1 citations · 2 across the 8 of their papers we have counts for
9 papers
Eliciting Specifications: When Does Interactive Clarification Help?
Mowafak Allaham, Aditya Vempaty, Ashish Jagmohan +2
LLMs can generate Lean specifications from task descriptions, but those descriptions are often underspecified or ambiguous and may omit verifier-relevant edge cases, yielding imper…
Synthetic Sources?: Auditing Generative Search Engine Citations for Evidence of AI-Generated Sources
Mowafak Allaham, Nicholas Diakopoulos
The growing accessibility of Large Language Models via conversational interfaces capable of responding to users' questions by drawing on, synthesizing, and citing information from…
Emergence WebVoyager: Toward Consistent and Transparent Evaluation of (Web) Agents in The Wild
Deepak Akkil, Mowafak Allaham, Amal Raj +2
Reliable evaluation of AI agents operating in complex, real-world environments requires methodologies that are robust, transparent, and contextually aligned with the tasks agents a…
Who Evaluates AI's Social Impacts? Mapping Coverage and Gaps in First and Third Party Evaluations
Anka Reuel, Avijit Ghosh, Jenny Chim +32
Foundation models are increasingly central to high-stakes AI systems, and governance frameworks now depend on evaluations to assess their risks and capabilities. Although general c…
Informing AI Risk Assessment with News Media: Analyzing National and Political Variation in the Coverage of AI Risks
Mowafak Allaham, Kimon Kieslich, Nicholas Diakopoulos
Risk-based approaches to AI governance often center the technological artifact as the primary focus of risk assessments, overlooking systemic risks that emerge from the complex int…
Global Perspectives of AI Risks and Harms: Analyzing the Negative Impacts of AI Technologies as Prioritized by News Media
Mowafak Allaham, Kimon Kieslich, Nicholas Diakopoulos
Emerging AI technologies have the potential to drive economic growth and innovation but can also pose significant risks to society. To mitigate these risks, governments, companies,…
Enhancing LLMs for Governance with Human Oversight: Evaluating and Aligning LLMs on Expert Classification of Climate Misinformation for Detecting False or Misleading Claims about Climate Change
Mowafak Allaham, Ayse D. Lokmanoglu, P. Sol Hart +1
Climate misinformation is a problem that has the potential to be substantially aggravated by the development of Large Language Models (LLMs). In this study we evaluate the potentia…
Towards Leveraging News Media to Support Impact Assessment of AI Technologies
Mowafak Allaham, Kimon Kieslich, Nicholas Diakopoulos
Expert-driven frameworks for impact assessments (IAs) may inadvertently overlook the effects of AI technologies on the public's social behavior, policy, and the cultural and geogra…
Evaluating the Capabilities of LLMs for Supporting Anticipatory Impact Assessment
Mowafak Allaham, Nicholas Diakopoulos
Gaining insight into the potential negative impacts of emerging Artificial Intelligence (AI) technologies in society is a challenge for implementing anticipatory governance approac…