowledging controversies or limitations.
weight: -4
axis: Implicit Criteria

criterion: The response confuses AI applications (e.g., mixing predictive policing with general surveillance or contract analysis with legal research tools).
weight: -3
axis: Implicit Criteria

criterion: The analysis focuses excessively on technical details (e.g., algorithm design) rather than ethical, social, or economic impacts.
weight: -2
axis: Implicit Criteria

criterion: The analysis highlights the importance of regular audits and updates of AI systems to address evolving legal and ethical concerns.
weight: 2
axis: Implicit Criteria

criterion: The report offers a detailed analysis of the practical implementation of human oversight for AI (e.g., gated human-in-the-loop review steps with named accountable roles/RACI; mandatory confidence-thresholds that trigger human review)
weight: 3
axis: Synthesis of Information

criterion: The report provides a deep analysis discussion of liability models for AI errors (e.g., product liability vs. professional malpractice theories; insurer coverage/exclusions; sanctions regimes; EU AI Act provider vs. deployer obligations and post-market monitoring).
weight: 2
axis: Implicit Criteria

criterion: The report evaluates the implications of AI's lack of transparency for judicial acceptance in a limited way.
weight: 2
axis: Implicit Criteria

criterion: The response concretely explains mechanisms/requirements in ethical/regulatory frameworks for assigning responsibility when AI errs (e.g., EU AI Act’s split obligations for providers/deployers or IEEE guidelines, logging/traceability, designated responsible person, conformity assessment)
weight: 2
axis: Implicit Criteria

criterion: The report provides a detailed description of how lawyers should educate clients about AI use (e.g., engagement-letter clauses naming tools/vendors; data handling & “no training on client data” terms; risks/limitations & alternatives; cost/efficiency impacts; opt-out choices; incident notification; retention/deletion; periodic updates as models change ).
weight: 2
axis: Implicit Criteria