Artificial intelligence promises immense societal impact - both beneficent and hazardous. Many now argue the time approaches for formalized ethical codes guiding developers and engineers building transformational systems. But enacting meaningful self-governance in such a dynamic ecosystem proves complex across theory and practice.

The Case for Shared Values

Backers argue that codifying best practices around safety and responsibility mirrors precedents from medicine, law and engineering encountering similar reckoning moments. They maintain credibility flows from voluntary yet enforced pledges putting people first beyond what regulations mandate.

Grand Challenges in Enforcement

Beyond root challenges like balancing public good with innovation, effective enforcement relies on agreement from decentralized communities with conflicts between competition and controls. Leadership resisting perceived constraints must instead embrace duty of care obligations for which their rank uniquely obliges.

Envisioning New Forms of Certifications

Third party programs now also emerge certifying ethical supply chains, dataset curation, model development lifecycles and deployment monitoring - each sphere requiring tailored oversight. However, truly detached appraisal resists commercial capture given market pressures resist cooperation.

The Inclusive Path Forwards

Grappling regulatory complexities demands substantive, ongoing multidisciplinary input - particularly from marginalized groups historically denied seats at policy tables while facing technology risk exposures. Only through such inclusive cooperation can the AI field transcend the crisis emerging from looking inward rather than first and foremost to societal good.