Opponents of strong transparency often invoke two main objections one being transparency will expose trade secrets and enable "gaming" of systems and two the technical complexity of modern ML models makes meaningful explanation impossible. On trade secrets, legal scholarship recognizes legitimate proprietary concerns but argues that transparency can be structured to protect core IP while still disclosing high-level information and providing researcher access under non-disclosure or controlled environments. Proposals include redacting model weights, sharing abstracted decision rules, and offering "explainability summaries" that do not reveal implementation details. The Harvard Law Review and other analyses propose legal frameworks that balance commercial protection with public interest. Harvard Law School Journals+1 Regarding complexity, the "black box" claim is sometimes overstated. Although exact replication of a model's internal states may be infeasible for lay audiences, high-level descriptions, transparency about optimization objectives, and impact metrics are both intelligible and actionable. Moreover, post-hoc tools (e.g., counterfactual explanations, high-level feature importance) and carefully designed transparency cues have been shown to help users and researchers interpret algorithmic behavior without exposing raw models. SpringerLink+1 rewrite this part in plain english in 220 words

Opponents of strong transparency often invoke two main objections one being transparency will expose trade secrets and enable \"gaming\" of systems and two the technical complexity of modern ML models makes meaningful explanation impossible. On trade secrets, legal scholarship recognizes legitimate proprietary concerns but argues that transparency can be structured to protect core IP while still disclosing high- level information and providing researcher access under non-disclosure or controlled environments. Proposals include redacting model weights, sharing abstracted decision rules, and offering \"explainability summaries\" that do not reveal implementation details. The Harvard Law Review and other analyses propose legal frameworks that balance commercial protection with public interest. Harvard Law School Journals+1 Regarding complexity, the \"black box\" claim is sometimes overstated. Although exact replication of a model's internal states may be infeasible for lay audiences, high-level descriptions, transparency about optimization objectives, and impact metrics are both intelligible and actionable. Moreover, post-hoc tools (e.g., counterfactual explanations, high-level feature importance) and carefully designed transparency cues have been shown to help users and researchers interpret algorithmic behavior without exposing raw models. SpringerLink+1 Both objections point to the need for nuance: transparency must be calibrated, staged, and governedrather than delivering either total secrecy or reckless disclosure