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Articles 1 - 3 of 3
Full-Text Articles in Law
Making Sustainability Disclosure Sustainable, Jill E. Fisch
Making Sustainability Disclosure Sustainable, Jill E. Fisch
All Faculty Scholarship
Sustainability is receiving increasing attention from issuers, investors and regulators. The desire to understand issuer sustainability practices and their relationship to economic performance has resulted in a proliferation of sustainability disclosure regimes and standards. The range of approaches to disclosure, however, limit the comparability and reliability of the information disclosed. The Securities & Exchange Commission (SEC) has solicited comment on whether to require expanded sustainability disclosures in issuer’s periodic financial reporting, and investors have communicated broad-based support for such expanded disclosures, but, to date, the SEC has not required general sustainability disclosure.
This Article argues that claims about the relationship …
Clean Energy Justice: Charting An Emerging Agenda, Shelley Welton, Joel B. Eisen
Clean Energy Justice: Charting An Emerging Agenda, Shelley Welton, Joel B. Eisen
All Faculty Scholarship
The rapid transition to clean energy is fraught with potential inequities. As clean energy policies ramp up in scale and ambition, they confront challenging new questions: Who should pay for the transition? Who should live next to the industrial-scale wind and solar farms these policies promote? Will the new “green” economy be a fairer one, with more widespread opportunity, than the fossil fuel economy it is replacing? Who gets to decide what kinds of resources power our decarbonized world? In this article, we assert that it is useful to understand these challenges collectively, as part of an emerging agenda of …
Transparency And Algorithmic Governance, Cary Coglianese, David Lehr
Transparency And Algorithmic Governance, Cary Coglianese, David Lehr
All Faculty Scholarship
Machine-learning algorithms are improving and automating important functions in medicine, transportation, and business. Government officials have also started to take notice of the accuracy and speed that such algorithms provide, increasingly relying on them to aid with consequential public-sector functions, including tax administration, regulatory oversight, and benefits administration. Despite machine-learning algorithms’ superior predictive power over conventional analytic tools, algorithmic forecasts are difficult to understand and explain. Machine learning’s “black-box” nature has thus raised concern: Can algorithmic governance be squared with legal principles of governmental transparency? We analyze this question and conclude that machine-learning algorithms’ relative inscrutability does not pose a …