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UID:20250415T0239Z-1744684744.2853-EO-700147-1@10.73.10.94
STATUS:CONFIRMED
DTSTAMP:20260501T023148Z
CREATED:20250414T144848Z
LAST-MODIFIED:20250414T144848Z
DTSTART;TZID=America/New_York:20250430T123000
DTEND;TZID=America/New_York:20250430T133000
SUMMARY: Radical Optionality: A Governance Strategy for Managing Uncertaint
 y
DESCRIPTION: Policymakers and companies looking to govern advanced artifici
 al intelligence systems are faced with a dilemma: uncertainty. Whether they
 ’re debating the present and future capabilities of the technology\, the na
 ture and severity of its risks\, or the benefits it might offer\, these dec
 ision makers are confronted with deep\, often unresolvable uncertainty. In 
 light of this challenge\, how should governments regulate AI? Often\, the s
 uggested […]
X-ALT-DESC;FMTTYPE=text/html: <p>Policymakers and companies looking to gove
 rn advanced artificial intelligence systems are faced with a dilemma: <a hr
 ef="https://carnegieendowment.org/research/2025/01/ai-has-been-surprising-f
 or-years?lang=en">uncertainty</a>. Whether they’re debating the present and
  future <a href="https://arxiv.org/abs/2206.07682">capabilities</a> of the 
 technology\, the nature and <a href="https://arxiv.org/pdf/2306.12001">seve
 rity</a> of its <a href="https://arxiv.org/pdf/2408.12622">risks</a>\, or t
 he <a href="https://darioamodei.com/machines-of-loving-grace">benefits</a> 
 it might offer\, these decision makers are confronted with deep\, often unr
 esolvable uncertainty.</p><p>In light of this challenge\, how should govern
 ments regulate AI?</p><p>Often\, the suggested answer falls near one of two
  poles: regulatory skepticism (“we don’t know enough\; do nothing”) or regu
 latory prescriptivism (“we know enough\; regulate today”). In this talk\, <
 a href="https://law-ai.org/team/mackenzie-arnold-2/"><strong>Mackenzie Arno
 ld</strong></a> will outline a third option: “radical optionality.” Radical
  optionality takes seriously the limits of our current knowledge\, and the 
 risk that static regulation may quickly become outdated or hinder technolog
 ical progress. But rather than resolving to do nothing\, it proposes a set 
 of actions to maintain flexibility and inform future decision making. By fo
 cusing on managing uncertainty rather than ignoring it\, radical optionalit
 y highlights the value of tools that help governments learn\, coordinate\, 
 reason\, and respond. It offers a potential path toward managing uncertaint
 y.</p><h4>Speaker</h4><p>Mackenzie is Director of US Policy at LawAI\, wher
 e he provides analysis and advice to ensure that advances in AI benefit the
  public at large. His own research focuses on administrative law\, agency d
 ecision making\, and liability. Prior to joining LawAI\, Mackenzie clerked 
 for Judge Joseph A. Greenaway\, Jr. of the Third Circuit Court of Appeals\,
  worked in public health law at a New York nonprofit\, and graduated\, cum 
 laude\, from Harvard Law School. Before law school\, Mackenzie completed a 
 Fulbright Grant in Ourense\, Spain and received his B.A. in political scien
 ce\, summa cum laude\, from Boston College\, winning the G.F. & J.W. Bemis 
 Award (for exemplary service to others) and the Donald S. Carlisle Award (a
 warded to the top graduate in political science).</p>
CATEGORIES:Speaker/Panel
LOCATION:Berkman Klein Multipurpose Room (Room 515)
GEO:0.000000;0.000000
ORGANIZER;CN="Jessica Weaver":MAILTO:jweaver@law.harvard.edu
URL;VALUE=URI:https://hls.harvard.edu/events/radical-optionality-a-governan
 ce-strategy-for-managing-uncertainty/
ATTACH;FMTTYPE=image/png:https://hls.harvard.edu/wp-content/uploads/2025/04/Screenshot-2025-04-14-at-10.44.32 AM.png
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